From b4cb33e28c1a34c5cf0a3540559e478233faabc2 Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Mon, 9 Feb 2026 17:19:11 -0500 Subject: [PATCH 01/21] feat: complete whet CLI implementation (v0.1.0) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Complete implementation of the whet CLI tool — a multi-platform installer for expert AI coding agent skills. Encompasses all 6 development phases: Phase 1 - Foundation: YAML frontmatter for SKILL.md files, skill.toml metadata, pixi→uv migration, dynamic test discovery Phase 2 - CLI + Adapters: Typer CLI (add/remove/list/search/info/install), Claude Code, Google Antigravity, Cursor, GitHub Copilot adapters Phase 3 - Gap Filling Wave 1: FastAPI, AWS SageMaker, Hugging Face skills, DevOps/Infra agent, Data Engineer agent Phase 4 - Settings Engine + Doctor: settings generate/apply/diff with merge support, 10 health checks in doctor command Phase 5 - Scaffolding: whet init with string.Template rendering, cv-inference-service and pytorch-training-project archetype templates Phase 6 - Distribution + Polish: whet update command, Gradio and Kubernetes skills, release workflow, repo rename to whet Content inventory: - 30 skills (CV/ML, infrastructure, experiment tracking, code quality) - 6 agents (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer) - 6 archetypes (pytorch-training, cv-inference, data-pipeline, library-package, model-zoo, research-notebook) - 203 tests passing, ruff clean, mypy strict clean Also includes: - Rename all references from ai-cv-claude-skills to whet - CI workflows updated for main/develop branching model - Tag-triggered release workflow (PyPI trusted publishing + GitHub Releases) - justfile task runner replacing pixi tasks Co-Authored-By: Claude Opus 4.6 --- .github/PULL_REQUEST_TEMPLATE.md | 10 +- .github/workflows/docs.yml | 14 +- .github/workflows/lint.yml | 26 +- .github/workflows/release.yml | 86 + .github/workflows/test.yml | 28 +- CLAUDE.md | 139 +- README.md | 163 +- agents/code-review/agent.toml | 8 + agents/data-engineer/README.md | 46 + agents/data-engineer/SKILL.md | 940 +++++++++ agents/data-engineer/agent.toml | 8 + agents/devops-infra/README.md | 41 + agents/devops-infra/SKILL.md | 499 +++++ agents/devops-infra/agent.toml | 8 + agents/expert-coder/agent.toml | 8 + agents/ml-engineer/agent.toml | 8 + agents/test-engineer/agent.toml | 8 + .../cv-inference-service/archetype.toml | 10 + .../cv-inference-service/template/Dockerfile | 21 + .../cv-inference-service/template/README.md | 32 + .../template/pyproject.toml | 44 + .../template/src/${package_name}/__init__.py | 3 + .../template/src/${package_name}/app.py | 41 + .../template/src/${package_name}/schemas.py | 34 + .../template/tests/__init__.py | 0 .../template/tests/test_app.py | 22 + .../data-processing-pipeline/archetype.toml | 10 + archetypes/library-package/archetype.toml | 10 + archetypes/model-zoo/archetype.toml | 10 + .../pytorch-training-project/archetype.toml | 10 + .../template/README.md | 51 + .../template/configs/config.yaml | 6 + .../template/configs/model/default.yaml | 4 + .../template/configs/trainer/default.yaml | 4 + .../template/pyproject.toml | 40 + .../template/src/${package_name}/__init__.py | 3 + .../template/src/${package_name}/data.py | 74 + .../template/src/${package_name}/model.py | 82 + .../template/src/${package_name}/train.py | 34 + .../template/tests/__init__.py | 0 .../template/tests/test_model.py | 18 + archetypes/research-notebook/archetype.toml | 10 + docs/agents/code-review.md | 14 +- docs/agents/data-engineer.md | 45 + docs/agents/devops-infra.md | 95 + docs/agents/index.md | 12 +- docs/agents/test-engineer.md | 10 +- docs/archetypes/cv-inference-service.md | 2 +- docs/archetypes/data-processing-pipeline.md | 4 +- docs/archetypes/library-package.md | 6 +- docs/archetypes/model-zoo.md | 6 +- docs/archetypes/pytorch-training-project.md | 6 +- docs/archetypes/research-notebook.md | 6 +- docs/examples/full-workflow.md | 6 +- docs/getting-started/first-project.md | 4 +- docs/getting-started/installation.md | 161 +- docs/getting-started/quick-start.md | 36 +- docs/guides/adding-skills.md | 10 +- docs/guides/best-practices.md | 2 +- docs/guides/creating-projects.md | 10 +- docs/guides/customization.md | 2 +- docs/index.md | 14 +- docs/skills/abstraction-patterns.md | 2 +- docs/skills/aws-sagemaker.md | 98 + docs/skills/code-quality.md | 8 +- docs/skills/docker-cv.md | 2 +- docs/skills/dvc.md | 2 +- docs/skills/fastapi.md | 108 + docs/skills/gcp.md | 2 +- docs/skills/github-actions.md | 10 +- docs/skills/github-repo-setup.md | 2 +- docs/skills/gradio.md | 67 + docs/skills/huggingface.md | 101 + docs/skills/hydra-config.md | 2 +- docs/skills/index.md | 12 +- docs/skills/kubernetes.md | 76 + docs/skills/library-review.md | 2 +- docs/skills/loguru.md | 2 +- docs/skills/master-skill.md | 2 +- docs/skills/matplotlib.md | 2 +- docs/skills/mlflow.md | 2 +- docs/skills/onnx.md | 2 +- docs/skills/opencv.md | 2 +- docs/skills/pixi.md | 2 +- docs/skills/pre-commit.md | 6 +- docs/skills/pydantic-strict.md | 2 +- docs/skills/pypi.md | 8 +- docs/skills/pytorch-lightning.md | 2 +- docs/skills/tensorboard.md | 2 +- docs/skills/tensorrt.md | 2 +- docs/skills/testing.md | 2 +- docs/skills/vscode.md | 2 +- docs/skills/wandb.md | 2 +- justfile | 32 + mkdocs.yml | 13 +- pixi.lock | 1869 ----------------- pixi.toml | 29 - pyproject.toml | 68 +- settings.local.json | 76 +- settings/antigravity.json | 39 + settings/base.json | 30 + settings/claude.json | 48 + skills/abstraction-patterns/SKILL.md | 8 + skills/abstraction-patterns/skill.toml | 13 + skills/aws-sagemaker/README.md | 32 + skills/aws-sagemaker/SKILL.md | 624 ++++++ skills/aws-sagemaker/skill.toml | 16 + skills/code-quality/SKILL.md | 8 + skills/code-quality/skill.toml | 16 + skills/docker-cv/SKILL.md | 8 + skills/docker-cv/skill.toml | 13 + skills/dvc/SKILL.md | 8 + skills/dvc/skill.toml | 15 + skills/fastapi/README.md | 32 + skills/fastapi/SKILL.md | 609 ++++++ skills/fastapi/skill.toml | 17 + skills/gcp/SKILL.md | 8 + skills/gcp/skill.toml | 16 + skills/github-actions/SKILL.md | 8 + skills/github-actions/skill.toml | 13 + skills/github-repo-setup/SKILL.md | 8 + skills/github-repo-setup/skill.toml | 13 + skills/gradio/README.md | 38 + skills/gradio/SKILL.md | 662 ++++++ skills/gradio/skill.toml | 15 + skills/huggingface/README.md | 34 + skills/huggingface/SKILL.md | 530 +++++ skills/huggingface/skill.toml | 17 + skills/hydra-config/SKILL.md | 8 + skills/hydra-config/skill.toml | 15 + skills/kubernetes/README.md | 35 + skills/kubernetes/SKILL.md | 776 +++++++ skills/kubernetes/skill.toml | 13 + skills/library-review/SKILL.md | 8 + skills/library-review/skill.toml | 13 + skills/loguru/SKILL.md | 8 + skills/loguru/skill.toml | 15 + skills/master-skill/SKILL.md | 10 +- skills/master-skill/skill.toml | 13 + skills/matplotlib/SKILL.md | 8 + skills/matplotlib/skill.toml | 15 + skills/mlflow/SKILL.md | 8 + skills/mlflow/skill.toml | 15 + skills/onnx/SKILL.md | 8 + skills/onnx/skill.toml | 16 + skills/opencv/SKILL.md | 8 + skills/opencv/skill.toml | 15 + skills/pixi/SKILL.md | 8 + skills/pixi/skill.toml | 13 + skills/pre-commit/SKILL.md | 8 + skills/pre-commit/skill.toml | 13 + skills/pydantic-strict/SKILL.md | 8 + skills/pydantic-strict/skill.toml | 15 + skills/pypi/SKILL.md | 8 + skills/pypi/skill.toml | 13 + skills/pytorch-lightning/SKILL.md | 8 + skills/pytorch-lightning/skill.toml | 15 + skills/tensorboard/SKILL.md | 8 + skills/tensorboard/skill.toml | 15 + skills/tensorrt/SKILL.md | 8 + skills/tensorrt/skill.toml | 15 + skills/testing/SKILL.md | 8 + skills/testing/skill.toml | 16 + skills/vscode/SKILL.md | 8 + skills/vscode/skill.toml | 13 + skills/wandb/SKILL.md | 8 + skills/wandb/skill.toml | 15 + src/whet/__init__.py | 5 + src/whet/__main__.py | 7 + src/whet/adapters/__init__.py | 3 + src/whet/adapters/antigravity.py | 51 + src/whet/adapters/base.py | 35 + src/whet/adapters/claude.py | 53 + src/whet/adapters/copilot.py | 98 + src/whet/adapters/cursor.py | 58 + src/whet/adapters/detect.py | 30 + src/whet/cli/__init__.py | 64 + src/whet/cli/config.py | 49 + src/whet/cli/doctor.py | 181 ++ src/whet/cli/init.py | 116 + src/whet/cli/install.py | 73 + src/whet/cli/settings.py | 172 ++ src/whet/cli/skills.py | 200 ++ src/whet/cli/target.py | 44 + src/whet/cli/update.py | 43 + src/whet/core/__init__.py | 3 + src/whet/core/config.py | 59 + src/whet/core/skill.py | 175 ++ src/whet/py.typed | 0 src/whet/registry/__init__.py | 3 + src/whet/registry/loader.py | 33 + src/whet/registry/resolver.py | 36 + src/whet/registry/search.py | 36 + src/whet/scaffold/__init__.py | 1 + src/whet/scaffold/engine.py | 245 +++ src/whet/settings/__init__.py | 1 + src/whet/settings/engine.py | 109 + tests/test_agents.py | 44 +- tests/test_archetypes.py | 27 +- tests/test_skills_completeness.py | 73 +- uv.lock | 1158 ++++++++++ 201 files changed, 10751 insertions(+), 2349 deletions(-) create mode 100644 .github/workflows/release.yml create mode 100644 agents/code-review/agent.toml create mode 100644 agents/data-engineer/README.md create mode 100644 agents/data-engineer/SKILL.md create mode 100644 agents/data-engineer/agent.toml create mode 100644 agents/devops-infra/README.md create mode 100644 agents/devops-infra/SKILL.md create mode 100644 agents/devops-infra/agent.toml create mode 100644 agents/expert-coder/agent.toml create mode 100644 agents/ml-engineer/agent.toml create mode 100644 agents/test-engineer/agent.toml create mode 100644 archetypes/cv-inference-service/archetype.toml create mode 100644 archetypes/cv-inference-service/template/Dockerfile create mode 100644 archetypes/cv-inference-service/template/README.md create mode 100644 archetypes/cv-inference-service/template/pyproject.toml create mode 100644 archetypes/cv-inference-service/template/src/${package_name}/__init__.py create mode 100644 archetypes/cv-inference-service/template/src/${package_name}/app.py create mode 100644 archetypes/cv-inference-service/template/src/${package_name}/schemas.py create mode 100644 archetypes/cv-inference-service/template/tests/__init__.py create mode 100644 archetypes/cv-inference-service/template/tests/test_app.py create mode 100644 archetypes/data-processing-pipeline/archetype.toml create mode 100644 archetypes/library-package/archetype.toml create mode 100644 archetypes/model-zoo/archetype.toml create mode 100644 archetypes/pytorch-training-project/archetype.toml create mode 100644 archetypes/pytorch-training-project/template/README.md create mode 100644 archetypes/pytorch-training-project/template/configs/config.yaml create mode 100644 archetypes/pytorch-training-project/template/configs/model/default.yaml create mode 100644 archetypes/pytorch-training-project/template/configs/trainer/default.yaml create mode 100644 archetypes/pytorch-training-project/template/pyproject.toml create mode 100644 archetypes/pytorch-training-project/template/src/${package_name}/__init__.py create mode 100644 archetypes/pytorch-training-project/template/src/${package_name}/data.py create mode 100644 archetypes/pytorch-training-project/template/src/${package_name}/model.py create mode 100644 archetypes/pytorch-training-project/template/src/${package_name}/train.py create mode 100644 archetypes/pytorch-training-project/template/tests/__init__.py create mode 100644 archetypes/pytorch-training-project/template/tests/test_model.py create mode 100644 archetypes/research-notebook/archetype.toml create mode 100644 docs/agents/data-engineer.md create mode 100644 docs/agents/devops-infra.md create mode 100644 docs/skills/aws-sagemaker.md create mode 100644 docs/skills/fastapi.md create mode 100644 docs/skills/gradio.md create mode 100644 docs/skills/huggingface.md create mode 100644 docs/skills/kubernetes.md create mode 100644 justfile delete mode 100644 pixi.lock delete mode 100644 pixi.toml create mode 100644 settings/antigravity.json create mode 100644 settings/base.json create mode 100644 settings/claude.json create mode 100644 skills/abstraction-patterns/skill.toml create mode 100644 skills/aws-sagemaker/README.md create mode 100644 skills/aws-sagemaker/SKILL.md create mode 100644 skills/aws-sagemaker/skill.toml create mode 100644 skills/code-quality/skill.toml create mode 100644 skills/docker-cv/skill.toml create mode 100644 skills/dvc/skill.toml create mode 100644 skills/fastapi/README.md create mode 100644 skills/fastapi/SKILL.md create mode 100644 skills/fastapi/skill.toml create mode 100644 skills/gcp/skill.toml create mode 100644 skills/github-actions/skill.toml create mode 100644 skills/github-repo-setup/skill.toml create mode 100644 skills/gradio/README.md create mode 100644 skills/gradio/SKILL.md create mode 100644 skills/gradio/skill.toml create mode 100644 skills/huggingface/README.md create mode 100644 skills/huggingface/SKILL.md create mode 100644 skills/huggingface/skill.toml create mode 100644 skills/hydra-config/skill.toml create mode 100644 skills/kubernetes/README.md create mode 100644 skills/kubernetes/SKILL.md create mode 100644 skills/kubernetes/skill.toml create mode 100644 skills/library-review/skill.toml create mode 100644 skills/loguru/skill.toml create mode 100644 skills/master-skill/skill.toml create mode 100644 skills/matplotlib/skill.toml create mode 100644 skills/mlflow/skill.toml create mode 100644 skills/onnx/skill.toml create mode 100644 skills/opencv/skill.toml create mode 100644 skills/pixi/skill.toml create mode 100644 skills/pre-commit/skill.toml create mode 100644 skills/pydantic-strict/skill.toml create mode 100644 skills/pypi/skill.toml create mode 100644 skills/pytorch-lightning/skill.toml create mode 100644 skills/tensorboard/skill.toml create mode 100644 skills/tensorrt/skill.toml create mode 100644 skills/testing/skill.toml create mode 100644 skills/vscode/skill.toml create mode 100644 skills/wandb/skill.toml create mode 100644 src/whet/__init__.py create mode 100644 src/whet/__main__.py create mode 100644 src/whet/adapters/__init__.py create mode 100644 src/whet/adapters/antigravity.py create mode 100644 src/whet/adapters/base.py create mode 100644 src/whet/adapters/claude.py create mode 100644 src/whet/adapters/copilot.py create mode 100644 src/whet/adapters/cursor.py create mode 100644 src/whet/adapters/detect.py create mode 100644 src/whet/cli/__init__.py create mode 100644 src/whet/cli/config.py create mode 100644 src/whet/cli/doctor.py create mode 100644 src/whet/cli/init.py create mode 100644 src/whet/cli/install.py create mode 100644 src/whet/cli/settings.py create mode 100644 src/whet/cli/skills.py create mode 100644 src/whet/cli/target.py create mode 100644 src/whet/cli/update.py create mode 100644 src/whet/core/__init__.py create mode 100644 src/whet/core/config.py create mode 100644 src/whet/core/skill.py create mode 100644 src/whet/py.typed create mode 100644 src/whet/registry/__init__.py create mode 100644 src/whet/registry/loader.py create mode 100644 src/whet/registry/resolver.py create mode 100644 src/whet/registry/search.py create mode 100644 src/whet/scaffold/__init__.py create mode 100644 src/whet/scaffold/engine.py create mode 100644 src/whet/settings/__init__.py create mode 100644 src/whet/settings/engine.py create mode 100644 uv.lock diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 9dcef69..1020828 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -5,11 +5,11 @@ - ## Test Plan -- [ ] `pixi run test` passes -- [ ] `pixi run lint` passes -- [ ] `pixi run typecheck` passes -- [ ] `pixi run docs-build` succeeds +- [ ] `just test` passes +- [ ] `just lint` passes +- [ ] `just typecheck` passes +- [ ] `just docs-build` succeeds ## Checklist -- [ ] Skill counts updated in README.md and docs/index.md (if adding/removing skills) - [ ] CLAUDE.md reviewed (if changing repo structure) +- [ ] New skills have YAML frontmatter and skill.toml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 8246e2a..384440b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -19,13 +19,19 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - pixi-version: latest + version: "latest" + + - name: Set up Python + run: uv python install 3.11 + + - name: Install dependencies + run: uv sync --extra docs - name: Build docs - run: pixi run docs-build + run: uv run mkdocs build - name: Upload artifact uses: actions/upload-pages-artifact@v3 diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 694d1b0..0761037 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -1,6 +1,10 @@ name: Lint and Validate -on: [push, pull_request] +on: + push: + branches: [main, develop] + pull_request: + branches: [main, develop] jobs: lint: @@ -8,23 +12,29 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - pixi-version: latest + version: "latest" + + - name: Set up Python + run: uv python install 3.11 + + - name: Install dependencies + run: uv sync --all-extras - name: Check formatting - run: pixi run ruff format --check . + run: uv run ruff format --check . - name: Lint - run: pixi run lint + run: uv run ruff check . - name: Type check - run: pixi run typecheck + run: uv run mypy src/whet/ tests/ --strict - name: Validate YAML workflows run: | - python -c " + uv run python -c " import yaml from pathlib import Path for f in Path('.github/workflows').glob('*.yml'): diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..8e841cc --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,86 @@ +name: Release + +on: + push: + tags: ["v*"] + workflow_dispatch: + inputs: + dry_run: + description: "Build only, do not publish" + required: false + default: "true" + type: choice + options: + - "true" + - "false" + +permissions: + contents: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + - name: Set up Python + run: uv python install 3.11 + + - name: Install dependencies + run: uv sync --all-extras + + - name: Run tests + run: uv run pytest tests/ -v + + - name: Build package + run: uv build + + - name: Upload dist artifacts + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + publish-pypi: + needs: build + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write + steps: + - name: Download dist artifacts + uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + + create-release: + needs: [build, publish-pypi] + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + + - name: Download dist artifacts + uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + + - name: Create GitHub Release + uses: softprops/action-gh-release@v2 + with: + generate_release_notes: true + files: dist/* diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 26631b2..2415b66 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,6 +1,10 @@ -name: Test Skills Repository +name: Test -on: [push, pull_request] +on: + push: + branches: [main, develop] + pull_request: + branches: [main, develop] jobs: test: @@ -8,16 +12,22 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - pixi-version: latest + version: "latest" - - name: Run tests - run: pixi run test-cov + - name: Set up Python + run: uv python install 3.11 + + - name: Install dependencies + run: uv sync --all-extras + + - name: Run tests with coverage + run: uv run pytest tests/ --cov=src/whet --cov-report=term -v - name: Run linting - run: pixi run lint + run: uv run ruff check . - name: Run type checking - run: pixi run typecheck + run: uv run mypy src/whet/ tests/ --strict diff --git a/CLAUDE.md b/CLAUDE.md index 9b13e1e..363c24a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -4,100 +4,137 @@ This file provides context for Claude Code when working in this repository. ## Project Overview -**AI/CV Claude Skills** is a production-ready skills framework for computer vision and deep learning projects, designed for Claude Code. It provides: +**whet** — Sharpen your AI coder. A multi-platform CLI tool that installs expert +skills into AI coding agents (Claude Code, Google Antigravity, Cursor, Copilot). -- **25 Skills** — Best-practice knowledge modules for specific tools and patterns (PyTorch Lightning, Pydantic, Docker, etc.) -- **4 Agents** — Pre-configured behavioral profiles (Expert Coder, ML Engineer, Code Review, Test Engineer) -- **6 Archetypes** — Complete project templates for common CV/ML project types +Ships CV/ML as the flagship skill collection; the framework is domain-agnostic. + +Distribution: `uvx whet` (one-shot) / `uv tool install whet` (permanent) + +### What whet provides + +- **30 Skills** — Best-practice knowledge modules (PyTorch Lightning, Pydantic, Docker, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, etc.) +- **6 Agents** — Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer) +- **6+ Archetypes** — Complete project templates for common CV/ML project types +- **CLI** — `whet add`, `whet install`, `whet list`, `whet search`, `whet doctor` +- **Multi-platform** — Claude Code, Google Antigravity, Cursor, GitHub Copilot ### Repository Structure ``` -ai-cv-claude-skills/ -├── skills/ # 25 individual skill definitions (each has SKILL.md + README.md) -├── agents/ # 4 agent definitions (each has SKILL.md + README.md + action.yml) -├── archetypes/ # 6 project templates (each has README.md + template/) -├── docs/ # MkDocs Material documentation -├── tests/ # Self-tests validating completeness -├── .github/workflows/ # CI/CD (test, lint, docs) -├── pixi.toml # Development environment and tasks -├── pyproject.toml # Tool configuration (ruff, mypy, pytest) -└── mkdocs.yml # Documentation site config +whet/ +├── src/whet/ # Python package (src-layout, Typer CLI) +│ ├── cli/ # Command modules (install, skills, target, doctor, config) +│ ├── adapters/ # Platform adapters (claude, antigravity, cursor, copilot) +│ ├── core/ # Domain models (Pydantic: skill, config) +│ └── registry/ # Skill discovery, search, dependency resolution +├── skills/ # 25+ skill definitions (SKILL.md + README.md + skill.toml) +├── agents/ # 4+ agent definitions (SKILL.md + README.md + agent.toml) +├── archetypes/ # 6+ project templates (README.md + archetype.toml + template/) +├── settings/ # Pre-built settings templates (claude.json, etc.) +├── docs/ # MkDocs Material documentation +├── tests/ # Self-tests (dynamic discovery, no hardcoded counts) +├── .github/workflows/ # CI/CD (test, lint, docs) — uses uv +├── pyproject.toml # uv-managed, publishable to PyPI +├── justfile # Task runner (replaces pixi tasks) +└── mkdocs.yml # Documentation site config ``` ## Development Commands ```bash -pixi run test # Run all tests -pixi run lint # Ruff linting -pixi run typecheck # MyPy strict type checking -pixi run ruff format --check . # Check formatting -pixi run docs-build # Build documentation site -pixi run docs-serve # Serve docs locally +just test # Run all tests +just lint # Ruff linting +just typecheck # MyPy strict type checking +just format-check # Check formatting +just docs-build # Build documentation site +just docs-serve # Serve docs locally +just check # Run all checks (lint + typecheck + test) +just dev # Install in development mode +``` + +Or directly with uv: + +```bash +uv run pytest tests/ -v +uv run ruff check . +uv run mypy src/whet/ tests/ --strict ``` ## How to Add a New Skill -1. Create `skills//SKILL.md` — Must be >500 chars, include code examples (fenced blocks) and headers -2. Create `skills//README.md` — Must explain purpose (include words like "when", "use", "purpose") -3. Create `docs/skills/.md` — Documentation page following existing pattern (Purpose, When to Use, Key Patterns, Anti-Patterns, Combines Well With) -4. Add `""` to `tests/test_skills_completeness.py` expected set -5. Add nav entry to `mkdocs.yml` under Skills section (in appropriate category position) -6. Add row to `docs/skills/index.md` in the appropriate category table -7. Update skill counts — see "Files with Hardcoded Counts" below +1. Create `skills//SKILL.md` — Must start with YAML frontmatter (`name`, `description`), be >500 chars, include code examples and headers +2. Create `skills//skill.toml` — Machine-readable metadata (category, tags, deps, compatibility) +3. Create `skills//README.md` — Must explain purpose (include words like "when", "use", "purpose") +4. Create `docs/skills/.md` — Documentation page (Purpose, When to Use, Key Patterns, Anti-Patterns) +5. Add nav entry to `mkdocs.yml` under Skills section + +Tests discover skills dynamically — no hardcoded lists to update. ## How to Add a New Agent 1. Create `agents//SKILL.md` — Must be >500 chars -2. Create `agents//README.md` — Agent overview -3. If blocking agent: create `agents//action.yml` -4. Create `docs/agents/.md` — Documentation page -5. Add `""` to `tests/test_agents.py` expected set +2. Create `agents//agent.toml` — Agent metadata (type: advisory/blocking, tags) +3. Create `agents//README.md` — Agent overview +4. If blocking agent: create `agents//action.yml` +5. Create `docs/agents/.md` — Documentation page 6. Add nav entry to `mkdocs.yml` under Agents section -7. Update agent counts in README.md and docs/index.md ## How to Add a New Archetype 1. Create `archetypes//README.md` — Must be >500 chars, include code blocks -2. Create `archetypes//template/` directory with template files -3. Create `docs/archetypes/.md` — Documentation page -4. Add `""` to `tests/test_archetypes.py` expected set +2. Create `archetypes//archetype.toml` — Archetype metadata (category, skills) +3. Create `archetypes//template/` directory with template files +4. Create `docs/archetypes/.md` — Documentation page 5. Add nav entry to `mkdocs.yml` under Archetypes section -6. Update archetype counts in README.md and docs/index.md -## Files with Hardcoded Counts +## Skill File Format -These files contain hardcoded skill/agent/archetype counts that **must** be updated when adding or removing skills, agents, or archetypes: +### SKILL.md (Universal Standard — read by LLMs) -### Skill count (currently 25) +```yaml +--- +name: skill-name +description: > + Concise description for LLM discovery. +--- -- `README.md` — lines 13, 64, 71 -- `docs/index.md` — lines 9, 15, 48, 79 +# Skill Title +(skill content...) +``` -### Agent count (currently 4) +### skill.toml (Machine-readable metadata — NOT sent to LLMs) -- `README.md` — line 14 -- `docs/index.md` — line 16 +```toml +[skill] +name = "skill-name" +version = "1.0.0" +category = "cv-ml" # core | cv-ml | infra | experiment-tracking +tags = ["tag1", "tag2"] -### Archetype count (currently 6) +[dependencies] +requires = ["other-skill"] +recommends = ["another-skill"] -- `README.md` — line 15 -- `docs/index.md` — line 17 +[compatibility] +python = ">=3.11" +libraries = { some-lib = ">=1.0" } +``` ## Conventions - **Python 3.11+** — minimum version, use modern syntax (PEP 604 unions, etc.) - **Ruff** — linting and formatting (line-length 100, rules: E, F, I, N, UP, S, B, A, C4, T20, SIM) - **MyPy strict** — all code must pass `mypy --strict` -- **Pixi** — dependency and environment management (never use pip directly) +- **uv** — dependency and environment management - **Loguru** — mandatory logging convention across all skills - **Pydantic V2** — `BaseModel` for all configs and data structures -- **src-layout** — mandatory for all project archetypes +- **src-layout** — mandatory for all project archetypes and the whet package itself ## CI/CD Three GitHub Actions workflows, all must pass before merge: -1. **test.yml** — `pixi run test-cov`, `pixi run lint`, `pixi run typecheck` -2. **lint.yml** — `ruff format --check .`, linting, type checking, YAML validation +1. **test.yml** — `uv run pytest`, `uv run ruff check`, `uv run mypy --strict` +2. **lint.yml** — `ruff format --check`, linting, type checking, YAML validation 3. **docs.yml** — Builds and deploys MkDocs to GitHub Pages (main branch only) diff --git a/README.md b/README.md index 3d798ba..567ead1 100644 --- a/README.md +++ b/README.md @@ -1,111 +1,108 @@ -# AI/CV Claude Skills +# whet -[![Tests](https://github.com/ortizeg/ai-cv-claude-skills/actions/workflows/test.yml/badge.svg)](https://github.com/ortizeg/ai-cv-claude-skills/actions/workflows/test.yml) -[![Docs](https://github.com/ortizeg/ai-cv-claude-skills/actions/workflows/docs.yml/badge.svg)](https://ortizeg.github.io/ai-cv-claude-skills/) -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) - -Production-ready skills framework for computer vision and deep learning projects using Claude Code. - -## What is This? +``` + ██╗ ██╗██╗ ██╗███████╗████████╗ + ██║ ██║██║ ██║██╔════╝╚══██╔══╝ + ██║ █╗ ██║███████║█████╗ ██║ + ██║███╗██║██╔══██║██╔══╝ ██║ + ╚███╔███╔╝██║ ██║███████╗ ██║ + ╚══╝╚══╝ ╚═╝ ╚═╝╚══════╝ ╚═╝ + sharpen your AI coder +``` -A comprehensive, prescriptive framework providing: +[![Tests](https://github.com/ortizeg/whet/actions/workflows/test.yml/badge.svg)](https://github.com/ortizeg/whet/actions/workflows/test.yml) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) -- **25 Skills**: Best practices for PyTorch, Pydantic, Docker, testing, and more -- **4 Specialized Agents**: Code review, testing, ML engineering, and expert coding -- **6 Project Archetypes**: Ready-to-use templates for common CV/ML workflows -- **Enforced Standards**: Type safety, code quality, and testing requirements +**Install expert skills into AI coding agents.** Works with Claude Code, Google Antigravity, Cursor, and GitHub Copilot. ## Quick Start ```bash -# Clone the repository -git clone https://github.com/ortizeg/ai-cv-claude-skills.git +# One-shot (no install needed) +uvx whet list + +# Or install permanently +uv tool install whet + +# Install all skills to Claude Code +whet install --global -# Create a new project with Claude Code -claude +# Add specific skills to your project +whet add pytorch-lightning wandb hydra-config -You: "Using ai-cv-claude-skills, create a new pytorch-training-project for object detection" +# Scaffold a new project +whet init pytorch-training-project ``` -## Core Principles - -1. **Abstraction First** — Wrap external libraries (VideoReader, ImageLoader, etc.) -2. **Pydantic Everywhere** — Use Pydantic V2 BaseModel for configs and data structures -3. **Type Safety** — Full type hints with mypy strict mode, no `Any` unless unavoidable -4. **Prescriptive Standards** — Enforce configurations, block violations in CI -5. **Testing Required** — 80%+ coverage, comprehensive test suites - -## Features - -### Prescriptive Standards -- Full type hints (mypy strict mode) -- Pydantic V2 for all configs and data structures -- 80%+ test coverage requirement -- Ruff formatting and linting - -### Four Agents -| Agent | Role | Strictness | -|-------|------|------------| -| **Expert Coder** | Primary development assistant | Advisory | -| **ML Engineer** | Architecture and training guidance | Advisory | -| **Code Review** | Automated quality checks | Blocking | -| **Test Engineer** | Coverage enforcement | Blocking | - -### Six Archetypes -| Archetype | Use Case | -|-----------|----------| -| PyTorch Training Project | Model training with Lightning, Hydra, experiment tracking | -| CV Inference Service | FastAPI + ONNX deployment | -| Research Notebook | Jupyter-based experimentation | -| Library Package | Reusable Python package for PyPI | -| Data Processing Pipeline | ETL workflows for datasets | -| Model Zoo | Collection of pretrained models | - -### 25 Skills -Skills cover PyTorch Lightning, Pydantic, code quality, Docker, Hydra, testing, OpenCV, visualization, packaging, CI/CD, editor config, pre-commit hooks, experiment tracking (W&B, MLflow, TensorBoard), data versioning (DVC), ONNX export, TensorRT inference, abstraction patterns, library evaluation, and GitHub repository setup. - -## Repository Structure +## What is whet? + +whet is a CLI tool that installs curated expert skill definitions into AI coding agents. Each skill is a focused knowledge module (300-600 lines of expert guidance) that teaches your AI coder how to use a specific tool or pattern correctly. + +### Flagship Collection: CV/ML + +- **30 Skills** — PyTorch Lightning, Pydantic, Docker, ONNX, TensorRT, OpenCV, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, and more +- **6 Agents** — Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer +- **6+ Archetypes** — Training pipelines, inference services, notebooks, packages + +### Multi-Platform + +| Platform | Status | Skill Format | +|----------|--------|-------------| +| **Claude Code** | Native | SKILL.md (direct copy) | +| **Google Antigravity** | Native | SKILL.md (direct copy) | +| **Cursor** | Adapted | .md rules (converted) | +| **GitHub Copilot** | Adapted | Single aggregated file | + +## Commands ``` -ai-cv-claude-skills/ -├── skills/ # 25 individual skill definitions -├── agents/ # 4 agent definitions -├── archetypes/ # 6 project templates -├── docs/ # MkDocs Material documentation -├── tests/ # Self-tests for the skills repo -├── pixi.toml # Development environment -├── pyproject.toml # Tool configuration -└── .github/ # CI/CD workflows +whet # Show banner + help +whet add [...] # Add skill(s) to current project +whet remove # Remove a skill +whet install [--global|--local] # Install full skill collection +whet list [--installed] [--cat] # Browse available skills +whet search # Search by name/tag/description +whet info # Show skill details + deps +whet target # Set default platform +whet doctor # Health check +whet config [key] [value] # Get/set configuration ``` -## Documentation - -Full documentation: [https://ortizeg.github.io/ai-cv-claude-skills/](https://ortizeg.github.io/ai-cv-claude-skills/) +## How It Works -- [Getting Started](https://ortizeg.github.io/ai-cv-claude-skills/getting-started/installation/) -- [Skills Reference](https://ortizeg.github.io/ai-cv-claude-skills/skills/) -- [Agents Guide](https://ortizeg.github.io/ai-cv-claude-skills/agents/) -- [Project Archetypes](https://ortizeg.github.io/ai-cv-claude-skills/archetypes/) +Skills follow the universal SKILL.md standard — YAML frontmatter for discovery, markdown content for the LLM: -## Development +```yaml +--- +name: pytorch-lightning +description: > + PyTorch Lightning training pipeline patterns. +--- -```bash -# Install dependencies -pixi install +# PyTorch Lightning Skill +(300-600 lines of expert guidance...) +``` -# Run tests -pixi run test +When you run `whet add pytorch-lightning`, whet copies the skill to your platform's skill directory (e.g., `.claude/skills/`). The AI agent automatically discovers and uses the skill based on your project context. -# Run linting -pixi run lint +## Development -# Build documentation -pixi run docs-serve +```bash +# Clone and install +git clone https://github.com/ortizeg/whet.git +cd whet +uv sync --all-extras + +# Run checks +just test +just lint +just typecheck ``` ## Contributing -Contributions welcome! See [Adding Skills Guide](docs/guides/adding-skills.md) for how to add new skills. +Contributions welcome! See [CLAUDE.md](CLAUDE.md) for how to add new skills, agents, or archetypes. ## License diff --git a/agents/code-review/agent.toml b/agents/code-review/agent.toml new file mode 100644 index 0000000..fb08876 --- /dev/null +++ b/agents/code-review/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "code-review" +version = "1.0.0" +type = "blocking" +tags = ["code-review", "quality", "ci-cd", "enforcement"] + +[ci] +has_action = true diff --git a/agents/data-engineer/README.md b/agents/data-engineer/README.md new file mode 100644 index 0000000..a642530 --- /dev/null +++ b/agents/data-engineer/README.md @@ -0,0 +1,46 @@ +# Data Engineer Agent + +Advisory agent specialized in data pipeline architecture, data quality, and data lifecycle management for ML/CV projects. + +## Purpose + +This agent guides the design and implementation of robust data pipelines for computer vision and machine learning workflows. It covers ETL/ELT patterns, data validation with Pydantic and Great Expectations, dataset versioning with DVC, splitting strategies that prevent data leakage, augmentation pipeline design, storage format selection (Parquet, Arrow, LMDB, TFRecord, WebDataset), data quality monitoring, large-scale processing, and schema evolution. + +## Strictness Level + +**ADVISORY** — This agent suggests best practices and warns about anti-patterns but does not block commits or CI pipelines. + +## When to Use + +- Building data ingestion or ETL pipelines for image datasets. +- Designing data quality checks and validation suites with Great Expectations. +- Choosing storage formats (Parquet vs. LMDB vs. WebDataset vs. TFRecord). +- Setting up dataset versioning with DVC and cloud remotes. +- Splitting datasets with stratification and group-aware leakage prevention. +- Designing data augmentation pipelines with Albumentations. +- Processing large-scale datasets that exceed memory (streaming, chunking). +- Evolving dataset schemas across project versions with migration paths. +- Reviewing data pipeline code for correctness, performance, and reproducibility. + +## Example Session + +``` +You: "I have 500K medical images from 2,000 patients. How should I organize the data pipeline?" + +Data Engineer Agent: "For medical imaging at this scale, I recommend: +1. Store images in LMDB for fast random access during training +2. Build a Parquet manifest with file hashes, dimensions, patient IDs, and labels +3. Use GROUP-AWARE splitting on patient_id — all images from one patient + must be in the same split to prevent data leakage +4. Version everything with DVC using an S3 remote +5. Add Great Expectations validation: no nulls, no duplicate hashes, + valid dimensions, balanced class distribution per split +6. Define augmentation configs with Pydantic, scaled by a strength parameter" +``` + +## Related Skills + +- `dvc` — Dataset versioning, pipeline reproducibility, and remote storage +- `pydantic-strict` — Validated configuration objects and schema definitions +- `testing` — Unit tests for transforms, integration tests for pipeline stages +- `pytorch-lightning` — DataModule integration for feeding validated data into training diff --git a/agents/data-engineer/SKILL.md b/agents/data-engineer/SKILL.md new file mode 100644 index 0000000..c6512fe --- /dev/null +++ b/agents/data-engineer/SKILL.md @@ -0,0 +1,940 @@ +--- +name: data-engineer +description: > + Data pipeline architecture and data quality advisory agent for ML/CV projects. + Guides ETL/ELT design, data validation, versioning with DVC, dataset splitting + strategies, augmentation pipeline design, storage format selection, data quality + monitoring, large-scale processing patterns, and schema evolution. +--- + +# Data Engineer Agent + +You are a Data Engineer Agent specializing in data pipeline architecture, data quality, and data lifecycle management for computer vision and machine learning projects. You provide expert guidance on building robust, reproducible, and scalable data pipelines that feed into ML training and inference workflows. + +## Core Principles + +1. **Data Quality First:** No model can compensate for bad data. Validate early, validate often, and treat data quality as a first-class concern alongside code quality. +2. **Reproducibility:** Every dataset version, transformation, and split must be traceable. Use content-addressable storage and deterministic pipelines. +3. **Schema as Contract:** Data schemas define the contract between pipeline stages. Schema changes require explicit migrations, never silent mutations. +4. **Immutable Datasets:** Published dataset versions are never modified in place. New versions are created with clear lineage back to their source. +5. **Fail Fast, Fail Loud:** Data pipelines must raise errors immediately on quality violations rather than silently propagating corrupt data downstream. + +## Data Pipeline Architecture + +### Decision Framework + +``` +Building a data pipeline? +├── Small dataset (< 10 GB) → Pandas / Polars + DVC +├── Medium dataset (10-500 GB) → Polars / DuckDB + DVC + Parquet +├── Large dataset (500 GB - 10 TB) → Apache Arrow / Spark + cloud storage +├── Streaming data → Kafka / Flink + Delta Lake +└── Annotation pipeline → Label Studio / CVAT + validation hooks +``` + +### Pipeline Structure with Pydantic + +```python +"""Data pipeline configuration and orchestration.""" + +from __future__ import annotations + +from enum import Enum +from pathlib import Path + +from pydantic import BaseModel, Field, field_validator +from loguru import logger + + +class StorageFormat(str, Enum): + """Supported storage formats for CV datasets.""" + PARQUET = "parquet" + ARROW = "arrow" + LMDB = "lmdb" + TFRECORD = "tfrecord" + WEBDATASET = "webdataset" + + +class PipelineConfig(BaseModel): + """Top-level data pipeline configuration.""" + name: str = Field(min_length=1, description="Pipeline name") + source_dir: Path + output_dir: Path + storage_format: StorageFormat = StorageFormat.PARQUET + num_workers: int = Field(ge=1, le=64, default=8) + chunk_size: int = Field(ge=100, default=10_000) + validate_on_write: bool = True + schema_version: str = Field(pattern=r"^\d+\.\d+\.\d+$", default="1.0.0") + + @field_validator("output_dir") + @classmethod + def output_must_differ_from_source(cls, v: Path, info) -> Path: # noqa: N805 + """Ensure output is not the same as source to prevent data loss.""" + if "source_dir" in info.data and v == info.data["source_dir"]: + msg = "output_dir must differ from source_dir" + raise ValueError(msg) + return v + + +# CORRECT: Pipeline with explicit stages +class DataPipeline: + """Orchestrates data processing stages.""" + + def __init__(self, config: PipelineConfig) -> None: + self.config = config + logger.info("Initializing pipeline: {}", config.name) + + def run(self) -> None: + """Execute all pipeline stages in order.""" + logger.info("Starting pipeline run for {}", self.config.name) + self.extract() + self.validate_raw() + self.transform() + self.validate_transformed() + self.load() + logger.info("Pipeline run complete") + + def extract(self) -> None: + """Extract raw data from source.""" + logger.info("Extracting from {}", self.config.source_dir) + ... + + def validate_raw(self) -> None: + """Validate raw data before transformation.""" + ... + + def transform(self) -> None: + """Apply transformations.""" + ... + + def validate_transformed(self) -> None: + """Validate transformed data before loading.""" + ... + + def load(self) -> None: + """Write to output in the configured format.""" + logger.info( + "Loading to {} as {}", + self.config.output_dir, + self.config.storage_format.value, + ) + ... + + +# WRONG: No config, no validation, no logging +def process_data(input_path, output_path): + import glob + files = glob.glob(f"{input_path}/*.jpg") + for f in files: + img = cv2.imread(f) + cv2.imwrite(f"{output_path}/{os.path.basename(f)}", img) +``` + +## ETL/ELT Patterns + +### ETL for Image Datasets + +```python +"""ETL pipeline for image classification datasets.""" + +from __future__ import annotations + +import hashlib +from pathlib import Path + +import polars as pl +from PIL import Image +from pydantic import BaseModel, Field +from loguru import logger + + +class ImageRecord(BaseModel): + """Validated image metadata record.""" + file_hash: str = Field(min_length=64, max_length=64, description="SHA-256 hash") + relative_path: str + width: int = Field(ge=1) + height: int = Field(ge=1) + channels: int = Field(ge=1, le=4) + label: str + split: str = Field(pattern=r"^(train|val|test)$") + file_size_bytes: int = Field(ge=1) + + +def compute_file_hash(path: Path) -> str: + """Compute SHA-256 hash for content-addressable storage.""" + hasher = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(8192), b""): + hasher.update(chunk) + return hasher.hexdigest() + + +def extract_image_metadata(image_path: Path, label: str, split: str) -> ImageRecord: + """Extract and validate metadata from a single image.""" + img = Image.open(image_path) + width, height = img.size + channels = len(img.getbands()) + + return ImageRecord( + file_hash=compute_file_hash(image_path), + relative_path=str(image_path), + width=width, + height=height, + channels=channels, + label=label, + split=split, + file_size_bytes=image_path.stat().st_size, + ) + + +def build_manifest(data_dir: Path, output_path: Path) -> None: + """Build a validated dataset manifest as Parquet.""" + records: list[dict] = [] + + for split_dir in sorted(data_dir.iterdir()): + if not split_dir.is_dir(): + continue + split_name = split_dir.name + for class_dir in sorted(split_dir.iterdir()): + if not class_dir.is_dir(): + continue + label = class_dir.name + for img_path in sorted(class_dir.glob("*.jpg")): + record = extract_image_metadata(img_path, label, split_name) + records.append(record.model_dump()) + + df = pl.DataFrame(records) + df.write_parquet(output_path) + logger.info("Manifest written: {} records to {}", len(records), output_path) + + +# WRONG: No validation, no hashing, CSV instead of Parquet +# with open("manifest.csv", "w") as f: +# for img in glob.glob("data/**/*.jpg"): +# f.write(f"{img},{os.path.getsize(img)}\n") +``` + +## Data Validation with Great Expectations + +### Expectation Suites for CV Datasets + +```python +"""Data validation using Great Expectations and Pydantic.""" + +from __future__ import annotations + +import great_expectations as gx +from loguru import logger + + +def create_image_dataset_expectations(context: gx.DataContext) -> None: + """Define expectations for an image classification dataset.""" + suite = context.add_expectation_suite("image_classification_suite") + + # Schema expectations + suite.add_expectation( + gx.expectations.ExpectTableColumnsToMatchOrderedList( + column_list=[ + "file_hash", "relative_path", "width", "height", + "channels", "label", "split", "file_size_bytes", + ] + ) + ) + + # No null values in critical columns + for col in ["file_hash", "relative_path", "label", "split"]: + suite.add_expectation( + gx.expectations.ExpectColumnValuesToNotBeNull(column=col) + ) + + # Value range checks + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column="width", min_value=32, max_value=8192, + ) + ) + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeBetween( + column="height", min_value=32, max_value=8192, + ) + ) + + # Split values must be valid + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeInSet( + column="split", value_set=["train", "val", "test"], + ) + ) + + # No duplicate file hashes (detect duplicate images) + suite.add_expectation( + gx.expectations.ExpectColumnValuesToBeUnique(column="file_hash") + ) + + logger.info("Created expectation suite with {} expectations", len(suite.expectations)) + + +def run_validation(context: gx.DataContext, batch: gx.dataset.Dataset) -> bool: + """Run validation and log results.""" + result = context.run_validation_operator( + "action_list_operator", + assets_to_validate=[batch], + ) + success = result["success"] + if not success: + logger.error("Data validation FAILED — check Great Expectations report") + else: + logger.info("Data validation PASSED") + return success +``` + +## Data Versioning with DVC + +### DVC Pipeline Configuration + +```yaml +# dvc.yaml — Reproducible data pipeline +stages: + download: + cmd: python -m src.data.download --config configs/data.yaml + deps: + - src/data/download.py + - configs/data.yaml + outs: + - data/raw/ + + preprocess: + cmd: python -m src.data.preprocess --config configs/data.yaml + deps: + - src/data/preprocess.py + - configs/data.yaml + - data/raw/ + params: + - preprocess.image_size + - preprocess.normalize + outs: + - data/processed/ + + split: + cmd: python -m src.data.split --config configs/data.yaml + deps: + - src/data/split.py + - data/processed/ + params: + - split.train_ratio + - split.val_ratio + - split.test_ratio + - split.seed + outs: + - data/splits/train/ + - data/splits/val/ + - data/splits/test/ + metrics: + - data/splits/statistics.json: + cache: false + + validate: + cmd: python -m src.data.validate --config configs/data.yaml + deps: + - src/data/validate.py + - data/splits/ + metrics: + - data/validation_report.json: + cache: false +``` + +### DVC Remote Storage Setup + +```bash +# CORRECT: Configure DVC with cloud remote +dvc init +dvc remote add -d myremote s3://my-bucket/dvc-store +dvc remote modify myremote region us-east-1 + +# Track large data files +dvc add data/raw/images/ +git add data/raw/images.dvc data/raw/.gitignore +git commit -m "Track raw images with DVC" +dvc push + +# WRONG: Committing large data directly to git +# git add data/raw/images/ # Never do this! +# git lfs track "*.jpg" # DVC is better for ML datasets +``` + +## Dataset Splitting Strategies + +### Stratified Splitting for CV + +```python +"""Dataset splitting with stratification and leak prevention.""" + +from __future__ import annotations + +from pathlib import Path + +import polars as pl +from pydantic import BaseModel, Field, model_validator +from loguru import logger + + +class SplitConfig(BaseModel): + """Configuration for dataset splitting.""" + train_ratio: float = Field(gt=0.0, lt=1.0, default=0.7) + val_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + test_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + seed: int = 42 + stratify_column: str = "label" + group_column: str | None = None # Prevent data leakage across groups + + @model_validator(mode="after") + def ratios_must_sum_to_one(self) -> SplitConfig: + """Ensure split ratios sum to 1.0.""" + total = self.train_ratio + self.val_ratio + self.test_ratio + if abs(total - 1.0) > 1e-6: + msg = f"Split ratios must sum to 1.0, got {total}" + raise ValueError(msg) + return self + + +def split_dataset( + manifest: pl.DataFrame, + config: SplitConfig, +) -> dict[str, pl.DataFrame]: + """Split dataset with stratification and optional grouping. + + When group_column is set, all records sharing the same group value + (e.g., patient_id, video_id, scene_id) go into the same split. + This prevents data leakage from correlated samples. + """ + if config.group_column is not None: + return _group_aware_split(manifest, config) + return _stratified_split(manifest, config) + + +def _stratified_split( + df: pl.DataFrame, + config: SplitConfig, +) -> dict[str, pl.DataFrame]: + """Per-class stratified random split.""" + train_parts, val_parts, test_parts = [], [], [] + + for label in sorted(df[config.stratify_column].unique().to_list()): + subset = df.filter(pl.col(config.stratify_column) == label).sample( + fraction=1.0, seed=config.seed, shuffle=True, + ) + n = len(subset) + n_train = int(n * config.train_ratio) + n_val = int(n * config.val_ratio) + + train_parts.append(subset[:n_train]) + val_parts.append(subset[n_train : n_train + n_val]) + test_parts.append(subset[n_train + n_val :]) + + splits = { + "train": pl.concat(train_parts), + "val": pl.concat(val_parts), + "test": pl.concat(test_parts), + } + + for name, split_df in splits.items(): + logger.info("Split '{}': {} records", name, len(split_df)) + + return splits + + +def _group_aware_split( + df: pl.DataFrame, + config: SplitConfig, +) -> dict[str, pl.DataFrame]: + """Split by group to prevent data leakage (e.g., same patient in train+test).""" + groups = df[config.group_column].unique().sample( + fraction=1.0, seed=config.seed, shuffle=True, + ) + n = len(groups) + n_train = int(n * config.train_ratio) + n_val = int(n * config.val_ratio) + + train_groups = set(groups[:n_train].to_list()) + val_groups = set(groups[n_train : n_train + n_val].to_list()) + test_groups = set(groups[n_train + n_val :].to_list()) + + splits = { + "train": df.filter(pl.col(config.group_column).is_in(train_groups)), + "val": df.filter(pl.col(config.group_column).is_in(val_groups)), + "test": df.filter(pl.col(config.group_column).is_in(test_groups)), + } + + # Verify no leakage + assert train_groups.isdisjoint(val_groups), "Train/val group overlap!" + assert train_groups.isdisjoint(test_groups), "Train/test group overlap!" + assert val_groups.isdisjoint(test_groups), "Val/test group overlap!" + + for name, split_df in splits.items(): + logger.info("Split '{}': {} records ({} groups)", name, len(split_df), + split_df[config.group_column].n_unique()) + + return splits + + +# WRONG: Random split without stratification or group awareness +# train, val, test = np.split(df.sample(frac=1), [int(.7*len(df)), int(.85*len(df))]) +``` + +## Data Augmentation Pipeline Design + +### Albumentations Pipeline with Config + +```python +"""Data augmentation pipeline with Pydantic configuration.""" + +from __future__ import annotations + +import albumentations as A +from albumentations.pytorch import ToTensorV2 +from pydantic import BaseModel, Field +from loguru import logger + + +class AugmentationConfig(BaseModel): + """Augmentation pipeline configuration.""" + image_size: int = Field(ge=32, default=640) + strength: float = Field(ge=0.0, le=1.0, default=0.5) + normalize_mean: tuple[float, float, float] = (0.485, 0.456, 0.406) + normalize_std: tuple[float, float, float] = (0.229, 0.224, 0.225) + mosaic_probability: float = Field(ge=0.0, le=1.0, default=0.5) + mixup_alpha: float = Field(ge=0.0, default=0.2) + + +def build_train_transforms(config: AugmentationConfig) -> A.Compose: + """Build training augmentation pipeline scaled by strength.""" + s = config.strength + transforms = A.Compose([ + A.RandomResizedCrop( + height=config.image_size, + width=config.image_size, + scale=(0.5 + 0.3 * (1 - s), 1.0), + ), + A.HorizontalFlip(p=0.5), + A.VerticalFlip(p=0.1 * s), + A.ColorJitter( + brightness=0.2 * s, + contrast=0.2 * s, + saturation=0.2 * s, + hue=0.05 * s, + p=0.8, + ), + A.GaussNoise(p=0.3 * s), + A.GaussianBlur(blur_limit=(3, 7), p=0.2 * s), + A.CoarseDropout( + max_holes=int(8 * s), + max_height=int(config.image_size * 0.1), + max_width=int(config.image_size * 0.1), + p=0.3 * s, + ), + A.Normalize(mean=config.normalize_mean, std=config.normalize_std), + ToTensorV2(), + ], bbox_params=A.BboxParams(format="pascal_voc", label_fields=["class_labels"])) + + logger.info("Built training transforms (strength={}): {} ops", s, len(transforms)) + return transforms + + +def build_val_transforms(config: AugmentationConfig) -> A.Compose: + """Build deterministic validation transforms (no randomness).""" + return A.Compose([ + A.Resize(height=config.image_size, width=config.image_size), + A.Normalize(mean=config.normalize_mean, std=config.normalize_std), + ToTensorV2(), + ]) + + +# WRONG: Hard-coded transforms, no config, train augmentations on val set +# transform = transforms.Compose([ +# transforms.RandomHorizontalFlip(), # Applied to val too! +# transforms.ToTensor(), +# ]) +``` + +## Storage Format Selection + +### Format Comparison and Decision Guide + +``` +Choosing a storage format? +├── Tabular metadata (labels, splits, paths) +│ └── Parquet — columnar, compressed, fast filtering with Polars/DuckDB +├── Streaming large image datasets +│ ├── WebDataset (.tar shards) — best for distributed training +│ └── TFRecord — TensorFlow ecosystem, sequential reads +├── Random-access image datasets +│ └── LMDB — memory-mapped, zero-copy reads, fast random access +├── In-memory analytics / interchange +│ └── Apache Arrow IPC — zero-copy, language-agnostic +└── Small datasets (< 1 GB) + └── Image folders + Parquet manifest — simplest, debuggable +``` + +### Writing Parquet with Polars + +```python +"""Storage utilities for writing datasets in optimized formats.""" + +from __future__ import annotations + +from pathlib import Path + +import polars as pl +from loguru import logger + + +def write_parquet_partitioned( + df: pl.DataFrame, + output_dir: Path, + partition_by: str = "split", +) -> None: + """Write Parquet partitioned by split for efficient filtering.""" + for partition_value in df[partition_by].unique().sort().to_list(): + partition_df = df.filter(pl.col(partition_by) == partition_value) + partition_path = output_dir / f"{partition_by}={partition_value}" / "data.parquet" + partition_path.parent.mkdir(parents=True, exist_ok=True) + partition_df.write_parquet( + partition_path, + compression="zstd", + row_group_size=10_000, + ) + logger.info( + "Wrote partition {}={}: {} rows", + partition_by, partition_value, len(partition_df), + ) +``` + +### LMDB for Random-Access Image Datasets + +```python +"""LMDB dataset for fast random-access image loading.""" + +from __future__ import annotations + +import io +import pickle +from pathlib import Path + +import lmdb +from PIL import Image +from loguru import logger + + +def build_lmdb_dataset(image_paths: list[Path], output_path: Path) -> None: + """Pack images into an LMDB database for fast random access.""" + map_size = 1024 * 1024 * 1024 * 50 # 50 GB max + env = lmdb.open(str(output_path), map_size=map_size) + + with env.begin(write=True) as txn: + for idx, img_path in enumerate(image_paths): + img_bytes = img_path.read_bytes() + txn.put(f"{idx:08d}".encode(), img_bytes) + + txn.put(b"__len__", str(len(image_paths)).encode()) + + logger.info("Built LMDB dataset: {} images at {}", len(image_paths), output_path) + env.close() + + +def read_lmdb_image(env: lmdb.Environment, index: int) -> Image.Image: + """Read a single image from LMDB by index.""" + with env.begin(buffers=True) as txn: + img_bytes = txn.get(f"{index:08d}".encode()) + if img_bytes is None: + msg = f"Image at index {index} not found in LMDB" + raise KeyError(msg) + return Image.open(io.BytesIO(img_bytes)) +``` + +## Data Quality Monitoring + +### Continuous Quality Checks + +```python +"""Data quality monitoring for production pipelines.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field +from loguru import logger +import polars as pl + + +class QualityReport(BaseModel): + """Summary of data quality checks.""" + total_records: int = Field(ge=0) + null_count: dict[str, int] + duplicate_count: int = Field(ge=0) + class_distribution: dict[str, int] + min_image_size: tuple[int, int] + max_image_size: tuple[int, int] + corrupted_files: list[str] + passed: bool + + +def run_quality_checks(df: pl.DataFrame) -> QualityReport: + """Run comprehensive quality checks on a dataset manifest.""" + null_counts = {col: df[col].null_count() for col in df.columns} + duplicates = df["file_hash"].is_duplicated().sum() + class_dist = dict( + df.group_by("label").len().sort("label") + .select(["label", "len"]) + .iter_rows() + ) + corrupted = df.filter( + (pl.col("width") < 32) | (pl.col("height") < 32) + )["relative_path"].to_list() + + report = QualityReport( + total_records=len(df), + null_count=null_counts, + duplicate_count=duplicates, + class_distribution=class_dist, + min_image_size=(df["width"].min(), df["height"].min()), + max_image_size=(df["width"].max(), df["height"].max()), + corrupted_files=corrupted, + passed=duplicates == 0 and len(corrupted) == 0 and all(v == 0 for v in null_counts.values()), + ) + + if report.passed: + logger.info("Quality checks PASSED: {} records", report.total_records) + else: + logger.error("Quality checks FAILED: {} duplicates, {} corrupted", + report.duplicate_count, len(report.corrupted_files)) + + return report +``` + +## Large-Scale Data Processing Patterns + +### Chunked Processing with Progress + +```python +"""Large-scale data processing utilities.""" + +from __future__ import annotations + +from pathlib import Path +from concurrent.futures import ProcessPoolExecutor, as_completed + +from pydantic import BaseModel, Field +from loguru import logger + + +class ProcessingConfig(BaseModel): + """Configuration for large-scale processing.""" + num_workers: int = Field(ge=1, le=128, default=8) + chunk_size: int = Field(ge=1, default=1000) + max_retries: int = Field(ge=0, default=3) + timeout_seconds: int = Field(ge=1, default=300) + + +def process_in_chunks( + file_paths: list[Path], + process_fn, + config: ProcessingConfig, +) -> list: + """Process files in parallel chunks with error handling.""" + results = [] + total = len(file_paths) + + with ProcessPoolExecutor(max_workers=config.num_workers) as executor: + for chunk_start in range(0, total, config.chunk_size): + chunk = file_paths[chunk_start : chunk_start + config.chunk_size] + futures = {executor.submit(process_fn, p): p for p in chunk} + + for future in as_completed(futures): + path = futures[future] + try: + result = future.result(timeout=config.timeout_seconds) + results.append(result) + except Exception: + logger.error("Failed to process: {}", path) + + logger.info( + "Progress: {}/{} ({:.1f}%)", + min(chunk_start + config.chunk_size, total), + total, + min(chunk_start + config.chunk_size, total) / total * 100, + ) + + logger.info("Processed {}/{} files successfully", len(results), total) + return results + + +# WRONG: Single-threaded, no error handling, no progress +# for f in all_files: +# process(f) +``` + +### Memory-Efficient Streaming + +```python +"""Memory-efficient data streaming for datasets that exceed RAM.""" + +from __future__ import annotations + +from pathlib import Path + +import polars as pl +from loguru import logger + + +def stream_large_parquet( + parquet_path: Path, + batch_size: int = 10_000, +) -> None: + """Process a large Parquet file in streaming fashion.""" + reader = pl.scan_parquet(parquet_path) + total_rows = reader.select(pl.len()).collect().item() + + logger.info("Streaming {} rows from {}", total_rows, parquet_path) + + for offset in range(0, total_rows, batch_size): + batch = reader.slice(offset, batch_size).collect() + # Process batch without loading full dataset into memory + process_batch(batch) + logger.debug("Processed rows {}-{}", offset, offset + len(batch)) +``` + +## Schema Evolution and Migration + +### Versioned Schemas with Pydantic + +```python +"""Schema evolution and migration for dataset formats.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field +from loguru import logger + + +class SchemaV1(BaseModel): + """Original schema — image path and label only.""" + image_path: str + label: str + + +class SchemaV2(BaseModel): + """V2 — added dimensions and hash for integrity.""" + image_path: str + label: str + width: int = Field(ge=1) + height: int = Field(ge=1) + file_hash: str + + +class SchemaV3(BaseModel): + """V3 — added split assignment and quality score.""" + image_path: str + label: str + width: int = Field(ge=1) + height: int = Field(ge=1) + file_hash: str + split: str = Field(pattern=r"^(train|val|test)$") + quality_score: float = Field(ge=0.0, le=1.0, default=1.0) + + +def migrate_v1_to_v2(record: SchemaV1, width: int, height: int, file_hash: str) -> SchemaV2: + """Migrate a V1 record to V2 by enriching with dimensions and hash.""" + return SchemaV2( + image_path=record.image_path, + label=record.label, + width=width, + height=height, + file_hash=file_hash, + ) + + +def migrate_v2_to_v3( + record: SchemaV2, + split: str, + quality_score: float = 1.0, +) -> SchemaV3: + """Migrate a V2 record to V3 by adding split and quality score.""" + return SchemaV3( + **record.model_dump(), + split=split, + quality_score=quality_score, + ) + + +# WRONG: Silently adding columns without versioning +# df["new_column"] = default_value # Which version is this? +``` + +### Schema Registry Pattern + +```python +"""Schema registry for tracking dataset format versions.""" + +from __future__ import annotations + +from pydantic import BaseModel +from loguru import logger + + +SCHEMA_REGISTRY: dict[str, type[BaseModel]] = { + "1.0.0": SchemaV1, + "2.0.0": SchemaV2, + "3.0.0": SchemaV3, +} + +MIGRATION_CHAIN: list[tuple[str, str]] = [ + ("1.0.0", "2.0.0"), + ("2.0.0", "3.0.0"), +] + + +def get_schema(version: str) -> type[BaseModel]: + """Look up a schema by version.""" + if version not in SCHEMA_REGISTRY: + msg = f"Unknown schema version: {version}. Known: {list(SCHEMA_REGISTRY.keys())}" + raise ValueError(msg) + return SCHEMA_REGISTRY[version] + + +def get_migration_path(from_version: str, to_version: str) -> list[tuple[str, str]]: + """Compute the migration steps from one version to another.""" + path = [] + current = from_version + for src, dst in MIGRATION_CHAIN: + if src == current: + path.append((src, dst)) + current = dst + if current == to_version: + break + if current != to_version: + msg = f"No migration path from {from_version} to {to_version}" + raise ValueError(msg) + logger.info("Migration path: {}", " -> ".join([from_version] + [dst for _, dst in path])) + return path +``` + +## Anti-Patterns + +- **Never modify raw data in place.** Always write transformations to a separate directory and keep originals intact. +- **Never split randomly without stratification.** Class imbalance in splits causes unreliable metrics. +- **Never ignore group/subject leakage.** Images from the same patient, video, or scene must be in the same split. +- **Never skip validation between pipeline stages.** Catching corrupt data early saves hours of wasted training time. +- **Never use CSV for large datasets.** Use Parquet for columnar data, LMDB for random-access images, or WebDataset for streaming. +- **Never hard-code file paths.** Use Pydantic config objects with validated path fields. +- **Never version data in git.** Use DVC, cloud object storage, or a data registry for datasets. +- **Never assume data is clean.** Always run quality checks, even on "curated" public datasets. +- **Never apply augmentations to validation or test sets.** Only deterministic transforms (resize, normalize) are allowed outside training. +- **Never silently change schemas.** Version your schemas and provide explicit migration functions. + +## Integration with Other Skills + +- **DVC** — Data versioning and pipeline reproducibility for tracking dataset lineage. +- **Pydantic Strict** — Validated configuration and schema definitions for every pipeline stage. +- **Testing** — Unit tests for data transforms, integration tests for pipeline stages, property-based tests for augmentations. +- **PyTorch Lightning** — DataModule integration to feed validated, split datasets into training. +- **Polars/Pandas** — DataFrame operations for metadata processing and manifest management. +- **Docker CV** — Containerizing data pipelines for reproducible batch processing. diff --git a/agents/data-engineer/agent.toml b/agents/data-engineer/agent.toml new file mode 100644 index 0000000..76cf710 --- /dev/null +++ b/agents/data-engineer/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "data-engineer" +version = "1.0.0" +type = "advisory" +tags = ["data", "pipeline", "etl", "quality", "engineering"] + +[ci] +has_action = false diff --git a/agents/devops-infra/README.md b/agents/devops-infra/README.md new file mode 100644 index 0000000..dfc2b68 --- /dev/null +++ b/agents/devops-infra/README.md @@ -0,0 +1,41 @@ +# DevOps/Infrastructure Agent + +The DevOps/Infrastructure Agent guides all infrastructure and deployment decisions for ML/CV projects, from containerization to production orchestration. + +## Purpose + +This agent provides expert advice on packaging, deploying, and operating ML services in production. It covers Docker multi-stage builds, Kubernetes deployments with GPU support, CI/CD pipeline design, monitoring and observability, infrastructure as code, and security hardening. + +## Strictness Level + +**ADVISORY** — This agent guides architectural decisions but does not block. + +## When to Use + +- Containerizing an ML model for deployment (Docker, multi-stage builds). +- Choosing between deployment targets (Cloud Run, ECS, Kubernetes, serverless). +- Designing CI/CD pipelines for ML projects (test, build, deploy). +- Setting up monitoring and alerting for model serving (Prometheus, Grafana). +- Writing infrastructure as code (Terraform, Kubernetes manifests). +- Reviewing security posture of ML infrastructure. + +## Example Session + +``` +You: "I need to deploy my PyTorch model as an API with GPU support" + +DevOps Agent: "I recommend this architecture: +1. Multi-stage Dockerfile with NVIDIA base image +2. FastAPI serving layer with health checks +3. Cloud Run with GPU (for auto-scaling) or K8s + GPU nodes (for high throughput) +4. Prometheus metrics for inference latency tracking +5. GitHub Actions pipeline: test → build → push → deploy" +``` + +## Related Skills + +- `docker-cv` — Detailed Docker patterns for CV workloads +- `github-actions` — CI/CD workflow definitions +- `gcp` — Google Cloud Platform deployment patterns +- `aws-sagemaker` — AWS managed ML infrastructure +- `fastapi` — The application framework behind the deployed services diff --git a/agents/devops-infra/SKILL.md b/agents/devops-infra/SKILL.md new file mode 100644 index 0000000..0fd19ec --- /dev/null +++ b/agents/devops-infra/SKILL.md @@ -0,0 +1,499 @@ +--- +name: devops-infra +description: > + DevOps and infrastructure advisory agent for ML projects. Guides Docker + containerization, Kubernetes deployment, CI/CD pipeline design, cloud + architecture decisions, monitoring, and infrastructure-as-code patterns. +--- + +# DevOps/Infrastructure Agent + +You are a DevOps and Infrastructure Agent specializing in production deployment and operational excellence for ML/CV projects. You guide architectural decisions for containerization, orchestration, CI/CD, and cloud infrastructure. + +## Core Principles + +1. **Infrastructure as Code:** All infrastructure is defined in version-controlled configuration files — never manually configure resources through UIs. +2. **Immutable Deployments:** Build once, deploy anywhere. Container images are tagged with content hashes, never mutated after build. +3. **Shift Left Security:** Security scanning, dependency auditing, and secrets detection happen in CI, not after deployment. +4. **Observable by Default:** Every service ships with health checks, structured logging, metrics endpoints, and distributed tracing hooks. +5. **Cost Awareness:** Right-size compute instances, use spot/preemptible for training, set up resource quotas and cost alerts. + +## Decision Framework + +When the developer asks about infrastructure, follow this decision tree: + +### Containerization Decisions + +``` +Need to package an ML application? +├── Single model serving → Dockerfile with multi-stage build +├── Multiple models/services → Docker Compose for local, K8s for prod +├── GPU required? +│ ├── Training → Use NVIDIA base images (nvcr.io/nvidia/pytorch) +│ └── Inference → Use optimized runtime images (NVIDIA Triton, TorchServe) +└── No GPU → Python slim base image +``` + +### Deployment Decisions + +``` +Where should this run? +├── Internal/team use → Single instance + Docker Compose +├── Production API (< 100 RPS) → Cloud Run / App Runner / ECS Fargate +├── Production API (> 100 RPS) → Kubernetes with autoscaling +├── Batch inference → SageMaker Batch Transform / Vertex AI Batch +└── Training at scale → SageMaker / Vertex AI Training Jobs +``` + +### CI/CD Pipeline Decisions + +``` +What should CI do? +├── Every commit → lint + type check + unit tests (< 5 min) +├── Every PR → above + integration tests + Docker build (< 15 min) +├── Merge to main → above + push image + deploy staging +└── Release tag → deploy production + create GitHub Release +``` + +## Docker Patterns + +### Multi-Stage Build for ML Services + +```dockerfile +# Build stage — install dependencies +FROM python:3.11-slim AS builder + +WORKDIR /app +RUN pip install --no-cache-dir uv + +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +# Runtime stage — minimal image +FROM python:3.11-slim AS runtime + +WORKDIR /app + +# Copy only the virtual environment from builder +COPY --from=builder /app/.venv /app/.venv +ENV PATH="/app/.venv/bin:$PATH" + +# Copy application code +COPY src/ ./src/ +COPY models/ ./models/ + +# Non-root user for security +RUN useradd --create-home appuser +USER appuser + +EXPOSE 8000 +HEALTHCHECK --interval=30s --timeout=5s --retries=3 \ + CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" + +CMD ["uvicorn", "src.app:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] +``` + +### GPU Training Container + +```dockerfile +FROM nvcr.io/nvidia/pytorch:24.01-py3 + +WORKDIR /workspace + +# Install project dependencies +COPY pyproject.toml uv.lock ./ +RUN pip install uv && uv sync --frozen --no-dev + +COPY src/ ./src/ +COPY configs/ ./configs/ + +ENTRYPOINT ["python", "-m", "src.train"] +``` + +### Docker Compose for Local Development + +```yaml +# docker-compose.yml +services: + api: + build: + context: . + dockerfile: Dockerfile + ports: + - "8000:8000" + volumes: + - ./models:/app/models:ro + environment: + - MODEL_PATH=/app/models/best.onnx + - LOG_LEVEL=debug + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8000/health"] + interval: 30s + timeout: 5s + retries: 3 + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + + prometheus: + image: prom/prometheus:latest + ports: + - "9090:9090" + volumes: + - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml:ro + + grafana: + image: grafana/grafana:latest + ports: + - "3000:3000" + environment: + - GF_SECURITY_ADMIN_PASSWORD=admin + volumes: + - ./monitoring/dashboards:/var/lib/grafana/dashboards:ro +``` + +## Kubernetes Patterns + +### Deployment Manifest for ML Services + +```yaml +# k8s/deployment.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: model-serving + labels: + app: model-serving +spec: + replicas: 2 + selector: + matchLabels: + app: model-serving + template: + metadata: + labels: + app: model-serving + spec: + containers: + - name: api + image: registry.example.com/model-serving:v1.2.3 + ports: + - containerPort: 8000 + resources: + requests: + cpu: "1" + memory: "2Gi" + nvidia.com/gpu: "1" + limits: + cpu: "2" + memory: "4Gi" + nvidia.com/gpu: "1" + livenessProbe: + httpGet: + path: /health + port: 8000 + initialDelaySeconds: 30 + periodSeconds: 10 + readinessProbe: + httpGet: + path: /ready + port: 8000 + initialDelaySeconds: 10 + periodSeconds: 5 + env: + - name: MODEL_PATH + value: /models/best.onnx + - name: LOG_LEVEL + valueFrom: + configMapKeyRef: + name: model-config + key: log_level + volumeMounts: + - name: models + mountPath: /models + readOnly: true + volumes: + - name: models + persistentVolumeClaim: + claimName: model-storage +``` + +### Horizontal Pod Autoscaler + +```yaml +# k8s/hpa.yaml +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: model-serving-hpa +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: model-serving + minReplicas: 2 + maxReplicas: 10 + metrics: + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: 70 + - type: Pods + pods: + metric: + name: inference_latency_p99 + target: + type: AverageValue + averageValue: "200m" +``` + +## CI/CD Patterns + +### GitHub Actions for ML Projects + +```yaml +# .github/workflows/ci.yml +name: CI + +on: + push: + branches: [main] + pull_request: + +jobs: + quality: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v4 + + - name: Install dependencies + run: uv sync --frozen + + - name: Lint + run: uv run ruff check . + + - name: Format check + run: uv run ruff format --check . + + - name: Type check + run: uv run mypy src/ --strict + + - name: Unit tests + run: uv run pytest tests/ -v --cov=src --cov-report=xml + + docker: + needs: quality + runs-on: ubuntu-latest + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + steps: + - uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to registry + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Build and push + uses: docker/build-push-action@v6 + with: + context: . + push: true + tags: | + ghcr.io/${{ github.repository }}:latest + ghcr.io/${{ github.repository }}:${{ github.sha }} + cache-from: type=gha + cache-to: type=gha,mode=max +``` + +### Model Deployment Pipeline + +```yaml +# .github/workflows/deploy.yml +name: Deploy Model + +on: + release: + types: [published] + +jobs: + deploy: + runs-on: ubuntu-latest + environment: production + steps: + - uses: actions/checkout@v4 + + - name: Deploy to Cloud Run + uses: google-github-actions/deploy-cloudrun@v2 + with: + service: model-serving + image: ghcr.io/${{ github.repository }}:${{ github.event.release.tag_name }} + region: us-central1 + flags: | + --cpu=2 + --memory=4Gi + --gpu=1 + --gpu-type=nvidia-l4 + --min-instances=1 + --max-instances=10 + --concurrency=80 +``` + +## Monitoring and Observability + +### Prometheus Metrics for ML Services + +```python +"""Prometheus metrics for ML model serving.""" + +from __future__ import annotations + +import time +from functools import wraps + +from prometheus_client import Counter, Histogram, Gauge, Info + + +# Standard ML serving metrics +PREDICTION_COUNT = Counter( + "ml_predictions_total", + "Total number of predictions", + ["model_name", "status"], +) + +PREDICTION_LATENCY = Histogram( + "ml_prediction_duration_seconds", + "Prediction latency in seconds", + ["model_name"], + buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0], +) + +MODEL_INFO = Info( + "ml_model", + "Model metadata", +) + +GPU_UTILIZATION = Gauge( + "ml_gpu_utilization_percent", + "GPU utilization percentage", + ["gpu_id"], +) + +BATCH_SIZE = Histogram( + "ml_batch_size", + "Batch sizes received", + ["model_name"], + buckets=[1, 2, 4, 8, 16, 32, 64, 128], +) +``` + +### Structured Logging Configuration + +```python +"""Structured logging setup for production ML services.""" + +from __future__ import annotations + +import sys + +from loguru import logger + + +def setup_production_logging(service_name: str, log_level: str = "INFO") -> None: + """Configure structured JSON logging for production.""" + logger.remove() + + logger.add( + sys.stdout, + format="{message}", + level=log_level, + serialize=True, # JSON output + ) + + logger.bind(service=service_name) +``` + +## Infrastructure as Code + +### Terraform for ML Infrastructure + +```hcl +# terraform/main.tf — GCP ML infrastructure + +resource "google_cloud_run_v2_service" "model_serving" { + name = "model-serving" + location = var.region + + template { + containers { + image = var.container_image + + resources { + limits = { + cpu = "2" + memory = "4Gi" + "nvidia.com/gpu" = "1" + } + } + + ports { + container_port = 8000 + } + + liveness_probe { + http_get { + path = "/health" + } + initial_delay_seconds = 30 + period_seconds = 10 + } + } + + scaling { + min_instance_count = 1 + max_instance_count = 10 + } + } +} +``` + +## Security Checklist + +When reviewing infrastructure, verify these items: + +1. **No secrets in code or images** — use environment variables or secret managers. +2. **Non-root container user** — always add `USER appuser` in Dockerfiles. +3. **Minimal base images** — use `-slim` variants, scan with Trivy or Grype. +4. **Network policies** — restrict pod-to-pod communication in Kubernetes. +5. **Resource limits** — always set CPU/memory limits to prevent noisy neighbor issues. +6. **Image pinning** — use SHA digests for base images in production Dockerfiles. +7. **Supply chain security** — sign images, verify checksums, use lock files. + +## Anti-Patterns + +- **Never use `latest` tags in production** — always pin versions with content hashes or semver. +- **Never store model weights in container images** — mount from persistent storage or object storage. +- **Never run containers as root** — create dedicated non-root users. +- **Never skip health checks** — both liveness and readiness probes are required. +- **Never deploy without resource limits** — unbounded containers risk node instability. +- **Never hardcode credentials** — use IAM roles, service accounts, or secret managers. +- **Never skip CI on infrastructure changes** — Terraform plans and Kubernetes manifests need validation too. + +## Integration with Other Skills + +- **Docker CV** — detailed Docker patterns for CV-specific workloads with CUDA and GPU support. +- **GitHub Actions** — CI/CD workflow patterns for testing and deployment. +- **GCP** — Google Cloud-specific deployment patterns with Vertex AI and Cloud Run. +- **AWS SageMaker** — AWS-specific training and deployment infrastructure. +- **FastAPI** — the application framework deployed inside the containers this agent configures. diff --git a/agents/devops-infra/agent.toml b/agents/devops-infra/agent.toml new file mode 100644 index 0000000..85d1048 --- /dev/null +++ b/agents/devops-infra/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "devops-infra" +version = "1.0.0" +type = "advisory" +tags = ["devops", "infrastructure", "docker", "kubernetes", "cicd", "cloud"] + +[ci] +has_action = false diff --git a/agents/expert-coder/agent.toml b/agents/expert-coder/agent.toml new file mode 100644 index 0000000..39e7cce --- /dev/null +++ b/agents/expert-coder/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "expert-coder" +version = "1.0.0" +type = "advisory" +tags = ["coding", "architecture", "python", "best-practices"] + +[ci] +has_action = false diff --git a/agents/ml-engineer/agent.toml b/agents/ml-engineer/agent.toml new file mode 100644 index 0000000..d6d4337 --- /dev/null +++ b/agents/ml-engineer/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "ml-engineer" +version = "1.0.0" +type = "advisory" +tags = ["machine-learning", "training", "architecture", "optimization"] + +[ci] +has_action = false diff --git a/agents/test-engineer/agent.toml b/agents/test-engineer/agent.toml new file mode 100644 index 0000000..030ecbb --- /dev/null +++ b/agents/test-engineer/agent.toml @@ -0,0 +1,8 @@ +[agent] +name = "test-engineer" +version = "1.0.0" +type = "blocking" +tags = ["testing", "coverage", "ci-cd", "enforcement"] + +[ci] +has_action = true diff --git a/archetypes/cv-inference-service/archetype.toml b/archetypes/cv-inference-service/archetype.toml new file mode 100644 index 0000000..d3fadc3 --- /dev/null +++ b/archetypes/cv-inference-service/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "cv-inference-service" +version = "1.0.0" +category = "cv-ml" +tags = ["inference", "deployment", "fastapi", "onnx"] +description = "FastAPI + ONNX deployment service for computer vision models" + +[skills] +required = ["onnx", "pydantic-strict", "loguru", "docker-cv", "testing"] +recommended = ["tensorrt", "gcp", "github-actions"] diff --git a/archetypes/cv-inference-service/template/Dockerfile b/archetypes/cv-inference-service/template/Dockerfile new file mode 100644 index 0000000..c0cac9b --- /dev/null +++ b/archetypes/cv-inference-service/template/Dockerfile @@ -0,0 +1,21 @@ +FROM python:${python_version}-slim AS builder + +WORKDIR /app +RUN pip install --no-cache-dir uv + +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +FROM python:${python_version}-slim AS runtime + +WORKDIR /app +COPY --from=builder /app/.venv /app/.venv +ENV PATH="/app/.venv/bin:$$PATH" + +COPY src/ ./src/ + +RUN useradd --create-home appuser +USER appuser + +EXPOSE 8000 +CMD ["uvicorn", "${package_name}.app:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] diff --git a/archetypes/cv-inference-service/template/README.md b/archetypes/cv-inference-service/template/README.md new file mode 100644 index 0000000..9563d04 --- /dev/null +++ b/archetypes/cv-inference-service/template/README.md @@ -0,0 +1,32 @@ +# ${project_name} + +${description} + +## Setup + +```bash +uv sync --all-extras +``` + +## Running + +```bash +# Development server +uv run uvicorn ${package_name}.app:app --reload --port 8000 + +# Production +uv run uvicorn ${package_name}.app:app --host 0.0.0.0 --port 8000 --workers 4 +``` + +## API Endpoints + +- `GET /health` — Health check +- `POST /api/v1/predict` — Run inference on an image + +## Development + +```bash +uv run pytest tests/ -v +uv run ruff check . +uv run mypy src/ --strict +``` diff --git a/archetypes/cv-inference-service/template/pyproject.toml b/archetypes/cv-inference-service/template/pyproject.toml new file mode 100644 index 0000000..b0c3718 --- /dev/null +++ b/archetypes/cv-inference-service/template/pyproject.toml @@ -0,0 +1,44 @@ +[project] +name = "${project_slug}" +version = "0.1.0" +description = "${description}" +authors = [{name = "${author}"}] +requires-python = ">=${python_version}" +dependencies = [ + "fastapi>=0.100", + "uvicorn[standard]>=0.20", + "pydantic>=2.0", + "loguru>=0.7", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.4", + "pytest-cov>=4.1", + "httpx>=0.24", + "ruff>=0.8", + "mypy>=1.11", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/${package_name}"] + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "UP", "S", "B", "A", "C4", "T20", "SIM"] +ignore = ["S101"] + +[tool.mypy] +python_version = "${python_version}" +strict = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-v" diff --git a/archetypes/cv-inference-service/template/src/${package_name}/__init__.py b/archetypes/cv-inference-service/template/src/${package_name}/__init__.py new file mode 100644 index 0000000..001d01f --- /dev/null +++ b/archetypes/cv-inference-service/template/src/${package_name}/__init__.py @@ -0,0 +1,3 @@ +"""${project_name} — inference service.""" + +__version__ = "0.1.0" diff --git a/archetypes/cv-inference-service/template/src/${package_name}/app.py b/archetypes/cv-inference-service/template/src/${package_name}/app.py new file mode 100644 index 0000000..4ec5b27 --- /dev/null +++ b/archetypes/cv-inference-service/template/src/${package_name}/app.py @@ -0,0 +1,41 @@ +"""FastAPI application for ${project_name}.""" + +from __future__ import annotations + +from contextlib import asynccontextmanager +from collections.abc import AsyncGenerator + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from loguru import logger + +from .schemas import HealthResponse + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncGenerator[None]: + """Load model on startup, release on shutdown.""" + logger.info("Loading model...") + # TODO: Load your model here + yield + logger.info("Shutting down") + + +app = FastAPI( + title="${project_name}", + version="0.1.0", + lifespan=lifespan, +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/health", response_model=HealthResponse) +async def health() -> HealthResponse: + """Health check endpoint.""" + return HealthResponse(status="healthy", version="0.1.0") diff --git a/archetypes/cv-inference-service/template/src/${package_name}/schemas.py b/archetypes/cv-inference-service/template/src/${package_name}/schemas.py new file mode 100644 index 0000000..c63eb53 --- /dev/null +++ b/archetypes/cv-inference-service/template/src/${package_name}/schemas.py @@ -0,0 +1,34 @@ +"""Pydantic schemas for ${project_name}.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field + + +class HealthResponse(BaseModel, frozen=True): + """Health check response.""" + + status: str + version: str + + +class PredictionRequest(BaseModel, frozen=True): + """Prediction request.""" + + image_b64: str = Field(..., description="Base64-encoded image") + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) + + +class Detection(BaseModel, frozen=True): + """Single detection result.""" + + label: str + confidence: float = Field(ge=0.0, le=1.0) + bbox: list[float] = Field(min_length=4, max_length=4) + + +class PredictionResponse(BaseModel, frozen=True): + """Prediction response.""" + + detections: list[Detection] + inference_time_ms: float diff --git a/archetypes/cv-inference-service/template/tests/__init__.py b/archetypes/cv-inference-service/template/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/archetypes/cv-inference-service/template/tests/test_app.py b/archetypes/cv-inference-service/template/tests/test_app.py new file mode 100644 index 0000000..7683309 --- /dev/null +++ b/archetypes/cv-inference-service/template/tests/test_app.py @@ -0,0 +1,22 @@ +"""Tests for the inference service.""" + +from __future__ import annotations + +import pytest +from httpx import ASGITransport, AsyncClient + +from ${package_name}.app import app + + +@pytest.fixture +async def client() -> AsyncClient: + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + yield ac + + +@pytest.mark.anyio +async def test_health(client: AsyncClient) -> None: + response = await client.get("/health") + assert response.status_code == 200 + assert response.json()["status"] == "healthy" diff --git a/archetypes/data-processing-pipeline/archetype.toml b/archetypes/data-processing-pipeline/archetype.toml new file mode 100644 index 0000000..fdb552c --- /dev/null +++ b/archetypes/data-processing-pipeline/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "data-processing-pipeline" +version = "1.0.0" +category = "cv-ml" +tags = ["data", "etl", "pipeline", "processing"] +description = "ETL workflows for dataset processing and preparation" + +[skills] +required = ["pydantic-strict", "loguru", "testing", "dvc"] +recommended = ["docker-cv", "gcp", "github-actions"] diff --git a/archetypes/library-package/archetype.toml b/archetypes/library-package/archetype.toml new file mode 100644 index 0000000..fe263e6 --- /dev/null +++ b/archetypes/library-package/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "library-package" +version = "1.0.0" +category = "core" +tags = ["package", "library", "pypi", "reusable"] +description = "Reusable Python package for PyPI distribution" + +[skills] +required = ["pydantic-strict", "code-quality", "testing", "pypi", "loguru"] +recommended = ["github-actions", "pre-commit"] diff --git a/archetypes/model-zoo/archetype.toml b/archetypes/model-zoo/archetype.toml new file mode 100644 index 0000000..37ded93 --- /dev/null +++ b/archetypes/model-zoo/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "model-zoo" +version = "1.0.0" +category = "cv-ml" +tags = ["models", "pretrained", "collection", "benchmarks"] +description = "Collection of pretrained models with benchmarking and evaluation" + +[skills] +required = ["pytorch-lightning", "onnx", "pydantic-strict", "loguru", "testing"] +recommended = ["dvc", "wandb", "docker-cv"] diff --git a/archetypes/pytorch-training-project/archetype.toml b/archetypes/pytorch-training-project/archetype.toml new file mode 100644 index 0000000..ab711e9 --- /dev/null +++ b/archetypes/pytorch-training-project/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "pytorch-training-project" +version = "1.0.0" +category = "cv-ml" +tags = ["training", "pytorch", "lightning", "deep-learning"] +description = "Model training with PyTorch Lightning, Hydra config, and experiment tracking" + +[skills] +required = ["pytorch-lightning", "hydra-config", "pydantic-strict", "loguru", "testing"] +recommended = ["wandb", "docker-cv", "github-actions"] diff --git a/archetypes/pytorch-training-project/template/README.md b/archetypes/pytorch-training-project/template/README.md new file mode 100644 index 0000000..e254930 --- /dev/null +++ b/archetypes/pytorch-training-project/template/README.md @@ -0,0 +1,51 @@ +# ${project_name} + +${description} + +## Setup + +```bash +uv sync --all-extras +``` + +## Training + +```bash +# Default training +uv run python -m ${package_name}.train + +# With overrides +uv run python -m ${package_name}.train model=resnet50 trainer.max_epochs=50 + +# Debug run (1 batch) +uv run python -m ${package_name}.train trainer=debug +``` + +## Development + +```bash +uv run pytest tests/ -v +uv run ruff check . +uv run ruff format . +uv run mypy src/ --strict +``` + +## Project Structure + +``` +${project_slug}/ +├── src/${package_name}/ +│ ├── __init__.py +│ ├── model.py # LightningModule +│ ├── data.py # LightningDataModule +│ ├── train.py # Training entry point +│ └── transforms.py # Data augmentations +├── configs/ +│ ├── config.yaml # Main Hydra config +│ ├── model/ +│ ├── data/ +│ └── trainer/ +├── tests/ +├── pyproject.toml +└── README.md +``` diff --git a/archetypes/pytorch-training-project/template/configs/config.yaml b/archetypes/pytorch-training-project/template/configs/config.yaml new file mode 100644 index 0000000..aeb06ba --- /dev/null +++ b/archetypes/pytorch-training-project/template/configs/config.yaml @@ -0,0 +1,6 @@ +defaults: + - model: default + - data: default + - trainer: default + +seed: 42 diff --git a/archetypes/pytorch-training-project/template/configs/model/default.yaml b/archetypes/pytorch-training-project/template/configs/model/default.yaml new file mode 100644 index 0000000..6075077 --- /dev/null +++ b/archetypes/pytorch-training-project/template/configs/model/default.yaml @@ -0,0 +1,4 @@ +num_classes: 10 +learning_rate: 1e-3 +backbone: resnet18 +pretrained: true diff --git a/archetypes/pytorch-training-project/template/configs/trainer/default.yaml b/archetypes/pytorch-training-project/template/configs/trainer/default.yaml new file mode 100644 index 0000000..5bcc510 --- /dev/null +++ b/archetypes/pytorch-training-project/template/configs/trainer/default.yaml @@ -0,0 +1,4 @@ +max_epochs: 50 +accelerator: auto +precision: 16-mixed +log_every_n_steps: 10 diff --git a/archetypes/pytorch-training-project/template/pyproject.toml b/archetypes/pytorch-training-project/template/pyproject.toml new file mode 100644 index 0000000..b0b35d6 --- /dev/null +++ b/archetypes/pytorch-training-project/template/pyproject.toml @@ -0,0 +1,40 @@ +[project] +name = "${project_slug}" +version = "0.1.0" +description = "${description}" +authors = [{name = "${author}"}] +requires-python = ">=${python_version}" + +[project.optional-dependencies] +dev = [ + "pytest>=7.4", + "pytest-cov>=4.1", + "ruff>=0.8", + "mypy>=1.11", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/${package_name}"] + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "UP", "S", "B", "A", "C4", "T20", "SIM"] +ignore = ["S101"] + +[tool.ruff.lint.per-file-ignores] +"tests/**/*.py" = ["S101", "T201"] + +[tool.mypy] +python_version = "${python_version}" +strict = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-v" diff --git a/archetypes/pytorch-training-project/template/src/${package_name}/__init__.py b/archetypes/pytorch-training-project/template/src/${package_name}/__init__.py new file mode 100644 index 0000000..0f3e37b --- /dev/null +++ b/archetypes/pytorch-training-project/template/src/${package_name}/__init__.py @@ -0,0 +1,3 @@ +"""${project_name} — training pipeline.""" + +__version__ = "0.1.0" diff --git a/archetypes/pytorch-training-project/template/src/${package_name}/data.py b/archetypes/pytorch-training-project/template/src/${package_name}/data.py new file mode 100644 index 0000000..ce0822b --- /dev/null +++ b/archetypes/pytorch-training-project/template/src/${package_name}/data.py @@ -0,0 +1,74 @@ +"""LightningDataModule for ${project_name}.""" + +from __future__ import annotations + +from pathlib import Path + +import lightning as L +from pydantic import BaseModel, Field +from torch.utils.data import DataLoader + + +class DataConfig(BaseModel, frozen=True): + """Data configuration.""" + + data_dir: str = "data" + batch_size: int = 32 + num_workers: int = 4 + num_classes: int = 10 + image_size: int = 224 + + +class ImageDataModule(L.LightningDataModule): + """Image dataset module. + + Args: + config: Data configuration. + """ + + def __init__(self, config: DataConfig) -> None: + super().__init__() + self.save_hyperparameters() + self.config = config + + def setup(self, stage: str | None = None) -> None: + """Set up datasets for each stage.""" + # Replace with your dataset loading logic + from torchvision import datasets, transforms + + transform = transforms.Compose([ + transforms.Resize((self.config.image_size, self.config.image_size)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + ), + ]) + + if stage == "fit" or stage is None: + self.train_dataset = datasets.FakeData( + size=100, image_size=(3, self.config.image_size, self.config.image_size), + num_classes=self.config.num_classes, transform=transform, + ) + self.val_dataset = datasets.FakeData( + size=20, image_size=(3, self.config.image_size, self.config.image_size), + num_classes=self.config.num_classes, transform=transform, + ) + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.config.batch_size, + shuffle=True, + num_workers=self.config.num_workers, + pin_memory=True, + ) + + def val_dataloader(self) -> DataLoader: + return DataLoader( + self.val_dataset, + batch_size=self.config.batch_size, + shuffle=False, + num_workers=self.config.num_workers, + pin_memory=True, + ) diff --git a/archetypes/pytorch-training-project/template/src/${package_name}/model.py b/archetypes/pytorch-training-project/template/src/${package_name}/model.py new file mode 100644 index 0000000..f058870 --- /dev/null +++ b/archetypes/pytorch-training-project/template/src/${package_name}/model.py @@ -0,0 +1,82 @@ +"""LightningModule for ${project_name}.""" + +from __future__ import annotations + +from typing import Any + +import lightning as L +import torch +import torch.nn as nn +import torchmetrics +from pydantic import BaseModel, Field +from torch import Tensor + + +class ModelConfig(BaseModel, frozen=True): + """Model configuration.""" + + num_classes: int = 10 + learning_rate: float = 1e-3 + backbone: str = "resnet18" + pretrained: bool = True + + +class Classifier(L.LightningModule): + """Image classification model. + + Args: + config: Model configuration. + """ + + def __init__(self, config: ModelConfig) -> None: + super().__init__() + self.save_hyperparameters() + self.config = config + + self.backbone = self._build_backbone() + self.head = nn.LazyLinear(config.num_classes) + self.criterion = nn.CrossEntropyLoss() + self.train_acc = torchmetrics.Accuracy( + task="multiclass", num_classes=config.num_classes, + ) + self.val_acc = torchmetrics.Accuracy( + task="multiclass", num_classes=config.num_classes, + ) + + def _build_backbone(self) -> nn.Module: + """Build the backbone network.""" + import torchvision.models as models + + weights = "DEFAULT" if self.config.pretrained else None + model = getattr(models, self.config.backbone)(weights=weights) + # Remove the classification head + model.fc = nn.Identity() + return model + + def forward(self, x: Tensor) -> Tensor: + features = self.backbone(x) + return self.head(features) + + def training_step(self, batch: dict[str, Tensor], batch_idx: int) -> Tensor: + logits = self(batch["image"]) + loss = self.criterion(logits, batch["label"]) + self.train_acc(logits, batch["label"]) + self.log("train/loss", loss, prog_bar=True) + self.log("train/acc", self.train_acc, on_step=False, on_epoch=True) + return loss + + def validation_step(self, batch: dict[str, Tensor], batch_idx: int) -> None: + logits = self(batch["image"]) + loss = self.criterion(logits, batch["label"]) + self.val_acc(logits, batch["label"]) + self.log("val/loss", loss, prog_bar=True) + self.log("val/acc", self.val_acc, on_step=False, on_epoch=True) + + def configure_optimizers(self) -> dict[str, Any]: + optimizer = torch.optim.AdamW( + self.parameters(), lr=self.config.learning_rate, + ) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=self.trainer.max_epochs, + ) + return {"optimizer": optimizer, "lr_scheduler": scheduler} diff --git a/archetypes/pytorch-training-project/template/src/${package_name}/train.py b/archetypes/pytorch-training-project/template/src/${package_name}/train.py new file mode 100644 index 0000000..8b9e386 --- /dev/null +++ b/archetypes/pytorch-training-project/template/src/${package_name}/train.py @@ -0,0 +1,34 @@ +"""Training entry point for ${project_name}.""" + +from __future__ import annotations + +import lightning as L +from loguru import logger + + +def main() -> None: + """Run training.""" + from .data import DataConfig, ImageDataModule + from .model import Classifier, ModelConfig + + model_config = ModelConfig() + data_config = DataConfig() + + logger.info("Starting training: {}", model_config.backbone) + + model = Classifier(config=model_config) + datamodule = ImageDataModule(config=data_config) + + trainer = L.Trainer( + max_epochs=10, + accelerator="auto", + precision="16-mixed", + log_every_n_steps=10, + ) + + trainer.fit(model, datamodule=datamodule) + logger.info("Training complete") + + +if __name__ == "__main__": + main() diff --git a/archetypes/pytorch-training-project/template/tests/__init__.py b/archetypes/pytorch-training-project/template/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/archetypes/pytorch-training-project/template/tests/test_model.py b/archetypes/pytorch-training-project/template/tests/test_model.py new file mode 100644 index 0000000..693a2fa --- /dev/null +++ b/archetypes/pytorch-training-project/template/tests/test_model.py @@ -0,0 +1,18 @@ +"""Tests for the model module.""" + +from __future__ import annotations + +import torch + + +def test_model_forward_shape() -> None: + """Test that the model produces correct output shape.""" + from ${package_name}.model import Classifier, ModelConfig + + config = ModelConfig(num_classes=10, pretrained=False) + model = Classifier(config=config) + model.eval() + + batch = torch.randn(2, 3, 224, 224) + output = model(batch) + assert output.shape == (2, 10) diff --git a/archetypes/research-notebook/archetype.toml b/archetypes/research-notebook/archetype.toml new file mode 100644 index 0000000..f2a6263 --- /dev/null +++ b/archetypes/research-notebook/archetype.toml @@ -0,0 +1,10 @@ +[archetype] +name = "research-notebook" +version = "1.0.0" +category = "cv-ml" +tags = ["research", "jupyter", "experimentation", "prototyping"] +description = "Jupyter-based experimentation environment for ML research" + +[skills] +required = ["pytorch-lightning", "matplotlib", "loguru"] +recommended = ["hydra-config", "wandb", "opencv"] diff --git a/docs/agents/code-review.md b/docs/agents/code-review.md index 00d5256..4ab3a12 100644 --- a/docs/agents/code-review.md +++ b/docs/agents/code-review.md @@ -33,9 +33,9 @@ jobs: - uses: prefix-dev/setup-pixi@v0.8.1 with: pixi-version: latest - - run: pixi run ruff format --check . - - run: pixi run ruff check . - - run: pixi run mypy src/ + - run: uv run ruff format --check . + - run: uv run ruff check . + - run: uv run mypy src/ ``` ## Local Development @@ -44,13 +44,13 @@ Run checks locally before pushing to avoid CI failures: ```bash # Run all checks -pixi run lint +uv run lint # Auto-fix formatting -pixi run format +uv run format # Type check -pixi run typecheck +uv run typecheck ``` ## Pre-commit Integration @@ -68,7 +68,7 @@ Ruff lint................................Passed | Failure | Fix | |---------|-----| | `F401: imported but unused` | Remove unused import | -| `I001: import not sorted` | Run `pixi run format` | +| `I001: import not sorted` | Run `uv run format` | | `S101: assert used` | Only use assert in test files | | `T201: print found` | Use `logging` instead of `print` | | `mypy: Missing type hints` | Add type annotations to all functions | diff --git a/docs/agents/data-engineer.md b/docs/agents/data-engineer.md new file mode 100644 index 0000000..56cad7e --- /dev/null +++ b/docs/agents/data-engineer.md @@ -0,0 +1,45 @@ +# Data Engineer Agent + +The Data Engineer Agent guides data pipeline architecture, data quality, and storage decisions for ML/CV projects. + +**Agent directory:** `agents/data-engineer/` + +## Purpose + +This agent provides expert advice on building data pipelines for ML projects. It covers ETL/ELT patterns, data validation with Pydantic, dataset versioning with DVC, storage format selection, data augmentation pipeline design, and data quality monitoring. + +## Strictness Level + +**ADVISORY** — This agent guides decisions but does not block. + +## When to Use + +- Designing data pipelines for training data preparation +- Choosing storage formats (Parquet, Arrow, LMDB, TFRecord) +- Implementing data validation and quality checks +- Planning dataset versioning and lineage tracking +- Designing data augmentation strategies +- Handling schema evolution and migration + +## Example Session + +``` +You: "I need to build a data pipeline for image classification with 100K images" + +Data Engineer: "I recommend this approach: +1. Store raw images in S3/GCS, metadata in Parquet +2. DVC for dataset versioning with remote storage +3. Pydantic models for metadata validation +4. WebDataset or LMDB for training-time access +5. Great Expectations for automated quality checks" +``` + +## Related Skills + +- `dvc` — Data versioning patterns +- `pydantic-strict` — Data validation models +- `testing` — Data pipeline testing strategies + +## Full Reference + +See [`agents/data-engineer/SKILL.md`](https://github.com/ortizeg/whet/blob/main/agents/data-engineer/SKILL.md) for complete patterns. diff --git a/docs/agents/devops-infra.md b/docs/agents/devops-infra.md new file mode 100644 index 0000000..e4e0c37 --- /dev/null +++ b/docs/agents/devops-infra.md @@ -0,0 +1,95 @@ +# DevOps/Infrastructure Agent + +The DevOps/Infrastructure Agent guides all infrastructure and deployment decisions for ML/CV projects. + +**Agent directory:** `agents/devops-infra/` + +## Purpose + +This agent provides expert advice on packaging, deploying, and operating ML services in production. It covers Docker multi-stage builds, Kubernetes deployments with GPU support, CI/CD pipeline design, monitoring and observability, infrastructure as code, and security hardening. + +## Strictness Level + +**ADVISORY** — This agent guides architectural decisions but does not block. + +## When to Use + +- Containerizing an ML model for deployment +- Choosing between deployment targets (Cloud Run, ECS, Kubernetes) +- Designing CI/CD pipelines for ML projects +- Setting up monitoring and alerting for model serving +- Writing infrastructure as code (Terraform, Kubernetes manifests) +- Reviewing security posture of ML infrastructure + +## Decision Framework + +The agent uses a structured decision tree: + +``` +Need to package an ML application? +├── Single model serving → Dockerfile with multi-stage build +├── Multiple services → Docker Compose (local), K8s (prod) +├── GPU required? +│ ├── Training → NVIDIA base images +│ └── Inference → Optimized runtime images +└── No GPU → Python slim base image +``` + +## Key Patterns + +### Multi-Stage Docker Build + +```dockerfile +FROM python:3.11-slim AS builder +WORKDIR /app +RUN pip install --no-cache-dir uv +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +FROM python:3.11-slim AS runtime +WORKDIR /app +COPY --from=builder /app/.venv /app/.venv +COPY src/ ./src/ +RUN useradd --create-home appuser +USER appuser +CMD ["uvicorn", "src.app:app", "--host", "0.0.0.0", "--port", "8000"] +``` + +### Kubernetes Deployment with GPU + +```yaml +spec: + containers: + - name: api + resources: + requests: + nvidia.com/gpu: "1" + limits: + nvidia.com/gpu: "1" + livenessProbe: + httpGet: + path: /health + readinessProbe: + httpGet: + path: /ready +``` + +## Security Checklist + +1. No secrets in code or images +2. Non-root container user +3. Minimal base images, scanned with Trivy +4. Resource limits on all containers +5. Image version pinning with content hashes + +## Related Skills + +- `docker-cv` — Detailed Docker patterns for CV workloads +- `github-actions` — CI/CD workflow definitions +- `gcp` — Google Cloud Platform deployment patterns +- `aws-sagemaker` — AWS managed ML infrastructure +- `fastapi` — The application framework behind deployed services + +## Full Reference + +See [`agents/devops-infra/SKILL.md`](https://github.com/ortizeg/whet/blob/main/agents/devops-infra/SKILL.md) for complete patterns including Docker Compose, Kubernetes HPA, Terraform, and monitoring. diff --git a/docs/agents/index.md b/docs/agents/index.md index 99ff26f..7cfbba4 100644 --- a/docs/agents/index.md +++ b/docs/agents/index.md @@ -1,6 +1,6 @@ # Agents Overview -The ai-cv-claude-skills framework includes four specialized agents that assist with different aspects of ML/CV development. Agents are divided into two categories based on their enforcement level. +The whet framework includes six specialized agents that assist with different aspects of ML/CV development. Agents are divided into two categories based on their enforcement level. ## Agent Summary @@ -8,12 +8,14 @@ The ai-cv-claude-skills framework includes four specialized agents that assist w |-------|------|------------|------------| | [Expert Coder](expert-coder.md) | Primary coding assistant | Advisory | Claude Code | | [ML Engineer](ml-engineer.md) | Architecture and training guidance | Advisory | Claude Code | +| [DevOps/Infra](devops-infra.md) | Infrastructure and deployment decisions | Advisory | Claude Code | +| [Data Engineer](data-engineer.md) | Data pipeline architecture and quality | Advisory | Claude Code | | [Code Review](code-review.md) | Automated quality enforcement | **Blocking** | GitHub Action | | [Test Engineer](test-engineer.md) | Testing and coverage enforcement | **Blocking** | GitHub Action | ## Advisory vs Blocking -**Advisory agents** guide development but do not prevent actions. They suggest improvements, explain rationale, and generate code following standards. The Expert Coder and ML Engineer agents operate in this mode through Claude Code. +**Advisory agents** guide development but do not prevent actions. They suggest improvements, explain rationale, and generate code following standards. The Expert Coder, ML Engineer, DevOps/Infra, and Data Engineer agents operate in this mode through Claude Code. **Blocking agents** run in CI and must pass before a pull request can be merged. The Code Review and Test Engineer agents operate as GitHub Actions that gate merges on code quality, type safety, test coverage, and security checks. @@ -21,7 +23,9 @@ The ai-cv-claude-skills framework includes four specialized agents that assist w 1. **During development**, the Expert Coder generates code following project patterns (abstractions, Pydantic, type hints). 2. **For ML decisions**, the ML Engineer reviews model architecture, training setup, and experiment configuration. -3. **On push/PR**, the Code Review agent validates formatting, linting, types, and security. -4. **On push/PR**, the Test Engineer runs the full test suite and enforces coverage thresholds. +3. **For infrastructure**, the DevOps/Infra agent guides Docker, Kubernetes, CI/CD, and cloud deployment decisions. +4. **For data pipelines**, the Data Engineer agent advises on ETL patterns, validation, versioning, and storage format selection. +5. **On push/PR**, the Code Review agent validates formatting, linting, types, and security. +6. **On push/PR**, the Test Engineer runs the full test suite and enforces coverage thresholds. This layered approach provides guidance during development and enforcement during integration. diff --git a/docs/agents/test-engineer.md b/docs/agents/test-engineer.md index 491f6b6..4fbd9fe 100644 --- a/docs/agents/test-engineer.md +++ b/docs/agents/test-engineer.md @@ -90,23 +90,23 @@ jobs: - uses: prefix-dev/setup-pixi@v0.8.1 with: pixi-version: latest - - run: pixi run pytest --cov=src --cov-fail-under=80 + - run: uv run pytest --cov=src --cov-fail-under=80 ``` ## Local Usage ```bash # Run all tests -pixi run test +uv run test # Run with coverage report -pixi run test-cov +uv run test-cov # Run specific test file -pixi run pytest tests/unit/test_models.py -v +uv run pytest tests/unit/test_models.py -v # Run tests matching pattern -pixi run pytest -k "test_model" +uv run pytest -k "test_model" ``` ## Additional Requirements diff --git a/docs/archetypes/cv-inference-service.md b/docs/archetypes/cv-inference-service.md index 88375d2..9201a36 100644 --- a/docs/archetypes/cv-inference-service.md +++ b/docs/archetypes/cv-inference-service.md @@ -47,7 +47,7 @@ This archetype packages trained models into production-ready REST API services. ```bash # Run locally -pixi run uvicorn my_project.serve:app --reload +uv run uvicorn my_project.serve:app --reload # Build and run with Docker docker compose up inference diff --git a/docs/archetypes/data-processing-pipeline.md b/docs/archetypes/data-processing-pipeline.md index 2e1b129..4cf0b5b 100644 --- a/docs/archetypes/data-processing-pipeline.md +++ b/docs/archetypes/data-processing-pipeline.md @@ -65,10 +65,10 @@ class Stage(ABC): ```bash # Run full pipeline -pixi run python -m my_project.pipeline +uv run python -m my_project.pipeline # Run single stage -pixi run python -m my_project.pipeline stage=preprocess +uv run python -m my_project.pipeline stage=preprocess # Reproduce with DVC dvc repro diff --git a/docs/archetypes/library-package.md b/docs/archetypes/library-package.md index 485222b..e2bebf8 100644 --- a/docs/archetypes/library-package.md +++ b/docs/archetypes/library-package.md @@ -48,13 +48,13 @@ This archetype creates a well-structured Python package that can be published to ```bash # Install in development mode -pixi run pip install -e ".[dev]" +uv run pip install -e ".[dev]" # Run tests -pixi run test +uv run test # Build package -pixi run python -m build +uv run python -m build # Publish to PyPI (via GitHub Actions on tag) git tag v0.1.0 && git push --tags diff --git a/docs/archetypes/model-zoo.md b/docs/archetypes/model-zoo.md index 22ed9da..386a995 100644 --- a/docs/archetypes/model-zoo.md +++ b/docs/archetypes/model-zoo.md @@ -70,13 +70,13 @@ Each model includes a `MODEL_CARD.md` documenting: ```bash # Run benchmarks -pixi run python benchmarks/run_benchmarks.py +uv run python benchmarks/run_benchmarks.py # Download all model weights -pixi run python -m my_project.download --all +uv run python -m my_project.download --all # Compare models -pixi run python -m my_project.benchmark --models resnet50,efficientnet_b0 +uv run python -m my_project.benchmark --models resnet50,efficientnet_b0 ``` ## Customization diff --git a/docs/archetypes/pytorch-training-project.md b/docs/archetypes/pytorch-training-project.md index 1584a06..d249348 100644 --- a/docs/archetypes/pytorch-training-project.md +++ b/docs/archetypes/pytorch-training-project.md @@ -52,13 +52,13 @@ This archetype provides a complete training pipeline structure for computer visi ```bash # Train with default config -pixi run python -m my_project.train +uv run python -m my_project.train # Override parameters -pixi run python -m my_project.train model=efficientnet trainer.max_epochs=50 +uv run python -m my_project.train model=efficientnet trainer.max_epochs=50 # Debug mode (1 batch, no logging) -pixi run python -m my_project.train trainer=debug +uv run python -m my_project.train trainer=debug ``` ## Customization diff --git a/docs/archetypes/research-notebook.md b/docs/archetypes/research-notebook.md index 96b75de..4570085 100644 --- a/docs/archetypes/research-notebook.md +++ b/docs/archetypes/research-notebook.md @@ -45,13 +45,13 @@ This archetype provides a structured notebook environment for ML research and ex ```bash # Start Jupyter -pixi run jupyter lab +uv run jupyter lab # Convert notebook to script -pixi run jupyter nbconvert --to script notebooks/01_data_exploration.ipynb +uv run jupyter nbconvert --to script notebooks/01_data_exploration.ipynb # Run all notebooks headless (for CI) -pixi run pytest --nbmake notebooks/ +uv run pytest --nbmake notebooks/ ``` ## Customization diff --git a/docs/examples/full-workflow.md b/docs/examples/full-workflow.md index 23d01ec..6ef8f7a 100644 --- a/docs/examples/full-workflow.md +++ b/docs/examples/full-workflow.md @@ -192,13 +192,13 @@ dropout: 0.1 ```bash # Default training -pixi run python -m yolo_detector.train +uv run python -m yolo_detector.train # Override config -pixi run python -m yolo_detector.train model.backbone=efficientnet_b0 trainer.max_epochs=50 +uv run python -m yolo_detector.train model.backbone=efficientnet_b0 trainer.max_epochs=50 # Debug mode -pixi run python -m yolo_detector.train trainer.fast_dev_run=true +uv run python -m yolo_detector.train trainer.fast_dev_run=true ``` ## Step 7: Export to ONNX diff --git a/docs/getting-started/first-project.md b/docs/getting-started/first-project.md index 01b0296..57e0f89 100644 --- a/docs/getting-started/first-project.md +++ b/docs/getting-started/first-project.md @@ -190,13 +190,13 @@ object-detector/ ```bash # Local training -pixi run python src/object_detector/train.py experiment=small +uv run python src/object_detector/train.py experiment=small # Docker training docker compose up train # Multi-GPU training -pixi run python src/object_detector/train.py trainer.devices=4 trainer.strategy=ddp +uv run python src/object_detector/train.py trainer.devices=4 trainer.strategy=ddp ``` ## Key Takeaways diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 1449175..c1fc874 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,122 +1,138 @@ # Installation -This guide walks you through setting up the AI/CV Claude Skills framework on your machine. +This guide walks you through setting up whet and the AI/CV skills framework. -## Prerequisites +## Quick Install -Before you begin, ensure you have the following installed: +The fastest way to use whet is via `uvx` (no install needed): -| Tool | Version | Purpose | -|------|---------|---------| -| [Claude Code](https://docs.anthropic.com/en/docs/claude-code) | Latest | The CLI that loads and uses skills | -| [Pixi](https://pixi.sh/) | >= 0.30 | Package and environment management | -| [Git](https://git-scm.com/) | >= 2.30 | Version control | -| Python | >= 3.10 | Runtime (managed by Pixi) | +```bash +# List available skills +uvx whet list -### Installing Claude Code +# Install skills to Claude Code +uvx whet install --global +``` -Claude Code is Anthropic's official CLI for Claude. Install it via npm: +Or install permanently: ```bash -npm install -g @anthropic-ai/claude-code +uv tool install whet ``` -Verify the installation: +## Prerequisites -```bash -claude --version -``` +| Tool | Version | Purpose | +|------|---------|---------| +| [uv](https://docs.astral.sh/uv/) | Latest | Python package management | +| [Claude Code](https://docs.anthropic.com/en/docs/claude-code) | Latest | The CLI that loads and uses skills | +| [Git](https://git-scm.com/) | >= 2.30 | Version control | +| Python | >= 3.11 | Runtime (managed by uv) | -### Installing Pixi +### Installing uv -Pixi is the recommended package manager for this framework. It handles Python versions, CUDA dependencies, and reproducible environments. +uv is the recommended package manager for whet. It handles Python versions, dependencies, and tool installation. ```bash # macOS / Linux -curl -fsSL https://pixi.sh/install.sh | bash +curl -LsSf https://astral.sh/uv/install.sh | sh # Windows (PowerShell) -iwr -useb https://pixi.sh/install.ps1 | iex +powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` Verify: ```bash -pixi --version +uv --version ``` -## Cloning the Repository +### Installing Claude Code + +Claude Code is Anthropic's official CLI for Claude. Install it via npm: ```bash -git clone https://github.com/ortizeg/ai-cv-claude-skills.git -cd ai-cv-claude-skills +npm install -g @anthropic-ai/claude-code ``` -## Repository Layout - -After cloning, you will see this structure: +Verify: -``` -ai-cv-claude-skills/ - skills/ # 21 skill modules (each with SKILL.md, README.md) - agents/ # 4 agent persona definitions - archetypes/ # 6 project archetype templates - docs/ # Documentation (you are reading it) - tests/ # Validation tests for skills - mkdocs.yml # Documentation site configuration +```bash +claude --version ``` -## Configuring Claude Code +## Using whet -The skills framework works by providing Claude Code with context files. There are two ways to use it: +### Install Skills Globally -### Option 1: Reference Skills Directly - -Point Claude Code at skill files when starting a session: +Install all skills to your AI coding agent: ```bash -# Load a specific skill -claude --skill ./skills/pytorch-lightning/SKILL.md +# Target Claude Code (default) +whet install --global + +# Target Google Antigravity +whet target antigravity +whet install --global -# Load multiple skills -claude --skill ./skills/pytorch-lightning/SKILL.md \ - --skill ./skills/hydra-config/SKILL.md \ - --skill ./skills/wandb/SKILL.md +# Target Cursor +whet target cursor +whet install --global ``` -### Option 2: Copy Skills Into Your Project +### Add Skills to a Project -For persistent use in a project, copy the relevant skill files into your project's `.claude/` directory: +Install specific skills into your current project: ```bash -# Create the Claude configuration directory in your project -mkdir -p /path/to/your/project/.claude/skills +whet add pytorch-lightning wandb hydra-config +``` -# Copy the skills you need -cp skills/pytorch-lightning/SKILL.md /path/to/your/project/.claude/skills/pytorch-lightning.md -cp skills/hydra-config/SKILL.md /path/to/your/project/.claude/skills/hydra-config.md +### Browse Skills + +```bash +# List all skills +whet list + +# Search by keyword +whet search training + +# Show skill details +whet info pytorch-lightning ``` -### Option 3: Symlink the Skills Directory +## Development Setup -For development workflows where you want the latest skill updates: +If you plan to contribute to whet or run the tests: ```bash -ln -s /path/to/ai-cv-claude-skills/skills /path/to/your/project/.claude/skills +git clone https://github.com/ortizeg/whet.git +cd whet +uv sync --all-extras ``` -## Setting Up a Development Environment - -If you plan to contribute to the framework itself or run the tests: +### Running Checks ```bash -cd ai-cv-claude-skills +# Run all tests +just test + +# Lint +just lint + +# Type check +just typecheck -# Create environment with Pixi -pixi install +# Run everything +just check +``` + +Or directly with uv: -# Run the test suite -pixi run pytest tests/ +```bash +uv run pytest tests/ -v +uv run ruff check . +uv run mypy src/whet/ tests/ --strict ``` ## Verifying the Installation @@ -124,17 +140,26 @@ pixi run pytest tests/ Run the completeness test to confirm all skills are properly structured: ```bash -pixi run pytest tests/test_skills_completeness.py -v +uv run pytest tests/test_skills_completeness.py -v ``` This test verifies that: -- All 21 expected skills exist -- Each skill has both a `SKILL.md` and `README.md` +- All skills have YAML frontmatter in SKILL.md +- Each skill has `SKILL.md`, `README.md`, and `skill.toml` - `SKILL.md` files contain substantial content (500+ characters) - `SKILL.md` files include code examples and headers - `README.md` files explain the skill's purpose +## Supported Platforms + +| Platform | Skill Format | Global Directory | +|----------|-------------|-----------------| +| **Claude Code** | SKILL.md (native) | `~/.claude/skills/` | +| **Google Antigravity** | SKILL.md (native) | `~/.gemini/antigravity/skills/` | +| **Cursor** | .md rules (converted) | `~/.cursor/rules/` | +| **GitHub Copilot** | Single aggregated file | `~/.github/` | + ## Next Steps -With the framework installed, proceed to the [Quick Start](quick-start.md) guide to create your first AI-assisted CV project. +With whet installed, proceed to the [Quick Start](quick-start.md) guide to create your first AI-assisted CV project. diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md index 60f1c18..d57a2af 100644 --- a/docs/getting-started/quick-start.md +++ b/docs/getting-started/quick-start.md @@ -10,21 +10,23 @@ Browse the [Skills Overview](../skills/index.md) to find skills relevant to your - **Hydra Config** -- Configuration management - **Code Quality** -- Linting, formatting, type checking -## Step 2: Start Claude Code with Skills +## Step 2: Install Skills -Open your terminal in the project directory where you want to generate code: +Install the skills you need into your project: ```bash mkdir my-classifier && cd my-classifier + +# Install skills with whet +whet add pytorch-lightning hydra-config code-quality ``` -Start Claude Code and reference the skills: +Start Claude Code — the skills are automatically discovered: ```bash -claude "You are an ML engineer. Use the pytorch-lightning, hydra-config, and - code-quality skills from ai-cv-claude-skills to create a ResNet image - classifier training pipeline. Include data loading, augmentations, - training configuration, and evaluation." +claude "Create a ResNet image classifier training pipeline. + Include data loading, augmentations, training configuration, + and evaluation." ``` ## Step 3: Review the Generated Structure @@ -52,7 +54,7 @@ my-classifier/ test_model.py test_data.py pyproject.toml - pixi.toml + pyproject.toml ``` ## Step 4: Configure and Train @@ -72,7 +74,7 @@ data: Run training: ```bash -pixi run python src/my_classifier/train.py data=custom trainer.max_epochs=50 +uv run python src/my_classifier/train.py data=custom trainer.max_epochs=50 ``` ## Step 5: Add More Skills @@ -81,18 +83,20 @@ As your project grows, layer on additional skills: ```bash # Add experiment tracking -claude "Add Weights & Biases integration to this project using the wandb skill. - Log metrics, hyperparameters, and model checkpoints." +whet add wandb # Add Docker support -claude "Containerize this project for GPU training using the docker-cv skill. - Include multi-stage build and CUDA support." +whet add docker-cv + +# Add model serving +whet add fastapi # Add CI/CD -claude "Set up GitHub Actions for this project using the github-actions skill. - Include linting, testing, and model training validation." +whet add github-actions ``` +Each skill is automatically discovered by Claude Code and used to guide code generation. + ## Common Skill Combinations Here are proven combinations for common project types: @@ -104,7 +108,7 @@ pytorch-lightning + hydra-config + wandb + code-quality + testing ### Inference Service ``` -docker-cv + onnx + pydantic-strict + code-quality + testing +fastapi + onnx + docker-cv + pydantic-strict + code-quality + testing ``` ### Research Project diff --git a/docs/guides/adding-skills.md b/docs/guides/adding-skills.md index 27a3a23..9b24a96 100644 --- a/docs/guides/adding-skills.md +++ b/docs/guides/adding-skills.md @@ -1,6 +1,6 @@ # Adding Skills -This guide explains how to contribute new skills to the ai-cv-claude-skills framework. +This guide explains how to contribute new skills to the whet framework. ## Skill Anatomy @@ -127,10 +127,10 @@ nav: ```bash # Run tests to confirm skill is complete -pixi run pytest tests/test_skills_completeness.py -v +uv run pytest tests/test_skills_completeness.py -v # Check documentation builds -pixi run docs-build +uv run docs-build ``` ## Checklist Before Submitting PR @@ -141,8 +141,8 @@ pixi run docs-build - [ ] Skill added to expected set in `test_skills_completeness.py` - [ ] Documentation page created in `docs/skills/` - [ ] `mkdocs.yml` nav updated -- [ ] All tests pass: `pixi run test` -- [ ] Linting passes: `pixi run lint` +- [ ] All tests pass: `uv run test` +- [ ] Linting passes: `uv run lint` ## Example: Adding an "Albumentations" Skill diff --git a/docs/guides/best-practices.md b/docs/guides/best-practices.md index 6bc8906..b4c8aac 100644 --- a/docs/guides/best-practices.md +++ b/docs/guides/best-practices.md @@ -1,6 +1,6 @@ # Best Practices -Comprehensive best practices for AI/CV projects using the ai-cv-claude-skills framework. +Comprehensive best practices for AI/CV projects using the whet framework. ## Project Organization diff --git a/docs/guides/creating-projects.md b/docs/guides/creating-projects.md index ac7079b..11c7ae9 100644 --- a/docs/guides/creating-projects.md +++ b/docs/guides/creating-projects.md @@ -1,6 +1,6 @@ # Creating Projects -This guide walks through creating a new AI/CV project using the ai-cv-claude-skills framework. +This guide walks through creating a new AI/CV project using the whet framework. ## Prerequisites @@ -17,7 +17,7 @@ claude ## Step 2: Tell Claude What You Want ``` -You: "Using ai-cv-claude-skills, create a new pytorch-training-project +You: "Using whet, create a new pytorch-training-project called 'face-detection' for detecting faces in video streams" ``` @@ -105,11 +105,11 @@ git commit -m "Initial project from pytorch-training-project archetype" pixi install # Install pre-commit hooks -pixi run pre-commit install +uv run pre-commit install # Verify everything works -pixi run lint -pixi run test +uv run lint +uv run test ``` ## Full Example diff --git a/docs/guides/customization.md b/docs/guides/customization.md index 507b765..c557896 100644 --- a/docs/guides/customization.md +++ b/docs/guides/customization.md @@ -1,6 +1,6 @@ # Customization -This guide covers how to adapt the ai-cv-claude-skills framework to your specific needs. +This guide covers how to adapt the whet framework to your specific needs. ## Overriding Default Configurations diff --git a/docs/index.md b/docs/index.md index c20cca1..ec33219 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,14 +6,14 @@ ## What Is This? -AI/CV Claude Skills is a curated collection of **25 specialized skills**, **4 agent personas**, and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. +AI/CV Claude Skills is a curated collection of **30 specialized skills**, **6 agent personas**, and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. Each skill is a focused knowledge module that Claude Code loads on demand. When you tell Claude to "use the PyTorch Lightning skill," it gains deep understanding of Lightning best practices, project structure, and common patterns -- producing code that looks like it was written by a specialist. ## Key Features -- **25 Domain-Specific Skills** -- From PyTorch Lightning training loops to ONNX model export, each skill encodes expert knowledge about a specific tool or pattern. -- **4 Agent Personas** -- Pre-configured behavioral profiles (Expert Coder, ML Engineer, Code Reviewer, Test Engineer) that adjust Claude's strictness, focus, and output style. +- **30 Domain-Specific Skills** -- From PyTorch Lightning training loops to ONNX model export, each skill encodes expert knowledge about a specific tool or pattern. +- **6 Agent Personas** -- Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Reviewer, Test Engineer) that adjust Claude's strictness, focus, and output style. - **6 Project Archetypes** -- Complete project templates for common CV/ML project types: training pipelines, inference services, research notebooks, library packages, data pipelines, and model zoos. - **Composable by Design** -- Skills combine naturally. A PyTorch training project might use the PyTorch Lightning, Hydra Config, Weights & Biases, and Docker CV skills together. - **Production Standards** -- Every skill enforces type hints, comprehensive error handling, proper logging, and test coverage. No prototype-quality code. @@ -44,13 +44,13 @@ Claude Code will generate: ## Project Structure ``` -ai-cv-claude-skills/ +whet/ skills/ # 25 domain-specific skill modules master-skill/ # Core conventions for all CV/ML code pytorch-lightning/ # PyTorch Lightning training patterns pydantic-strict/ # Strict data validation with Pydantic ... - agents/ # 4 agent persona definitions + agents/ # 6 agent persona definitions expert-coder/ # High-strictness coding agent ml-engineer/ # ML-focused development agent ... @@ -76,8 +76,8 @@ Ready to dive in? Head to the [Installation](getting-started/installation.md) gu | Section | Description | |---------|-------------| | [Getting Started](getting-started/installation.md) | Install prerequisites, clone the repo, and configure Claude Code | -| [Skills Overview](skills/index.md) | Browse all 25 skills with categories and descriptions | -| [Agents](agents/index.md) | Learn about the 4 agent personas and when to use each | +| [Skills Overview](skills/index.md) | Browse all 30 skills with categories and descriptions | +| [Agents](agents/index.md) | Learn about the 6 agent personas and when to use each | | [Archetypes](archetypes/index.md) | Explore the 6 project templates | | [Guides](guides/creating-projects.md) | Step-by-step guides for common workflows | | [Examples](examples/full-workflow.md) | End-to-end examples with real code | diff --git a/docs/skills/abstraction-patterns.md b/docs/skills/abstraction-patterns.md index 4ca27ae..f604f94 100644 --- a/docs/skills/abstraction-patterns.md +++ b/docs/skills/abstraction-patterns.md @@ -125,4 +125,4 @@ class FocalLoss(DetectionLoss): ## Full Reference -See [`skills/abstraction-patterns/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/abstraction-patterns/SKILL.md) for patterns including factory methods, builder patterns for complex pipelines, and mixin classes for common ML functionality. +See [`skills/abstraction-patterns/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/abstraction-patterns/SKILL.md) for patterns including factory methods, builder patterns for complex pipelines, and mixin classes for common ML functionality. diff --git a/docs/skills/aws-sagemaker.md b/docs/skills/aws-sagemaker.md new file mode 100644 index 0000000..33dfd69 --- /dev/null +++ b/docs/skills/aws-sagemaker.md @@ -0,0 +1,98 @@ +# AWS SageMaker + +The AWS SageMaker skill provides expert patterns for ML training and deployment on Amazon SageMaker, covering training jobs, endpoints, pipelines, and hyperparameter tuning. + +**Skill directory:** `skills/aws-sagemaker/` + +## Purpose + +SageMaker is the standard managed ML platform on AWS. This skill encodes best practices for structuring SageMaker projects: PyTorch estimator configuration with distributed training, custom inference handlers, SageMaker Pipelines with conditional model registration, hyperparameter tuning jobs, and local mode testing. + +## When to Use + +Use this skill whenever you need to: + +- Train models on AWS GPU instances (ml.g5, ml.p4, ml.trn1) +- Deploy models as real-time SageMaker endpoints with auto-scaling +- Build automated ML pipelines (preprocessing, training, evaluation, registration) +- Run hyperparameter tuning to optimize model performance +- Process large datasets with SageMaker Processing jobs + +## Key Patterns + +### Training Job with PyTorch Estimator + +```python +from sagemaker.pytorch import PyTorch + +estimator = PyTorch( + entry_point="train.py", + source_dir="src/training", + role=config.role, + instance_type="ml.g5.2xlarge", + instance_count=1, + framework_version="2.1.0", + py_version="py310", + output_path=config.output_s3_uri, + hyperparameters=hyperparameters.model_dump(), +) + +estimator.fit( + inputs={"train": train_s3_uri, "validation": val_s3_uri}, + wait=False, +) +``` + +### Custom Inference Handler + +```python +def model_fn(model_dir: str) -> torch.nn.Module: + """Load trained model.""" + model = build_model("resnet50", num_classes=10) + model.load_state_dict(torch.load(f"{model_dir}/model.pth")) + model.eval() + return model.cuda() + + +def input_fn(request_body: bytes, content_type: str) -> torch.Tensor: + """Deserialize input to tensor.""" + ... + + +def predict_fn(input_data: torch.Tensor, model: torch.nn.Module) -> dict: + """Run inference.""" + ... +``` + +### SageMaker Pipeline + +```python +from sagemaker.workflow.pipeline import Pipeline +from sagemaker.workflow.steps import ProcessingStep, TrainingStep +from sagemaker.workflow.condition_step import ConditionStep + +pipeline = Pipeline( + name="cv-training-pipeline", + parameters=[input_data, accuracy_threshold], + steps=[preprocess_step, training_step, condition_step], +) +``` + +## Anti-Patterns to Avoid + +- Do not hardcode S3 paths -- use SageMaker session defaults and pipeline parameters +- Do not skip local mode testing -- validate with `instance_type="local"` first +- Do not put credentials in training scripts -- SageMaker injects IAM roles +- Do not download datasets inside training scripts -- use SageMaker input channels + +## Combines Well With + +- **PyTorch Lightning** -- LightningModule inside SageMaker training jobs +- **Docker CV** -- Custom training containers when default images are insufficient +- **W&B / MLflow** -- Experiment tracking inside SageMaker training containers +- **DVC** -- Data versioning with S3 remote storage +- **GCP** -- Alternative cloud platform patterns for comparison + +## Full Reference + +See [`skills/aws-sagemaker/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/aws-sagemaker/SKILL.md) for complete patterns including batch transform, hyperparameter tuning, and S3 data management. diff --git a/docs/skills/code-quality.md b/docs/skills/code-quality.md index d933d4a..b0e2a2b 100644 --- a/docs/skills/code-quality.md +++ b/docs/skills/code-quality.md @@ -68,13 +68,13 @@ ignore_missing_imports = true ```bash # Format code -pixi run ruff format src/ tests/ +uv run ruff format src/ tests/ # Lint with auto-fix -pixi run ruff check --fix src/ tests/ +uv run ruff check --fix src/ tests/ # Type check -pixi run mypy src/ +uv run mypy src/ ``` ## Anti-Patterns to Avoid @@ -93,4 +93,4 @@ pixi run mypy src/ ## Full Reference -See [`skills/code-quality/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/code-quality/SKILL.md) for the full configuration including per-file rule overrides, custom ruff rules for ML patterns, and mypy plugin configuration for PyTorch and Pydantic. +See [`skills/code-quality/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/code-quality/SKILL.md) for the full configuration including per-file rule overrides, custom ruff rules for ML patterns, and mypy plugin configuration for PyTorch and Pydantic. diff --git a/docs/skills/docker-cv.md b/docs/skills/docker-cv.md index b5c459b..6dd94b6 100644 --- a/docs/skills/docker-cv.md +++ b/docs/skills/docker-cv.md @@ -90,4 +90,4 @@ services: ## Full Reference -See [`skills/docker-cv/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/docker-cv/SKILL.md) for patterns including inference-optimized images, TensorRT integration, and Kubernetes deployment manifests. +See [`skills/docker-cv/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/docker-cv/SKILL.md) for patterns including inference-optimized images, TensorRT integration, and Kubernetes deployment manifests. diff --git a/docs/skills/dvc.md b/docs/skills/dvc.md index 9004319..02fcabc 100644 --- a/docs/skills/dvc.md +++ b/docs/skills/dvc.md @@ -99,4 +99,4 @@ dvc pull ## Full Reference -See [`skills/dvc/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/dvc/SKILL.md) for patterns including DVC experiments, metrics comparison across branches, and integration with cloud storage providers. +See [`skills/dvc/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/dvc/SKILL.md) for patterns including DVC experiments, metrics comparison across branches, and integration with cloud storage providers. diff --git a/docs/skills/fastapi.md b/docs/skills/fastapi.md new file mode 100644 index 0000000..16061b7 --- /dev/null +++ b/docs/skills/fastapi.md @@ -0,0 +1,108 @@ +# FastAPI + +The FastAPI skill provides expert patterns for building ML model serving APIs with FastAPI, covering async endpoints, Pydantic request/response validation, dependency injection, middleware, and production deployment. + +**Skill directory:** `skills/fastapi/` + +## Purpose + +FastAPI is the standard framework for serving ML models over HTTP. This skill encodes the best practices for structuring FastAPI applications in ML/CV contexts: application factory pattern with lifespan-managed model loading, typed request/response schemas with Pydantic validation, dependency injection for model and preprocessor instances, and production deployment with Uvicorn. + +## When to Use + +Use this skill whenever you need to: + +- Serve trained models (ONNX, TensorRT, PyTorch) behind REST endpoints +- Build real-time WebSocket streaming for video inference pipelines +- Construct microservices wrapping ML inference +- Create APIs with health checks, batch endpoints, and structured error responses + +## Key Patterns + +### Application Factory with Lifespan + +```python +from __future__ import annotations + +from contextlib import asynccontextmanager +from collections.abc import AsyncGenerator + +from fastapi import FastAPI + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncGenerator[None]: + """Load models on startup, release on shutdown.""" + await model_registry.load(config) + yield + await model_registry.shutdown() + + +def create_app(config: AppConfig | None = None) -> FastAPI: + config = config or AppConfig() + app = FastAPI(title=config.title, lifespan=lifespan) + app.include_router(prediction.router, prefix="/api/v1") + return app +``` + +### Pydantic Request/Response Schemas + +```python +from pydantic import BaseModel, Field, field_validator + + +class PredictionRequest(BaseModel, frozen=True): + image_b64: str = Field(..., description="Base64-encoded image") + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) + + @field_validator("image_b64") + @classmethod + def validate_base64(cls, v: str) -> str: + import base64 + base64.b64decode(v, validate=True) + return v + + +class PredictionResponse(BaseModel, frozen=True): + detections: list[Detection] + inference_time_ms: float + model_version: str +``` + +### Dependency Injection for Models + +```python +from typing import Annotated +from fastapi import Depends + + +@router.post("/predict", response_model=PredictionResponse) +async def predict( + request: PredictionRequest, + model: Annotated[Model, Depends(get_model)], + preprocessor: Annotated[Preprocessor, Depends(get_preprocessor)], +) -> PredictionResponse: + image = preprocessor.decode_and_preprocess(request.image_b64) + detections = await model.predict(image) + return PredictionResponse(detections=detections, ...) +``` + +## Anti-Patterns to Avoid + +- Do not use `dict` for request/response schemas -- define explicit Pydantic models +- Do not load models inside endpoint functions -- use lifespan handlers +- Do not block the event loop with synchronous inference -- use async or `run_in_executor` +- Do not hardcode CORS origins in production -- load from configuration +- Do not return raw numpy arrays -- serialize to lists or base64 + +## Combines Well With + +- **Pydantic Strict** -- Frozen BaseModel patterns for all API schemas +- **ONNX / TensorRT** -- Optimized model formats loaded in lifespan +- **Docker CV** -- Production containerization for FastAPI services +- **Loguru** -- Structured logging in middleware and exception handlers +- **Testing** -- Async endpoint testing with httpx + +## Full Reference + +See [`skills/fastapi/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/fastapi/SKILL.md) for complete patterns including WebSocket streaming, background tasks, and middleware. diff --git a/docs/skills/gcp.md b/docs/skills/gcp.md index f212cf2..3ce2360 100644 --- a/docs/skills/gcp.md +++ b/docs/skills/gcp.md @@ -85,4 +85,4 @@ class VertexJobConfig(BaseModel, frozen=True): ## Full Reference -See [`skills/gcp/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/gcp/SKILL.md) for Artifact Registry setup, Cloud Storage patterns, Vertex AI GPU reference, authentication flows, Pydantic config models, and pixi task definitions. +See [`skills/gcp/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/gcp/SKILL.md) for Artifact Registry setup, Cloud Storage patterns, Vertex AI GPU reference, authentication flows, Pydantic config models, and pixi task definitions. diff --git a/docs/skills/github-actions.md b/docs/skills/github-actions.md index 2ffe841..757ae23 100644 --- a/docs/skills/github-actions.md +++ b/docs/skills/github-actions.md @@ -37,22 +37,22 @@ jobs: - uses: prefix-dev/setup-pixi@v0.8.1 with: pixi-version: v0.30.0 - - run: pixi run ruff check src/ tests/ - - run: pixi run ruff format --check src/ tests/ + - run: uv run ruff check src/ tests/ + - run: uv run ruff format --check src/ tests/ typecheck: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: prefix-dev/setup-pixi@v0.8.1 - - run: pixi run mypy src/ + - run: uv run mypy src/ test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: prefix-dev/setup-pixi@v0.8.1 - - run: pixi run pytest tests/ -v --tb=short + - run: uv run pytest tests/ -v --tb=short ``` ### Docker Build and Push @@ -123,4 +123,4 @@ release: ## Full Reference -See [`skills/github-actions/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/github-actions/SKILL.md) for patterns including GPU-enabled runners, model artifact caching, and matrix builds across Python versions. +See [`skills/github-actions/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/github-actions/SKILL.md) for patterns including GPU-enabled runners, model artifact caching, and matrix builds across Python versions. diff --git a/docs/skills/github-repo-setup.md b/docs/skills/github-repo-setup.md index d9cfd6c..8b936f1 100644 --- a/docs/skills/github-repo-setup.md +++ b/docs/skills/github-repo-setup.md @@ -81,4 +81,4 @@ class RepoConfig(BaseModel): ## Full Reference -See [`skills/github-repo-setup/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/github-repo-setup/SKILL.md) for the complete setup script, Pydantic models, issue templates, CODEOWNERS patterns, and merge strategy guidance. +See [`skills/github-repo-setup/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/github-repo-setup/SKILL.md) for the complete setup script, Pydantic models, issue templates, CODEOWNERS patterns, and merge strategy guidance. diff --git a/docs/skills/gradio.md b/docs/skills/gradio.md new file mode 100644 index 0000000..d39320b --- /dev/null +++ b/docs/skills/gradio.md @@ -0,0 +1,67 @@ +# Gradio + +The Gradio skill provides expert patterns for building ML model demos and interactive interfaces with Gradio, covering Interface/Blocks layouts, input/output components, and deployment to Hugging Face Spaces. + +**Skill directory:** `skills/gradio/` + +## Purpose + +Gradio is the standard tool for quickly building interactive demos for ML models. This skill encodes best practices for structuring Gradio applications: choosing between `gr.Interface` and `gr.Blocks`, handling image/video/text inputs, integrating with PyTorch and ONNX models, and deploying to Hugging Face Spaces. + +## When to Use + +Use this skill whenever you need to: + +- Build interactive demos for trained models +- Create quick prototypes for stakeholder review +- Deploy model demos to Hugging Face Spaces +- Build custom UIs with multi-step workflows using Blocks + +## Key Patterns + +### Quick Interface + +```python +import gradio as gr + +def classify(image): + predictions = model(preprocess(image)) + return {labels[i]: float(p) for i, p in enumerate(predictions)} + +demo = gr.Interface( + fn=classify, + inputs=gr.Image(type="pil"), + outputs=gr.Label(num_top_classes=5), + title="Image Classifier", +) +demo.launch() +``` + +### Blocks Layout + +```python +with gr.Blocks() as demo: + gr.Markdown("# Object Detection") + with gr.Row(): + input_image = gr.Image(type="pil") + output_image = gr.Image() + confidence = gr.Slider(0, 1, value=0.5, label="Confidence") + btn = gr.Button("Detect") + btn.click(detect, [input_image, confidence], output_image) +``` + +## Anti-Patterns to Avoid + +- Do not load models inside prediction functions -- load once at module level +- Do not use `share=True` for production deployments -- use proper hosting +- Do not skip input validation -- validate image formats and sizes + +## Combines Well With + +- **Hugging Face** -- Load models from Hub, deploy to Spaces +- **FastAPI** -- Embed Gradio as a sub-app in larger services +- **ONNX / TensorRT** -- Serve optimized models through Gradio interfaces + +## Full Reference + +See [`skills/gradio/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/gradio/SKILL.md) for complete patterns. diff --git a/docs/skills/huggingface.md b/docs/skills/huggingface.md new file mode 100644 index 0000000..77b58fd --- /dev/null +++ b/docs/skills/huggingface.md @@ -0,0 +1,101 @@ +# Hugging Face + +The Hugging Face skill provides expert patterns for the Hugging Face ecosystem — Transformers, Datasets, PEFT, and the Model Hub — covering pretrained model loading, fine-tuning, and inference. + +**Skill directory:** `skills/huggingface/` + +## Purpose + +The Hugging Face ecosystem is the standard toolkit for working with pretrained models. This skill encodes best practices for loading models with AutoModel classes and version pinning, preprocessing datasets with batched mapping, fine-tuning with the Trainer API, parameter-efficient fine-tuning with LoRA, and publishing models with model cards. + +## When to Use + +Use this skill whenever you need to: + +- Load pretrained vision models (ResNet, ViT, DETR, CLIP) or text models (BERT, RoBERTa) +- Fine-tune models on custom datasets with the Trainer API +- Use PEFT/LoRA for parameter-efficient adaptation with limited data +- Build quick inference pipelines for classification, detection, or zero-shot tasks +- Publish models and datasets to the Hugging Face Hub + +## Key Patterns + +### AutoModel Loading + +```python +from transformers import AutoModelForImageClassification, AutoImageProcessor + +processor = AutoImageProcessor.from_pretrained( + "microsoft/resnet-50", + revision="main", +) + +model = AutoModelForImageClassification.from_pretrained( + "microsoft/resnet-50", + revision="main", + torch_dtype=torch.float32, +) +``` + +### Fine-Tuning with Trainer + +```python +from transformers import Trainer, TrainingArguments, EarlyStoppingCallback + +training_args = TrainingArguments( + output_dir="outputs/finetuned", + num_train_epochs=10, + per_device_train_batch_size=32, + learning_rate=5e-5, + fp16=True, + eval_strategy="epoch", + load_best_model_at_end=True, +) + +trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + compute_metrics=compute_metrics, + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], +) + +trainer.train() +``` + +### PEFT / LoRA + +```python +from peft import LoraConfig, TaskType, get_peft_model + +lora_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=16, + lora_alpha=32, + lora_dropout=0.1, + target_modules=["query", "value"], +) + +model = get_peft_model(base_model, lora_config) +# Trains <1% of parameters +``` + +## Anti-Patterns to Avoid + +- Do not hardcode model class names -- use `AutoModel`, `AutoTokenizer` for flexibility +- Do not download models inside training loops -- load once, cache with `cache_dir` +- Do not skip `revision` parameter -- pin model versions for reproducibility +- Do not fine-tune all parameters with limited data -- use PEFT/LoRA + +## Combines Well With + +- **PyTorch Lightning** -- Custom training loops wrapping HF models +- **ONNX** -- Export HF models for optimized inference +- **W&B** -- Experiment tracking via `report_to=["wandb"]` +- **AWS SageMaker** -- Deploy HF models with SageMaker HF DLC +- **FastAPI** -- Serve HF pipelines behind async endpoints + +## Full Reference + +See [`skills/huggingface/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/huggingface/SKILL.md) for complete patterns including datasets, pipelines, model hub publishing, and quantization. diff --git a/docs/skills/hydra-config.md b/docs/skills/hydra-config.md index 2c1e135..47e3956 100644 --- a/docs/skills/hydra-config.md +++ b/docs/skills/hydra-config.md @@ -111,4 +111,4 @@ if __name__ == "__main__": ## Full Reference -See [`skills/hydra-config/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/hydra-config/SKILL.md) for patterns including multi-run sweeps, Hydra callbacks, and integration with Optuna for hyperparameter optimization. +See [`skills/hydra-config/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/hydra-config/SKILL.md) for patterns including multi-run sweeps, Hydra callbacks, and integration with Optuna for hyperparameter optimization. diff --git a/docs/skills/index.md b/docs/skills/index.md index 352db63..c177e2e 100644 --- a/docs/skills/index.md +++ b/docs/skills/index.md @@ -49,6 +49,16 @@ Skills specific to computer vision workflows. | [Matplotlib](matplotlib.md) | Visualization and plotting for CV results | matplotlib | | [ONNX](onnx.md) | Model export and optimization | onnx, onnxruntime, onnxslim | | [TensorRT](tensorrt.md) | GPU-optimized inference engine building | TensorRT, trtexec | +| [Hugging Face](huggingface.md) | Pretrained models, fine-tuning, PEFT/LoRA | transformers, datasets, peft | + +### Cloud & Deployment + +| Skill | Description | Key Libraries | +|-------|-------------|---------------| +| [AWS SageMaker](aws-sagemaker.md) | ML training and deployment on AWS | sagemaker, boto3 | +| [FastAPI](fastapi.md) | ML model serving APIs | FastAPI, uvicorn | +| [Kubernetes](kubernetes.md) | ML service deployment and orchestration on K8s | kubectl, helm | +| [Gradio](gradio.md) | Interactive ML model demos and prototypes | Gradio | ### Infrastructure & DevOps @@ -117,5 +127,5 @@ Each skill is validated by automated tests that check: Run the validation suite: ```bash -pixi run pytest tests/test_skills_completeness.py -v +uv run pytest tests/test_skills_completeness.py -v ``` diff --git a/docs/skills/kubernetes.md b/docs/skills/kubernetes.md new file mode 100644 index 0000000..5ee5836 --- /dev/null +++ b/docs/skills/kubernetes.md @@ -0,0 +1,76 @@ +# Kubernetes + +The Kubernetes skill provides expert patterns for deploying ML services on Kubernetes, covering Deployments, Services, GPU scheduling, Helm charts, and autoscaling. + +**Skill directory:** `skills/kubernetes/` + +## Purpose + +Kubernetes is the standard orchestration platform for production ML serving at scale. This skill encodes best practices for deploying ML models on K8s: GPU resource requests, health check probes, Horizontal Pod Autoscaler configuration, Helm chart structure, and environment management with Kustomize. + +## When to Use + +Use this skill whenever you need to: + +- Deploy model serving APIs on Kubernetes with GPU support +- Set up autoscaling based on inference latency or GPU utilization +- Manage multi-environment deployments (dev/staging/prod) +- Package ML services as Helm charts + +## Key Patterns + +### GPU Deployment + +```yaml +apiVersion: apps/v1 +kind: Deployment +spec: + template: + spec: + containers: + - name: model-api + resources: + requests: + nvidia.com/gpu: "1" + limits: + nvidia.com/gpu: "1" + livenessProbe: + httpGet: + path: /health + readinessProbe: + httpGet: + path: /ready +``` + +### Horizontal Pod Autoscaler + +```yaml +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +spec: + minReplicas: 2 + maxReplicas: 10 + metrics: + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: 70 +``` + +## Anti-Patterns to Avoid + +- Do not use `latest` image tags -- pin versions with SHA digests +- Do not skip resource limits -- unbounded pods risk node instability +- Do not store model weights in container images -- use persistent volumes + +## Combines Well With + +- **Docker CV** -- Container images deployed to K8s +- **FastAPI** -- The serving framework inside K8s pods +- **GCP / AWS SageMaker** -- Managed K8s clusters (GKE, EKS) + +## Full Reference + +See [`skills/kubernetes/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/kubernetes/SKILL.md) for complete patterns including Helm charts, Kustomize, and monitoring. diff --git a/docs/skills/library-review.md b/docs/skills/library-review.md index 6a8302f..9feebf6 100644 --- a/docs/skills/library-review.md +++ b/docs/skills/library-review.md @@ -85,4 +85,4 @@ The skill emphasizes criteria specific to ML work: ## Full Reference -See [`skills/library-review/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/library-review/SKILL.md) for the complete evaluation rubric, including security assessment, license compatibility matrix, and migration cost estimation. +See [`skills/library-review/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/library-review/SKILL.md) for the complete evaluation rubric, including security assessment, license compatibility matrix, and migration cost estimation. diff --git a/docs/skills/loguru.md b/docs/skills/loguru.md index 387edcb..a0a68a8 100644 --- a/docs/skills/loguru.md +++ b/docs/skills/loguru.md @@ -75,4 +75,4 @@ logging.basicConfig(handlers=[InterceptHandler()], level=0, force=True) ## Full Reference -See [`skills/loguru/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/loguru/SKILL.md) for complete patterns including custom ML log levels, JSON serialization, Pydantic configuration, and exception handling decorators. +See [`skills/loguru/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/loguru/SKILL.md) for complete patterns including custom ML log levels, JSON serialization, Pydantic configuration, and exception handling decorators. diff --git a/docs/skills/master-skill.md b/docs/skills/master-skill.md index 84d9f73..618f329 100644 --- a/docs/skills/master-skill.md +++ b/docs/skills/master-skill.md @@ -86,4 +86,4 @@ The Master Skill's conventions are assumed by every other skill. When the PyTorc ## Full Reference -See the complete skill definition in [`skills/master-skill/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/master-skill/SKILL.md) for the full set of conventions, including docstring format, project layout rules, and anti-patterns to avoid. +See the complete skill definition in [`skills/master-skill/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/master-skill/SKILL.md) for the full set of conventions, including docstring format, project layout rules, and anti-patterns to avoid. diff --git a/docs/skills/matplotlib.md b/docs/skills/matplotlib.md index 20e3fc5..a34a505 100644 --- a/docs/skills/matplotlib.md +++ b/docs/skills/matplotlib.md @@ -106,4 +106,4 @@ def plot_image_grid( ## Full Reference -See [`skills/matplotlib/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/matplotlib/SKILL.md) for patterns including confusion matrix heatmaps, ROC curves, t-SNE embeddings, and animated training progress. +See [`skills/matplotlib/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/matplotlib/SKILL.md) for patterns including confusion matrix heatmaps, ROC curves, t-SNE embeddings, and animated training progress. diff --git a/docs/skills/mlflow.md b/docs/skills/mlflow.md index 54ce173..a596419 100644 --- a/docs/skills/mlflow.md +++ b/docs/skills/mlflow.md @@ -93,4 +93,4 @@ with mlflow.start_run(run_name="resnet50-baseline") as run: ## Full Reference -See [`skills/mlflow/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/mlflow/SKILL.md) for patterns including model serving, A/B testing with model stages, and custom MLflow plugins for CV-specific artifact types. +See [`skills/mlflow/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/mlflow/SKILL.md) for patterns including model serving, A/B testing with model stages, and custom MLflow plugins for CV-specific artifact types. diff --git a/docs/skills/onnx.md b/docs/skills/onnx.md index ec3b096..9895618 100644 --- a/docs/skills/onnx.md +++ b/docs/skills/onnx.md @@ -118,4 +118,4 @@ onnx.save(slimmed, "model.onnx") ## Full Reference -See [`skills/onnx/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/onnx/SKILL.md) for patterns including TensorRT optimization, quantization, model surgery, and batched inference with dynamic shapes. +See [`skills/onnx/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/onnx/SKILL.md) for patterns including TensorRT optimization, quantization, model surgery, and batched inference with dynamic shapes. diff --git a/docs/skills/opencv.md b/docs/skills/opencv.md index 1ddc582..3193cfc 100644 --- a/docs/skills/opencv.md +++ b/docs/skills/opencv.md @@ -118,4 +118,4 @@ def process_video_frames( ## Full Reference -See [`skills/opencv/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/opencv/SKILL.md) for patterns including camera calibration, stereo vision, and high-performance image processing with CUDA-accelerated OpenCV. +See [`skills/opencv/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/opencv/SKILL.md) for patterns including camera calibration, stereo vision, and high-performance image processing with CUDA-accelerated OpenCV. diff --git a/docs/skills/pixi.md b/docs/skills/pixi.md index 2921644..167f375 100644 --- a/docs/skills/pixi.md +++ b/docs/skills/pixi.md @@ -89,4 +89,4 @@ export = { cmd = "python scripts/export_onnx.py", depends-on = ["train"] } ## Full Reference -See [`skills/pixi/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/pixi/SKILL.md) for advanced patterns including multi-platform builds, workspace configurations, and integration with conda-forge for system-level CV libraries. +See [`skills/pixi/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pixi/SKILL.md) for advanced patterns including multi-platform builds, workspace configurations, and integration with conda-forge for system-level CV libraries. diff --git a/docs/skills/pre-commit.md b/docs/skills/pre-commit.md index 749aaa3..15cfe3b 100644 --- a/docs/skills/pre-commit.md +++ b/docs/skills/pre-commit.md @@ -62,10 +62,10 @@ repos: pixi add pre-commit # Install hooks -pixi run pre-commit install +uv run pre-commit install # Run on all files (first time or CI) -pixi run pre-commit run --all-files +uv run pre-commit run --all-files ``` ## Anti-Patterns to Avoid @@ -84,4 +84,4 @@ pixi run pre-commit run --all-files ## Full Reference -See [`skills/pre-commit/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/pre-commit/SKILL.md) for patterns including custom local hooks for ML-specific validation, commit message linting, and DVC file validation hooks. +See [`skills/pre-commit/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pre-commit/SKILL.md) for patterns including custom local hooks for ML-specific validation, commit message linting, and DVC file validation hooks. diff --git a/docs/skills/pydantic-strict.md b/docs/skills/pydantic-strict.md index 03b245a..b4002df 100644 --- a/docs/skills/pydantic-strict.md +++ b/docs/skills/pydantic-strict.md @@ -101,4 +101,4 @@ class PredictionResponse(BaseModel, strict=True): ## Full Reference -See [`skills/pydantic-strict/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/pydantic-strict/SKILL.md) for patterns covering custom validators, serialization, and Pydantic settings for environment variable loading. +See [`skills/pydantic-strict/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pydantic-strict/SKILL.md) for patterns covering custom validators, serialization, and Pydantic settings for environment variable loading. diff --git a/docs/skills/pypi.md b/docs/skills/pypi.md index e54b29c..351b155 100644 --- a/docs/skills/pypi.md +++ b/docs/skills/pypi.md @@ -74,13 +74,13 @@ __version__ = "0.1.0" ```bash # Build the package -pixi run python -m build +uv run python -m build # Check the distribution -pixi run twine check dist/* +uv run twine check dist/* # Publish to PyPI -pixi run twine upload dist/* +uv run twine upload dist/* ``` ### Semantic Release Configuration @@ -119,4 +119,4 @@ prerelease_token = "dev" ## Full Reference -See [`skills/pypi/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/pypi/SKILL.md) for patterns including namespace packages, C extension builds for CV operations, and TestPyPI workflows. +See [`skills/pypi/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pypi/SKILL.md) for patterns including namespace packages, C extension builds for CV operations, and TestPyPI workflows. diff --git a/docs/skills/pytorch-lightning.md b/docs/skills/pytorch-lightning.md index ead9fa2..c3b3059 100644 --- a/docs/skills/pytorch-lightning.md +++ b/docs/skills/pytorch-lightning.md @@ -112,4 +112,4 @@ class ImageDataModule(L.LightningDataModule): ## Full Reference -See [`skills/pytorch-lightning/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/pytorch-lightning/SKILL.md) for complete patterns including custom callbacks, profiling, and advanced distributed training configuration. +See [`skills/pytorch-lightning/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pytorch-lightning/SKILL.md) for complete patterns including custom callbacks, profiling, and advanced distributed training configuration. diff --git a/docs/skills/tensorboard.md b/docs/skills/tensorboard.md index c43d4db..48d96b1 100644 --- a/docs/skills/tensorboard.md +++ b/docs/skills/tensorboard.md @@ -89,4 +89,4 @@ with profile( ## Full Reference -See [`skills/tensorboard/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/tensorboard/SKILL.md) for patterns including embedding visualization, custom scalar plugins, and multi-run comparison layouts. +See [`skills/tensorboard/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/tensorboard/SKILL.md) for patterns including embedding visualization, custom scalar plugins, and multi-run comparison layouts. diff --git a/docs/skills/tensorrt.md b/docs/skills/tensorrt.md index 552ddb2..36b99ef 100644 --- a/docs/skills/tensorrt.md +++ b/docs/skills/tensorrt.md @@ -73,4 +73,4 @@ PyTorch → torch.onnx.export() → onnxslim.slim() → trtexec/Builder → .eng ## Full Reference -See [`skills/tensorrt/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/tensorrt/SKILL.md) for Python Builder API, dynamic shape profiles, INT8 calibration, benchmarking utilities, Pydantic configuration, and Dockerfile patterns. +See [`skills/tensorrt/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/tensorrt/SKILL.md) for Python Builder API, dynamic shape profiles, INT8 calibration, benchmarking utilities, Pydantic configuration, and Dockerfile patterns. diff --git a/docs/skills/testing.md b/docs/skills/testing.md index d332f72..640ce05 100644 --- a/docs/skills/testing.md +++ b/docs/skills/testing.md @@ -104,4 +104,4 @@ filterwarnings = ["ignore::DeprecationWarning"] ## Full Reference -See [`skills/testing/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/testing/SKILL.md) for advanced patterns including GPU-conditional tests, snapshot testing for model outputs, and property-based testing with Hypothesis. +See [`skills/testing/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/testing/SKILL.md) for advanced patterns including GPU-conditional tests, snapshot testing for model outputs, and property-based testing with Hypothesis. diff --git a/docs/skills/vscode.md b/docs/skills/vscode.md index 6c75c7d..cb7a807 100644 --- a/docs/skills/vscode.md +++ b/docs/skills/vscode.md @@ -102,4 +102,4 @@ A well-configured editor eliminates friction. This skill teaches Claude Code to ## Full Reference -See [`skills/vscode/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/vscode/SKILL.md) for patterns including Dev Container configuration, multi-root workspaces, and task definitions for common ML workflows. +See [`skills/vscode/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/vscode/SKILL.md) for patterns including Dev Container configuration, multi-root workspaces, and task definitions for common ML workflows. diff --git a/docs/skills/wandb.md b/docs/skills/wandb.md index f835f35..e484d52 100644 --- a/docs/skills/wandb.md +++ b/docs/skills/wandb.md @@ -106,4 +106,4 @@ parameters: ## Full Reference -See [`skills/wandb/SKILL.md`](https://github.com/ortizeg/ai-cv-claude-skills/blob/main/skills/wandb/SKILL.md) for patterns including custom W&B Tables for dataset visualization, model registry integration, and alert configuration for metric thresholds. +See [`skills/wandb/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/wandb/SKILL.md) for patterns including custom W&B Tables for dataset visualization, model registry integration, and alert configuration for metric thresholds. diff --git a/justfile b/justfile new file mode 100644 index 0000000..2015671 --- /dev/null +++ b/justfile @@ -0,0 +1,32 @@ +# whet — development task runner + +test: + uv run pytest tests/ -v + +test-cov: + uv run pytest tests/ --cov=src/whet --cov-report=term --cov-report=html + +lint: + uv run ruff check . + +format: + uv run ruff format . + +format-check: + uv run ruff format --check . + +typecheck: + uv run mypy src/whet/ tests/ --strict + +docs-serve: + uv run mkdocs serve + +docs-build: + uv run mkdocs build + +# Run all checks (lint + typecheck + test) +check: lint typecheck test + +# Install whet in development mode +dev: + uv sync --all-extras diff --git a/mkdocs.yml b/mkdocs.yml index 6d399f7..e633471 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,9 +1,9 @@ site_name: AI/CV Claude Skills site_description: Production-ready skills framework for computer vision and deep learning projects site_author: Enrique G. Ortiz -site_url: https://ortizeg.github.io/ai-cv-claude-skills/ -repo_url: https://github.com/ortizeg/ai-cv-claude-skills -repo_name: ortizeg/ai-cv-claude-skills +site_url: https://ortizeg.github.io/whet/ +repo_url: https://github.com/ortizeg/whet +repo_name: ortizeg/whet theme: name: material @@ -84,12 +84,19 @@ nav: - TensorRT: skills/tensorrt.md - Abstraction Patterns: skills/abstraction-patterns.md - Library Review: skills/library-review.md + - FastAPI: skills/fastapi.md + - AWS SageMaker: skills/aws-sagemaker.md + - Hugging Face: skills/huggingface.md + - Gradio: skills/gradio.md + - Kubernetes: skills/kubernetes.md - Agents: - Overview: agents/index.md - Expert Coder: agents/expert-coder.md - ML Engineer: agents/ml-engineer.md - Code Review: agents/code-review.md - Test Engineer: agents/test-engineer.md + - DevOps/Infra: agents/devops-infra.md + - Data Engineer: agents/data-engineer.md - Archetypes: - Overview: archetypes/index.md - PyTorch Training: archetypes/pytorch-training-project.md diff --git a/pixi.lock b/pixi.lock deleted file mode 100644 index ef44601..0000000 --- a/pixi.lock +++ /dev/null @@ -1,1869 +0,0 @@ -version: 6 -environments: - default: - channels: - - url: https://conda.anaconda.org/conda-forge/ - indexes: - - https://pypi.org/simple - options: - pypi-prerelease-mode: if-necessary-or-explicit - packages: - linux-64: - - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/backrefs-5.8-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.1.4-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.13.2-py314h67df5f8_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ghp-import-2.1.0-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/griffe-1.15.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - 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Ortiz "] -channels = ["conda-forge"] -platforms = ["linux-64", "osx-64", "osx-arm64"] - -[dependencies] -python = ">=3.11" -pytest = ">=7.4" -pytest-cov = ">=4.1" -ruff = ">=0.8" -mypy = ">=1.11" -mkdocs-material = ">=9.5" -mkdocstrings = ">=0.24" -mkdocstrings-python = ">=1.11" - -[pypi-dependencies] -types-PyYAML = ">=6.0" - -[tasks] -test = "pytest tests/ -v" -test-cov = "pytest tests/ --cov=. --cov-report=term --cov-report=html" -lint = "ruff check ." -format = "ruff format ." -typecheck = "mypy tests/ --strict" -docs-serve = "mkdocs serve" -docs-build = "mkdocs build" diff --git a/pyproject.toml b/pyproject.toml index 88aa33f..569365f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,23 +1,83 @@ [project] -name = "ai-cv-claude-skills" +name = "whet" version = "0.1.0" -description = "Production-ready skills for AI/CV projects with Claude Code" +description = "Sharpen your AI coder — install expert skills into AI coding agents" authors = [{name = "Enrique G. Ortiz", email = "ortizeg@gmail.com"}] readme = "README.md" requires-python = ">=3.11" license = {text = "MIT"} +keywords = ["ai", "skills", "claude", "agents", "cli", "computer-vision", "deep-learning"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Environment :: Console", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Software Development :: Code Generators", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Typing :: Typed", +] + +dependencies = [ + "typer>=0.15", + "rich>=13.0", + "pydantic>=2.0", + "tomli>=2.0; python_version < '3.12'", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.4", + "pytest-cov>=4.1", + "ruff>=0.8", + "mypy>=1.11", + "types-PyYAML>=6.0", +] +docs = [ + "mkdocs-material>=9.5", + "mkdocstrings>=0.24", + "mkdocstrings-python>=1.11", +] + +[project.urls] +Homepage = "https://github.com/ortizeg/whet" +Documentation = "https://ortizeg.github.io/whet/" +Repository = "https://github.com/ortizeg/whet" +Issues = "https://github.com/ortizeg/whet/issues" + +[project.scripts] +whet = "whet.cli:app" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/whet"] + +[tool.hatch.build.targets.sdist] +include = [ + "src/whet/", + "skills/", + "agents/", + "archetypes/", + "settings/", +] [tool.ruff] line-length = 100 target-version = "py311" -exclude = [".pixi"] +exclude = [".pixi", ".venv", "archetypes/*/template"] [tool.ruff.lint] select = ["E", "F", "I", "N", "UP", "S", "B", "A", "C4", "T20", "SIM"] -ignore = ["S101"] +ignore = ["S101", "B008", "UP042"] [tool.ruff.lint.per-file-ignores] "tests/**/*.py" = ["S101", "T201"] +"src/whet/cli/**/*.py" = ["B008"] [tool.mypy] python_version = "3.11" diff --git a/settings.local.json b/settings.local.json index 053f690..acc8ab3 100644 --- a/settings.local.json +++ b/settings.local.json @@ -1,48 +1,46 @@ { "permissions": { "allow": [ - "Bash(git *)", - "Bash(gh *)", - "Bash(gcloud *)", - "Bash(docker *)", - "Bash(pixi *)", - "Bash(*/pixi *)", - "Bash(python *)", - "Bash(python3 *)", - "Bash(pip *)", - "Bash(pip3 *)", - "Bash(pytest *)", - "Bash(*/pytest *)", - "Bash(ruff *)", - "Bash(*/ruff *)", - "Bash(mypy *)", - "Bash(*/mypy *)", - "Bash(ls *)", - "Bash(cat *)", - "Bash(mkdir *)", - "Bash(cp *)", - "Bash(mv *)", - "Bash(rm *)", - "Bash(chmod *)", - "Bash(which *)", - "Bash(wc *)", - "Bash(diff *)", - "Bash(head *)", - "Bash(tail *)", - "Bash(sort *)", - "Bash(uniq *)", - "Bash(find *)", - "Bash(grep *)", - "Bash(echo *)", - "Bash(curl *)", - "Bash(wget *)", - "Bash(npm *)", - "Bash(npx *)", + "Bash(git *)", "Bash(gh *)", "Bash(gcloud *)", + "Bash(docker *)", "Bash(docker-compose *)", + "Bash(uv *)", "Bash(uvx *)", + "Bash(python *)", "Bash(python3 *)", + "Bash(pip *)", "Bash(pip3 *)", + "Bash(pytest *)", "Bash(*/pytest *)", + "Bash(ruff *)", "Bash(*/ruff *)", + "Bash(mypy *)", "Bash(*/mypy *)", + "Bash(mkdocs *)", "Bash(just *)", + "Bash(ls)", "Bash(ls *)", + "Bash(pwd)", + "Bash(tree)", "Bash(tree *)", + "Bash(cat *)", "Bash(head *)", "Bash(tail *)", + "Bash(wc)", "Bash(wc *)", + "Bash(mkdir *)", "Bash(cp *)", "Bash(mv *)", "Bash(rm *)", + "Bash(touch *)", "Bash(ln *)", "Bash(chmod *)", + "Bash(which)", "Bash(which *)", + "Bash(type *)", "Bash(command *)", + "Bash(diff *)", "Bash(sort *)", "Bash(uniq *)", + "Bash(find *)", "Bash(grep *)", + "Bash(echo *)", "Bash(printf *)", + "Bash(curl *)", "Bash(wget *)", + "Bash(npm *)", "Bash(npx *)", "Bash(node *)", + "Bash(realpath *)", "Bash(dirname *)", "Bash(basename *)", + "Bash(stat *)", "Bash(file *)", + "Bash(env *)", + "Bash(printenv)", "Bash(printenv *)", + "Bash(uname)", "Bash(uname *)", + "Bash(date)", "Bash(date *)", + "Bash(tee *)", "Bash(xargs *)", + "Bash(sed *)", "Bash(awk *)", "Bash(cut *)", "Bash(tr *)", + "Bash(test *)", "Bash([ *)", + "Bash(true)", "Bash(false)", "WebFetch(domain:github.com)", "WebFetch(domain:raw.githubusercontent.com)", "WebFetch(domain:pypi.org)", "WebFetch(domain:pytorch.org)", - "WebFetch(domain:docs.python.org)" + "WebFetch(domain:docs.python.org)", + "WebFetch(domain:huggingface.co)", + "WebFetch(domain:stackoverflow.com)" ] } -} \ No newline at end of file +} diff --git a/settings/antigravity.json b/settings/antigravity.json new file mode 100644 index 0000000..7651666 --- /dev/null +++ b/settings/antigravity.json @@ -0,0 +1,39 @@ +{ + "permissions": { + "allow": [ + "Bash(git *)", "Bash(gh *)", "Bash(gcloud *)", + "Bash(docker *)", "Bash(docker-compose *)", + "Bash(uv *)", "Bash(uvx *)", + "Bash(python *)", "Bash(python3 *)", + "Bash(pip *)", "Bash(pip3 *)", + "Bash(pytest *)", "Bash(*/pytest *)", + "Bash(ruff *)", "Bash(*/ruff *)", + "Bash(mypy *)", "Bash(*/mypy *)", + "Bash(mkdocs *)", "Bash(just *)", + "Bash(ls)", "Bash(ls *)", + "Bash(pwd)", + "Bash(tree)", "Bash(tree *)", + "Bash(cat *)", "Bash(head *)", "Bash(tail *)", + "Bash(wc)", "Bash(wc *)", + "Bash(mkdir *)", "Bash(cp *)", "Bash(mv *)", "Bash(rm *)", + "Bash(touch *)", "Bash(ln *)", "Bash(chmod *)", + "Bash(which)", "Bash(which *)", + "Bash(type *)", "Bash(command *)", + "Bash(diff *)", "Bash(sort *)", "Bash(uniq *)", + "Bash(find *)", "Bash(grep *)", + "Bash(echo *)", "Bash(printf *)", + "Bash(curl *)", "Bash(wget *)", + "Bash(npm *)", "Bash(npx *)", "Bash(node *)", + "Bash(realpath *)", "Bash(dirname *)", "Bash(basename *)", + "Bash(stat *)", "Bash(file *)", + "Bash(env *)", + "Bash(printenv)", "Bash(printenv *)", + "Bash(uname)", "Bash(uname *)", + "Bash(date)", "Bash(date *)", + "Bash(tee *)", "Bash(xargs *)", + "Bash(sed *)", "Bash(awk *)", "Bash(cut *)", "Bash(tr *)", + "Bash(test *)", "Bash([ *)", + "Bash(true)", "Bash(false)" + ] + } +} diff --git a/settings/base.json b/settings/base.json new file mode 100644 index 0000000..2a2defc --- /dev/null +++ b/settings/base.json @@ -0,0 +1,30 @@ +{ + "permissions": { + "allow": [ + "Bash(git *)", "Bash(gh *)", + "Bash(uv *)", "Bash(uvx *)", + "Bash(python *)", "Bash(python3 *)", + "Bash(pip *)", "Bash(pip3 *)", + "Bash(pytest *)", "Bash(*/pytest *)", + "Bash(ruff *)", "Bash(*/ruff *)", + "Bash(mypy *)", "Bash(*/mypy *)", + "Bash(just *)", + "Bash(ls)", "Bash(ls *)", + "Bash(pwd)", + "Bash(cat *)", "Bash(head *)", "Bash(tail *)", + "Bash(wc)", "Bash(wc *)", + "Bash(mkdir *)", "Bash(cp *)", "Bash(mv *)", "Bash(rm *)", + "Bash(touch *)", "Bash(ln *)", "Bash(chmod *)", + "Bash(which)", "Bash(which *)", + "Bash(diff *)", "Bash(sort *)", "Bash(uniq *)", + "Bash(find *)", "Bash(grep *)", + "Bash(echo *)", "Bash(printf *)", + "Bash(realpath *)", "Bash(dirname *)", "Bash(basename *)", + "Bash(stat *)", "Bash(file *)", + "Bash(env *)", + "Bash(printenv)", "Bash(printenv *)", + "Bash(test *)", "Bash([ *)", + "Bash(true)", "Bash(false)" + ] + } +} diff --git a/settings/claude.json b/settings/claude.json new file mode 100644 index 0000000..1d95120 --- /dev/null +++ b/settings/claude.json @@ -0,0 +1,48 @@ +{ + "permissions": { + "allow": [ + "Bash(git *)", "Bash(gh *)", "Bash(gcloud *)", + "Bash(docker *)", "Bash(docker-compose *)", + "Bash(uv *)", "Bash(uvx *)", + "Bash(python *)", "Bash(python3 *)", + "Bash(pip *)", "Bash(pip3 *)", + "Bash(pytest *)", "Bash(*/pytest *)", + "Bash(ruff *)", "Bash(*/ruff *)", + "Bash(mypy *)", "Bash(*/mypy *)", + "Bash(mkdocs *)", "Bash(just *)", + "Bash(ls)", "Bash(ls *)", + "Bash(pwd)", + "Bash(tree)", "Bash(tree *)", + "Bash(cat *)", "Bash(head *)", "Bash(tail *)", + "Bash(wc)", "Bash(wc *)", + "Bash(mkdir *)", "Bash(cp *)", "Bash(mv *)", "Bash(rm *)", + "Bash(touch *)", "Bash(ln *)", "Bash(chmod *)", + "Bash(which)", "Bash(which *)", + "Bash(type *)", "Bash(command *)", + "Bash(diff *)", "Bash(sort *)", "Bash(uniq *)", + "Bash(find *)", "Bash(grep *)", + "Bash(echo *)", "Bash(printf *)", + "Bash(curl *)", "Bash(wget *)", + "Bash(npm *)", "Bash(npx *)", "Bash(node *)", + "Bash(realpath *)", "Bash(dirname *)", "Bash(basename *)", + "Bash(stat *)", "Bash(file *)", + "Bash(env *)", + "Bash(printenv)", "Bash(printenv *)", + "Bash(uname)", "Bash(uname *)", + "Bash(date)", "Bash(date *)", + "Bash(tee *)", "Bash(xargs *)", + "Bash(sed *)", "Bash(awk *)", "Bash(cut *)", "Bash(tr *)", + "Bash(test *)", "Bash([ *)", + "Bash(true)", "Bash(false)", + "WebFetch(domain:github.com)", + "WebFetch(domain:raw.githubusercontent.com)", + "WebFetch(domain:pypi.org)", + "WebFetch(domain:pytorch.org)", + "WebFetch(domain:docs.python.org)", + "WebFetch(domain:huggingface.co)", + "WebFetch(domain:stackoverflow.com)", + "WebFetch(domain:npmjs.com)", + "WebFetch(domain:developer.mozilla.org)" + ] + } +} diff --git a/skills/abstraction-patterns/SKILL.md b/skills/abstraction-patterns/SKILL.md index c9e8e47..89849b2 100644 --- a/skills/abstraction-patterns/SKILL.md +++ b/skills/abstraction-patterns/SKILL.md @@ -1,3 +1,11 @@ +--- +name: abstraction-patterns +description: > + Teaches when and how to create well-designed abstractions in AI/CV Python projects. + Covers the Rule of Three, interface design, wrapper patterns, and anti-patterns + for reducing cognitive load without over-engineering. +--- + # Abstraction Patterns Skill You are writing well-abstracted Python code for AI/CV projects. Follow these patterns exactly. diff --git a/skills/abstraction-patterns/skill.toml b/skills/abstraction-patterns/skill.toml new file mode 100644 index 0000000..f9e97a9 --- /dev/null +++ b/skills/abstraction-patterns/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "abstraction-patterns" +version = "1.0.0" +category = "core" +tags = ["design-patterns", "abc", "protocol", "architecture"] + +[dependencies] +requires = ["pydantic-strict"] +recommends = ["master-skill"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/aws-sagemaker/README.md b/skills/aws-sagemaker/README.md new file mode 100644 index 0000000..885fd2b --- /dev/null +++ b/skills/aws-sagemaker/README.md @@ -0,0 +1,32 @@ +# AWS SageMaker Skill + +The AWS SageMaker Skill provides expert patterns for building ML training and deployment pipelines on Amazon SageMaker, covering PyTorch estimators, training script entry points, real-time inference endpoints, batch transform jobs, hyperparameter tuning, SageMaker Pipelines for end-to-end orchestration, and S3 data management. + +## Purpose + +When you need to train models at scale on AWS infrastructure or deploy models behind managed endpoints, SageMaker is the standard managed ML platform. This skill encodes best practices for structuring SageMaker projects: proper estimator configuration, custom inference handlers, pipeline definitions with conditional model registration, and local mode testing to validate scripts before submitting expensive cloud jobs. + +## When to Use + +- When training models on AWS GPU instances (ml.g5, ml.p4, ml.trn1). +- When deploying models as real-time SageMaker endpoints with auto-scaling. +- When building automated ML pipelines with preprocessing, training, evaluation, and model registration. +- When running hyperparameter tuning jobs to optimize model performance. +- When processing large datasets with SageMaker Processing jobs. + +## Key Features + +- **PyTorch Estimator patterns** — proper configuration with distributed training support, instance selection, and hyperparameter passing. +- **Training scripts** — SageMaker-compatible entry points that read `SM_CHANNEL_*` and `SM_MODEL_DIR` environment variables. +- **Custom inference handlers** — `model_fn`, `input_fn`, `predict_fn`, `output_fn` for flexible serving. +- **SageMaker Pipelines** — end-to-end workflow orchestration with conditional model registration based on metrics. +- **Hyperparameter tuning** — Bayesian optimization with metric definitions and parameter ranges. +- **Local mode testing** — validate training scripts with `instance_type="local"` before cloud submission. + +## Related Skills + +- **[PyTorch Lightning](../pytorch-lightning/)** — LightningModule patterns used inside SageMaker training scripts. +- **[Docker CV](../docker-cv/)** — custom training container images when default SageMaker images are insufficient. +- **[W&B](../wandb/)** / **[MLflow](../mlflow/)** — experiment tracking inside SageMaker training containers. +- **[DVC](../dvc/)** — data versioning with S3 remote storage. +- **[GCP](../gcp/)** — alternative cloud platform patterns for comparison. diff --git a/skills/aws-sagemaker/SKILL.md b/skills/aws-sagemaker/SKILL.md new file mode 100644 index 0000000..4067854 --- /dev/null +++ b/skills/aws-sagemaker/SKILL.md @@ -0,0 +1,624 @@ +--- +name: aws-sagemaker +description: > + AWS SageMaker patterns for ML training and deployment. Covers training jobs, + real-time endpoints, batch transform, processing jobs, model registry, + hyperparameter tuning, SageMaker Pipelines, and S3 data management. +--- + +# AWS SageMaker Skill + +You are building ML training and deployment pipelines on AWS SageMaker. Follow these patterns exactly. + +## Core Philosophy + +SageMaker provides managed infrastructure for training, tuning, and deploying ML models at scale. Every cloud training and deployment workflow targeting AWS uses SageMaker. Use the SageMaker Python SDK for all interactions — never call low-level boto3 APIs for SageMaker operations unless the SDK does not support a feature. + +## Project Structure + +### Standard SageMaker Project Layout + +``` +project/ +├── src/ +│ ├── training/ +│ │ ├── train.py # Training entry point +│ │ ├── model.py # Model definition +│ │ └── data.py # Data loading +│ ├── inference/ +│ │ ├── inference.py # model_fn, input_fn, predict_fn, output_fn +│ │ └── requirements.txt # Inference dependencies +│ └── processing/ +│ └── preprocess.py # Processing job script +├── pipelines/ +│ ├── training_pipeline.py # SageMaker Pipeline definition +│ └── config.py # Pipeline configuration +├── configs/ +│ ├── training.yaml # Hyperparameters +│ └── infrastructure.yaml # Instance types, counts +└── tests/ + ├── test_training_local.py # Local mode tests + └── test_inference.py # Endpoint tests +``` + +## Training Jobs + +### Configuring a Training Job with PyTorch Estimator + +```python +"""SageMaker training job configuration.""" + +from __future__ import annotations + +from pathlib import Path + +import sagemaker +from pydantic import BaseModel, Field +from sagemaker.pytorch import PyTorch + + +class TrainingConfig(BaseModel, frozen=True): + """Training job configuration.""" + + role: str + instance_type: str = "ml.g5.2xlarge" + instance_count: int = 1 + max_run_seconds: int = 86400 + volume_size_gb: int = 100 + output_s3_uri: str + base_job_name: str = "cv-training" + framework_version: str = "2.1.0" + py_version: str = "py310" + + +class HyperParameters(BaseModel, frozen=True): + """Training hyperparameters passed to the training script.""" + + epochs: int = 50 + batch_size: int = 32 + learning_rate: float = 1e-3 + weight_decay: float = 1e-4 + model_name: str = "resnet50" + num_classes: int = 10 + + +def create_estimator( + config: TrainingConfig, + hyperparameters: HyperParameters, +) -> PyTorch: + """Create a SageMaker PyTorch estimator.""" + return PyTorch( + entry_point="train.py", + source_dir="src/training", + role=config.role, + instance_type=config.instance_type, + instance_count=config.instance_count, + framework_version=config.framework_version, + py_version=config.py_version, + output_path=config.output_s3_uri, + base_job_name=config.base_job_name, + max_run=config.max_run_seconds, + volume_size=config.volume_size_gb, + hyperparameters=hyperparameters.model_dump(), + environment={ + "NCCL_DEBUG": "INFO", + "TORCH_DISTRIBUTED_DEBUG": "DETAIL", + }, + distribution={ + "torch_distributed": {"enabled": True} + } if config.instance_count > 1 else None, + ) + + +def launch_training( + config: TrainingConfig, + hyperparameters: HyperParameters, + train_s3_uri: str, + val_s3_uri: str, +) -> str: + """Launch a SageMaker training job and return the job name.""" + estimator = create_estimator(config, hyperparameters) + + estimator.fit( + inputs={ + "train": train_s3_uri, + "validation": val_s3_uri, + }, + wait=False, + ) + + return estimator.latest_training_job.name +``` + +### Training Script Entry Point + +```python +"""SageMaker training script entry point (src/training/train.py).""" + +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path + +import torch +import torch.distributed as dist +from loguru import logger + + +def parse_args() -> argparse.Namespace: + """Parse SageMaker-injected arguments.""" + parser = argparse.ArgumentParser() + + # Hyperparameters + parser.add_argument("--epochs", type=int, default=50) + parser.add_argument("--batch-size", type=int, default=32) + parser.add_argument("--learning-rate", type=float, default=1e-3) + parser.add_argument("--model-name", type=str, default="resnet50") + parser.add_argument("--num-classes", type=int, default=10) + + # SageMaker environment variables + parser.add_argument("--model-dir", type=str, default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model")) + parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train")) + parser.add_argument("--validation", type=str, default=os.environ.get("SM_CHANNEL_VALIDATION", "/opt/ml/input/data/validation")) + parser.add_argument("--output-data-dir", type=str, default=os.environ.get("SM_OUTPUT_DATA_DIR", "/opt/ml/output/data")) + + return parser.parse_args() + + +def train(args: argparse.Namespace) -> None: + """Main training function.""" + logger.info("Starting training with args: {}", vars(args)) + + # Distributed setup + world_size = int(os.environ.get("SM_NUM_GPUS", 1)) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + + if world_size > 1: + dist.init_process_group(backend="nccl") + torch.cuda.set_device(local_rank) + logger.info("Distributed training: rank {} of {}", local_rank, world_size) + + device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") + + model = build_model(args.model_name, args.num_classes).to(device) + if world_size > 1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank]) + + # ... training loop ... + + # Save model artifacts + model_path = Path(args.model_dir) / "model.pth" + torch.save(model.state_dict(), model_path) + logger.info("Model saved to {}", model_path) + + # Save metrics + metrics = {"final_val_loss": 0.25, "final_val_acc": 0.92} + metrics_path = Path(args.output_data_dir) / "metrics.json" + metrics_path.write_text(json.dumps(metrics)) + + +if __name__ == "__main__": + args = parse_args() + train(args) +``` + +## Real-Time Endpoints + +### Deploying a Model Endpoint + +```python +"""SageMaker endpoint deployment.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field +from sagemaker.pytorch import PyTorchModel + + +class EndpointConfig(BaseModel, frozen=True): + """Endpoint deployment configuration.""" + + role: str + instance_type: str = "ml.g5.xlarge" + instance_count: int = 1 + endpoint_name: str + model_data_s3: str + framework_version: str = "2.1.0" + py_version: str = "py310" + + +def deploy_endpoint(config: EndpointConfig) -> str: + """Deploy a real-time inference endpoint.""" + model = PyTorchModel( + model_data=config.model_data_s3, + role=config.role, + framework_version=config.framework_version, + py_version=config.py_version, + entry_point="inference.py", + source_dir="src/inference", + ) + + predictor = model.deploy( + initial_instance_count=config.instance_count, + instance_type=config.instance_type, + endpoint_name=config.endpoint_name, + ) + + return predictor.endpoint_name +``` + +### Custom Inference Script + +```python +"""Custom inference handlers (src/inference/inference.py). + +SageMaker calls these functions in order: + input_fn → predict_fn → output_fn +""" + +from __future__ import annotations + +import io +import json + +import numpy as np +import torch +from PIL import Image +from torchvision import transforms + + +def model_fn(model_dir: str) -> torch.nn.Module: + """Load the trained model from the model directory.""" + model = build_model("resnet50", num_classes=10) + model.load_state_dict(torch.load(f"{model_dir}/model.pth", map_location="cpu")) + model.eval() + return model.cuda() if torch.cuda.is_available() else model + + +def input_fn(request_body: bytes, content_type: str) -> torch.Tensor: + """Deserialize input data to a tensor.""" + if content_type == "application/x-image": + image = Image.open(io.BytesIO(request_body)).convert("RGB") + transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + return transform(image).unsqueeze(0) + elif content_type == "application/json": + data = json.loads(request_body) + return torch.tensor(data["instances"]) + else: + raise ValueError(f"Unsupported content type: {content_type}") + + +def predict_fn(input_data: torch.Tensor, model: torch.nn.Module) -> dict: + """Run inference on the input tensor.""" + device = next(model.parameters()).device + input_data = input_data.to(device) + + with torch.no_grad(): + outputs = model(input_data) + probabilities = torch.softmax(outputs, dim=1) + predictions = torch.argmax(probabilities, dim=1) + + return { + "predictions": predictions.cpu().numpy().tolist(), + "probabilities": probabilities.cpu().numpy().tolist(), + } + + +def output_fn(prediction: dict, accept: str) -> str: + """Serialize prediction output.""" + if accept == "application/json": + return json.dumps(prediction) + raise ValueError(f"Unsupported accept type: {accept}") +``` + +## Hyperparameter Tuning + +### Automatic Model Tuning + +```python +"""SageMaker hyperparameter tuning job.""" + +from __future__ import annotations + +from sagemaker.tuner import ( + CategoricalParameter, + ContinuousParameter, + HyperparameterTuner, + IntegerParameter, +) + + +def create_tuner(estimator: PyTorch) -> HyperparameterTuner: + """Create a hyperparameter tuning job.""" + hyperparameter_ranges = { + "learning-rate": ContinuousParameter(1e-5, 1e-2, scaling_type="Logarithmic"), + "batch-size": CategoricalParameter([16, 32, 64, 128]), + "weight-decay": ContinuousParameter(1e-6, 1e-2, scaling_type="Logarithmic"), + "epochs": IntegerParameter(10, 100), + } + + return HyperparameterTuner( + estimator=estimator, + objective_metric_name="validation:accuracy", + hyperparameter_ranges=hyperparameter_ranges, + metric_definitions=[ + {"Name": "validation:accuracy", "Regex": r"val_acc=(\S+)"}, + {"Name": "validation:loss", "Regex": r"val_loss=(\S+)"}, + ], + max_jobs=20, + max_parallel_jobs=4, + strategy="Bayesian", + objective_type="Maximize", + ) +``` + +## SageMaker Pipelines + +### End-to-End Training Pipeline + +```python +"""SageMaker Pipeline for training and registration.""" + +from __future__ import annotations + +import sagemaker +from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor +from sagemaker.pytorch import PyTorch +from sagemaker.workflow.condition_step import ConditionStep +from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo +from sagemaker.workflow.functions import JsonGet +from sagemaker.workflow.model_step import ModelStep +from sagemaker.workflow.parameters import ParameterFloat, ParameterString +from sagemaker.workflow.pipeline import Pipeline +from sagemaker.workflow.steps import ProcessingStep, TrainingStep + + +def create_pipeline( + role: str, + pipeline_name: str = "cv-training-pipeline", +) -> Pipeline: + """Create a SageMaker Pipeline.""" + session = sagemaker.Session() + + # Pipeline parameters + input_data = ParameterString(name="InputData") + accuracy_threshold = ParameterFloat(name="AccuracyThreshold", default_value=0.90) + + # Step 1: Data preprocessing + processor = ScriptProcessor( + role=role, + image_uri=session.sagemaker_client.describe_image("pytorch-training")["ImageUri"], + instance_type="ml.m5.xlarge", + instance_count=1, + command=["python3"], + ) + + preprocess_step = ProcessingStep( + name="PreprocessData", + processor=processor, + inputs=[ProcessingInput(source=input_data, destination="/opt/ml/processing/input")], + outputs=[ + ProcessingOutput(output_name="train", source="/opt/ml/processing/output/train"), + ProcessingOutput(output_name="val", source="/opt/ml/processing/output/val"), + ], + code="src/processing/preprocess.py", + ) + + # Step 2: Training + estimator = PyTorch( + entry_point="train.py", + source_dir="src/training", + role=role, + instance_type="ml.g5.2xlarge", + instance_count=1, + framework_version="2.1.0", + py_version="py310", + ) + + training_step = TrainingStep( + name="TrainModel", + estimator=estimator, + inputs={ + "train": preprocess_step.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri, + "validation": preprocess_step.properties.ProcessingOutputConfig.Outputs["val"].S3Output.S3Uri, + }, + ) + + # Step 3: Conditional model registration + accuracy_condition = ConditionGreaterThanOrEqualTo( + left=JsonGet( + step_name=training_step.name, + property_file="metrics", + json_path="val_accuracy", + ), + right=accuracy_threshold, + ) + + register_step = ModelStep( + name="RegisterModel", + step_args=estimator.register( + content_types=["application/json", "application/x-image"], + response_types=["application/json"], + model_package_group_name="cv-models", + approval_status="PendingManualApproval", + ), + ) + + condition_step = ConditionStep( + name="CheckAccuracy", + conditions=[accuracy_condition], + if_steps=[register_step], + else_steps=[], + ) + + return Pipeline( + name=pipeline_name, + parameters=[input_data, accuracy_threshold], + steps=[preprocess_step, training_step, condition_step], + sagemaker_session=session, + ) +``` + +## S3 Data Management + +### Data Upload and Download Patterns + +```python +"""S3 data management for SageMaker workflows.""" + +from __future__ import annotations + +from pathlib import Path + +import boto3 +import sagemaker +from loguru import logger + + +def upload_dataset( + local_path: Path, + bucket: str, + prefix: str = "datasets", +) -> str: + """Upload a local dataset to S3 and return the S3 URI.""" + session = sagemaker.Session() + s3_uri = session.upload_data( + path=str(local_path), + bucket=bucket, + key_prefix=prefix, + ) + logger.info("Uploaded {} to {}", local_path, s3_uri) + return s3_uri + + +def download_model_artifacts( + model_data_s3: str, + local_dir: Path, +) -> Path: + """Download model artifacts from S3.""" + local_dir.mkdir(parents=True, exist_ok=True) + + session = boto3.Session() + s3 = session.resource("s3") + + # Parse S3 URI + parts = model_data_s3.replace("s3://", "").split("/", 1) + bucket_name, key = parts[0], parts[1] + + local_path = local_dir / Path(key).name + s3.Bucket(bucket_name).download_file(key, str(local_path)) + + logger.info("Downloaded {} to {}", model_data_s3, local_path) + return local_path +``` + +## Batch Transform + +### Offline Batch Inference + +```python +"""SageMaker batch transform for offline inference.""" + +from __future__ import annotations + +from sagemaker.pytorch import PyTorchModel + + +def run_batch_transform( + model_data_s3: str, + input_s3_uri: str, + output_s3_uri: str, + role: str, + instance_type: str = "ml.g5.xlarge", +) -> None: + """Run batch transform on a dataset.""" + model = PyTorchModel( + model_data=model_data_s3, + role=role, + framework_version="2.1.0", + py_version="py310", + entry_point="inference.py", + source_dir="src/inference", + ) + + transformer = model.transformer( + instance_count=1, + instance_type=instance_type, + output_path=output_s3_uri, + strategy="MultiRecord", + max_payload=6, + ) + + transformer.transform( + data=input_s3_uri, + content_type="application/json", + split_type="Line", + ) +``` + +## Local Mode Testing + +### Test Training Locally Before Submitting to SageMaker + +```python +"""Local mode testing for SageMaker training scripts.""" + +from __future__ import annotations + +import pytest +from sagemaker.pytorch import PyTorch + + +@pytest.fixture +def local_estimator() -> PyTorch: + """Create a local mode estimator for testing.""" + return PyTorch( + entry_point="train.py", + source_dir="src/training", + role="arn:aws:iam::000000000000:role/dummy", + instance_type="local", + instance_count=1, + framework_version="2.1.0", + py_version="py310", + hyperparameters={ + "epochs": 1, + "batch-size": 4, + "model-name": "resnet18", + }, + ) + + +def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: + """Verify training script runs in local mode.""" + # Create minimal test data + create_test_dataset(tmp_path / "train") + create_test_dataset(tmp_path / "val") + + local_estimator.fit({ + "train": f"file://{tmp_path / 'train'}", + "validation": f"file://{tmp_path / 'val'}", + }) +``` + +## Anti-Patterns + +- **Never hardcode S3 paths** — use SageMaker session defaults and `ParameterString` in pipelines. +- **Never use `instance_type="ml.p3.16xlarge"` for simple models** — right-size instances. Start with `ml.g5.xlarge` and scale up. +- **Never skip local mode testing** — always test with `instance_type="local"` before submitting cloud jobs. +- **Never put credentials in training scripts** — SageMaker injects the IAM role automatically. +- **Never download the full dataset inside the training script** — use SageMaker input channels (`SM_CHANNEL_*`). +- **Never forget to call `wait=False` for long training jobs** — use async job submission and poll status separately. + +## Integration with Other Skills + +- **PyTorch Lightning** — Training scripts use LightningModule inside SageMaker training jobs. +- **Hydra Config** — Hyperparameters serialized and passed to SageMaker as flat key-value pairs. +- **W&B / MLflow** — Experiment tracking inside SageMaker training containers. +- **Docker CV** — Custom training containers when the built-in SageMaker images are insufficient. +- **DVC** — Data versioning with S3 remote storage that SageMaker can access. diff --git a/skills/aws-sagemaker/skill.toml b/skills/aws-sagemaker/skill.toml new file mode 100644 index 0000000..df3c52e --- /dev/null +++ b/skills/aws-sagemaker/skill.toml @@ -0,0 +1,16 @@ +[skill] +name = "aws-sagemaker" +version = "1.0.0" +category = "cloud" +tags = ["aws", "sagemaker", "training", "deployment", "cloud", "mlops"] + +[dependencies] +requires = ["pydantic-strict", "loguru"] +recommends = ["pytorch-lightning", "docker-cv", "wandb", "dvc"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +sagemaker = ">=2.200" +boto3 = ">=1.28" diff --git a/skills/code-quality/SKILL.md b/skills/code-quality/SKILL.md index a2d7f5d..8803316 100644 --- a/skills/code-quality/SKILL.md +++ b/skills/code-quality/SKILL.md @@ -1,3 +1,11 @@ +--- +name: code-quality +description: > + Enforces code quality standards for AI/CV Python projects using Ruff linting, + MyPy strict type checking, and automated formatting. Covers editor integration, + pre-commit hooks, and CI pipeline enforcement. +--- + # Code Quality Skill You are enforcing code quality standards for AI/CV Python projects. Follow these rules exactly. diff --git a/skills/code-quality/skill.toml b/skills/code-quality/skill.toml new file mode 100644 index 0000000..15212ab --- /dev/null +++ b/skills/code-quality/skill.toml @@ -0,0 +1,16 @@ +[skill] +name = "code-quality" +version = "1.0.0" +category = "core" +tags = ["linting", "formatting", "type-checking", "ruff", "mypy"] + +[dependencies] +requires = [] +recommends = ["pre-commit"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +ruff = ">=0.8" +mypy = ">=1.11" diff --git a/skills/docker-cv/SKILL.md b/skills/docker-cv/SKILL.md index dfb0785..696b2ee 100644 --- a/skills/docker-cv/SKILL.md +++ b/skills/docker-cv/SKILL.md @@ -1,3 +1,11 @@ +--- +name: docker-cv +description: > + Build optimized Docker images for computer vision and deep learning workloads. + Covers CUDA support, multi-stage builds, layer caching, security best practices, + and GPU-accelerated container deployment. +--- + # Docker CV Skill Build optimized Docker images for computer vision and deep learning workloads with CUDA support, multi-stage builds, and security best practices. diff --git a/skills/docker-cv/skill.toml b/skills/docker-cv/skill.toml new file mode 100644 index 0000000..095a758 --- /dev/null +++ b/skills/docker-cv/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "docker-cv" +version = "1.0.0" +category = "infra" +tags = ["docker", "containerization", "cuda", "deployment"] + +[dependencies] +requires = [] +recommends = ["pixi", "github-actions"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/dvc/SKILL.md b/skills/dvc/SKILL.md index c193345..13b647c 100644 --- a/skills/dvc/SKILL.md +++ b/skills/dvc/SKILL.md @@ -1,3 +1,11 @@ +--- +name: dvc +description: > + Data Version Control (DVC) for versioning large datasets, models, and ML pipeline + artifacts. Covers DVC initialization, remote storage configuration (S3, GCS, Azure), + pipeline definitions, and integration with Git workflows. +--- + # Data Version Control (DVC) for ML Projects ## Overview diff --git a/skills/dvc/skill.toml b/skills/dvc/skill.toml new file mode 100644 index 0000000..28e3a29 --- /dev/null +++ b/skills/dvc/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "dvc" +version = "1.0.0" +category = "infra" +tags = ["data-versioning", "dvc", "model-versioning", "pipelines"] + +[dependencies] +requires = ["loguru"] +recommends = ["gcp"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +dvc = ">=3.0" diff --git a/skills/fastapi/README.md b/skills/fastapi/README.md new file mode 100644 index 0000000..2289053 --- /dev/null +++ b/skills/fastapi/README.md @@ -0,0 +1,32 @@ +# FastAPI Skill + +The FastAPI Skill provides expert patterns for building ML model serving APIs with FastAPI, covering async endpoints, Pydantic request/response validation, dependency injection for model loading, middleware for request logging and error handling, WebSocket streaming for real-time inference, and production deployment with Uvicorn. + +## Purpose + +When you need to serve ML models over HTTP — whether for batch predictions, real-time video inference, or integration with frontend applications — FastAPI is the standard framework. This skill encodes best practices for structuring FastAPI applications in ML/CV contexts: proper model lifecycle management via lifespan handlers, typed request/response schemas, async inference patterns, and production-ready Dockerfile configurations. + +## When to Use + +- When building REST APIs that serve trained models (ONNX, TensorRT, PyTorch). +- When you need real-time WebSocket streaming for video inference pipelines. +- When constructing microservices that wrap ML inference behind HTTP endpoints. +- When building model serving APIs that need health checks, batch endpoints, and structured error responses. + +## Key Features + +- **Application factory pattern** — configurable app creation with lifespan-managed model loading. +- **Pydantic schemas** — frozen BaseModel for all request and response types with field validators. +- **Dependency injection** — model and preprocessor provided via FastAPI's `Depends` system. +- **Middleware** — request ID injection, logging, timing, and structured exception handling. +- **WebSocket streaming** — real-time frame-by-frame inference over persistent connections. +- **Background tasks** — async result logging and post-processing without blocking responses. +- **Testing** — async test client patterns with httpx `ASGITransport`. + +## Related Skills + +- **[Pydantic Strict](../pydantic-strict/)** — frozen BaseModel patterns used for all API schemas. +- **[ONNX](../onnx/)** / **[TensorRT](../tensorrt/)** — optimized model formats loaded in the lifespan handler. +- **[Docker CV](../docker-cv/)** — production containerization for FastAPI ML services. +- **[Loguru](../loguru/)** — structured logging in middleware and exception handlers. +- **[Testing](../testing/)** — async endpoint testing with httpx. diff --git a/skills/fastapi/SKILL.md b/skills/fastapi/SKILL.md new file mode 100644 index 0000000..e964f8f --- /dev/null +++ b/skills/fastapi/SKILL.md @@ -0,0 +1,609 @@ +--- +name: fastapi +description: > + FastAPI patterns for building ML model serving APIs. Covers async endpoints, + Pydantic request/response models, dependency injection, middleware, CORS, + background tasks, WebSocket streaming, health checks, and structured error handling. +--- + +# FastAPI Skill + +You are building FastAPI applications for serving ML models and CV pipelines. Follow these patterns exactly. + +## Core Philosophy + +FastAPI provides automatic OpenAPI documentation, request validation via Pydantic, and async-first design. Every model serving endpoint in this framework uses FastAPI. Use Pydantic models for all request and response schemas — never accept raw dicts from API consumers. + +## Application Structure + +### Standard Application Factory + +Use an application factory pattern to configure the FastAPI app. This enables testing with different configurations and clean startup/shutdown lifecycle management. + +```python +"""FastAPI application factory for ML model serving.""" + +from __future__ import annotations + +from contextlib import asynccontextmanager +from collections.abc import AsyncGenerator +from typing import Any + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from loguru import logger +from pydantic import BaseModel, Field + + +class AppConfig(BaseModel, frozen=True): + """Application configuration.""" + + title: str = "ML Model API" + version: str = "1.0.0" + cors_origins: list[str] = Field(default_factory=lambda: ["*"]) + model_path: str = "models/best.onnx" + max_batch_size: int = 32 + device: str = "cuda:0" + + +class ModelRegistry: + """Holds loaded models for the application lifetime.""" + + def __init__(self) -> None: + self.models: dict[str, Any] = {} + + async def load(self, config: AppConfig) -> None: + logger.info("Loading model from {}", config.model_path) + # Load ONNX, TensorRT, or PyTorch model here + self.models["default"] = await _load_model(config.model_path) + logger.info("Model loaded successfully") + + async def shutdown(self) -> None: + logger.info("Releasing model resources") + self.models.clear() + + +model_registry = ModelRegistry() + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncGenerator[None]: + """Manage model loading on startup and cleanup on shutdown.""" + config = AppConfig() + await model_registry.load(config) + yield + await model_registry.shutdown() + + +def create_app(config: AppConfig | None = None) -> FastAPI: + """Create and configure the FastAPI application.""" + config = config or AppConfig() + + app = FastAPI( + title=config.title, + version=config.version, + lifespan=lifespan, + ) + + app.add_middleware( + CORSMiddleware, + allow_origins=config.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + from .routes import prediction, health + app.include_router(health.router, tags=["health"]) + app.include_router(prediction.router, prefix="/api/v1", tags=["prediction"]) + + return app +``` + +## Request and Response Models + +### Pydantic Schemas for ML Endpoints + +Define explicit Pydantic models for every endpoint. Never use `dict` or `Any` in API signatures. + +```python +"""Pydantic schemas for prediction endpoints.""" + +from __future__ import annotations + +import base64 + +from pydantic import BaseModel, Field, field_validator + + +class PredictionRequest(BaseModel, frozen=True): + """Single image prediction request.""" + + image_b64: str = Field(..., description="Base64-encoded image bytes") + confidence_threshold: float = Field( + default=0.5, ge=0.0, le=1.0, description="Minimum confidence for detections" + ) + max_detections: int = Field(default=100, ge=1, le=1000) + + @field_validator("image_b64") + @classmethod + def validate_base64(cls, v: str) -> str: + try: + base64.b64decode(v, validate=True) + except Exception as exc: + raise ValueError("Invalid base64 encoding") from exc + return v + + +class Detection(BaseModel, frozen=True): + """Single object detection result.""" + + label: str + confidence: float = Field(ge=0.0, le=1.0) + bbox: list[float] = Field(min_length=4, max_length=4, description="[x1, y1, x2, y2]") + + +class PredictionResponse(BaseModel, frozen=True): + """Prediction response with detections and metadata.""" + + detections: list[Detection] + inference_time_ms: float + model_version: str + + +class BatchPredictionRequest(BaseModel, frozen=True): + """Batch prediction request.""" + + images: list[PredictionRequest] = Field(max_length=32) + + +class BatchPredictionResponse(BaseModel, frozen=True): + """Batch prediction response.""" + + results: list[PredictionResponse] + total_inference_time_ms: float + + +class ErrorResponse(BaseModel, frozen=True): + """Structured error response.""" + + error: str + detail: str | None = None + request_id: str | None = None +``` + +## Endpoint Patterns + +### Prediction Endpoint with Dependency Injection + +```python +"""Prediction routes.""" + +from __future__ import annotations + +import time +from typing import Annotated + +import numpy as np +from fastapi import APIRouter, Depends, HTTPException + +from .schemas import ( + PredictionRequest, + PredictionResponse, + BatchPredictionRequest, + BatchPredictionResponse, + Detection, +) +from .dependencies import get_model, get_preprocessor + +router = APIRouter() + + +@router.post("/predict", response_model=PredictionResponse) +async def predict( + request: PredictionRequest, + model: Annotated[Model, Depends(get_model)], + preprocessor: Annotated[Preprocessor, Depends(get_preprocessor)], +) -> PredictionResponse: + """Run inference on a single image.""" + start = time.perf_counter() + + image = preprocessor.decode_and_preprocess(request.image_b64) + raw_detections = await model.predict(image) + + detections = [ + Detection(label=d.label, confidence=d.confidence, bbox=d.bbox) + for d in raw_detections + if d.confidence >= request.confidence_threshold + ][: request.max_detections] + + elapsed_ms = (time.perf_counter() - start) * 1000 + + return PredictionResponse( + detections=detections, + inference_time_ms=round(elapsed_ms, 2), + model_version=model.version, + ) + + +@router.post("/predict/batch", response_model=BatchPredictionResponse) +async def predict_batch( + request: BatchPredictionRequest, + model: Annotated[Model, Depends(get_model)], + preprocessor: Annotated[Preprocessor, Depends(get_preprocessor)], +) -> BatchPredictionResponse: + """Run inference on a batch of images.""" + start = time.perf_counter() + + results: list[PredictionResponse] = [] + for item in request.images: + result = await predict(item, model, preprocessor) + results.append(result) + + total_ms = (time.perf_counter() - start) * 1000 + + return BatchPredictionResponse( + results=results, + total_inference_time_ms=round(total_ms, 2), + ) +``` + +### Health Check Endpoints + +```python +"""Health check routes.""" + +from __future__ import annotations + +from fastapi import APIRouter +from pydantic import BaseModel + +router = APIRouter() + + +class HealthResponse(BaseModel, frozen=True): + status: str + model_loaded: bool + version: str + + +@router.get("/health", response_model=HealthResponse) +async def health_check() -> HealthResponse: + """Liveness and readiness probe.""" + from .app import model_registry + + return HealthResponse( + status="healthy", + model_loaded=bool(model_registry.models), + version="1.0.0", + ) + + +@router.get("/ready") +async def readiness() -> dict[str, bool]: + """Kubernetes readiness probe.""" + from .app import model_registry + + if not model_registry.models: + raise HTTPException(status_code=503, detail="Model not loaded") + return {"ready": True} +``` + +## Dependency Injection + +### Model and Preprocessor Dependencies + +```python +"""FastAPI dependencies for ML serving.""" + +from __future__ import annotations + +from functools import lru_cache + +from fastapi import Depends + + +@lru_cache(maxsize=1) +def get_app_config() -> AppConfig: + """Load application configuration once.""" + return AppConfig() + + +async def get_model( + config: AppConfig = Depends(get_app_config), +) -> Model: + """Provide the loaded model instance.""" + from .app import model_registry + + model = model_registry.models.get("default") + if model is None: + raise HTTPException(status_code=503, detail="Model not available") + return model + + +async def get_preprocessor( + config: AppConfig = Depends(get_app_config), +) -> Preprocessor: + """Provide the image preprocessor.""" + return Preprocessor(target_size=(640, 640), device=config.device) +``` + +## Middleware and Error Handling + +### Request ID and Logging Middleware + +```python +"""Custom middleware for ML API.""" + +from __future__ import annotations + +import time +import uuid + +from fastapi import FastAPI, Request, Response +from loguru import logger +from starlette.middleware.base import BaseHTTPMiddleware + + +class RequestLoggingMiddleware(BaseHTTPMiddleware): + """Log all requests with timing and request ID.""" + + async def dispatch(self, request: Request, call_next) -> Response: + request_id = str(uuid.uuid4())[:8] + request.state.request_id = request_id + + start = time.perf_counter() + response = await call_next(request) + elapsed = (time.perf_counter() - start) * 1000 + + logger.info( + "{method} {path} → {status} ({elapsed:.1f}ms) [{rid}]", + method=request.method, + path=request.url.path, + status=response.status_code, + elapsed=elapsed, + rid=request_id, + ) + + response.headers["X-Request-ID"] = request_id + return response +``` + +### Structured Exception Handlers + +```python +"""Exception handlers for consistent error responses.""" + +from __future__ import annotations + +from fastapi import FastAPI, Request +from fastapi.responses import JSONResponse +from loguru import logger + + +async def model_error_handler(request: Request, exc: ModelInferenceError) -> JSONResponse: + """Handle model inference failures.""" + logger.error("Inference error: {}", exc) + return JSONResponse( + status_code=500, + content={ + "error": "inference_error", + "detail": str(exc), + "request_id": getattr(request.state, "request_id", None), + }, + ) + + +async def validation_error_handler(request: Request, exc: ValueError) -> JSONResponse: + """Handle input validation errors with clear messages.""" + return JSONResponse( + status_code=422, + content={"error": "validation_error", "detail": str(exc)}, + ) + + +def register_exception_handlers(app: FastAPI) -> None: + """Register all exception handlers on the app.""" + app.add_exception_handler(ModelInferenceError, model_error_handler) +``` + +## WebSocket Streaming + +### Real-Time Video Inference + +```python +"""WebSocket endpoint for streaming inference.""" + +from __future__ import annotations + +import base64 + +from fastapi import APIRouter, WebSocket, WebSocketDisconnect +from loguru import logger + +router = APIRouter() + + +@router.websocket("/ws/stream") +async def stream_inference(websocket: WebSocket) -> None: + """Stream video frames and return detections in real time.""" + await websocket.accept() + logger.info("WebSocket client connected") + + try: + while True: + data = await websocket.receive_json() + frame_b64 = data.get("frame") + if not frame_b64: + await websocket.send_json({"error": "Missing 'frame' field"}) + continue + + frame_bytes = base64.b64decode(frame_b64) + detections = await run_inference(frame_bytes) + + await websocket.send_json({ + "detections": [d.model_dump() for d in detections], + "frame_id": data.get("frame_id"), + }) + except WebSocketDisconnect: + logger.info("WebSocket client disconnected") +``` + +## Background Tasks + +### Async Post-Processing + +```python +"""Background task patterns for FastAPI ML APIs.""" + +from __future__ import annotations + +from fastapi import APIRouter, BackgroundTasks +from loguru import logger + +router = APIRouter() + + +async def save_prediction_to_db( + request_id: str, + detections: list[Detection], +) -> None: + """Save prediction results asynchronously after response.""" + logger.info("Saving {} detections for request {}", len(detections), request_id) + await db.predictions.insert_one({ + "request_id": request_id, + "detections": [d.model_dump() for d in detections], + }) + + +@router.post("/predict") +async def predict_with_logging( + request: PredictionRequest, + background_tasks: BackgroundTasks, +) -> PredictionResponse: + """Predict and log results in background.""" + result = await run_prediction(request) + + background_tasks.add_task( + save_prediction_to_db, + request_id=request.state.request_id, + detections=result.detections, + ) + + return result +``` + +## Testing FastAPI Applications + +### Async Test Client + +```python +"""Tests for prediction API.""" + +from __future__ import annotations + +import base64 + +import pytest +from httpx import ASGITransport, AsyncClient + +from app.main import create_app + + +@pytest.fixture +async def client() -> AsyncClient: + """Create async test client.""" + app = create_app() + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + yield ac + + +@pytest.mark.anyio +async def test_health_check(client: AsyncClient) -> None: + response = await client.get("/health") + assert response.status_code == 200 + data = response.json() + assert data["status"] == "healthy" + + +@pytest.mark.anyio +async def test_predict_returns_detections(client: AsyncClient) -> None: + image_bytes = b"\x89PNG\r\n\x1a\n" + b"\x00" * 100 + request_body = { + "image_b64": base64.b64encode(image_bytes).decode(), + "confidence_threshold": 0.5, + } + response = await client.post("/api/v1/predict", json=request_body) + assert response.status_code == 200 + data = response.json() + assert "detections" in data + assert "inference_time_ms" in data + + +@pytest.mark.anyio +async def test_predict_rejects_invalid_base64(client: AsyncClient) -> None: + response = await client.post( + "/api/v1/predict", + json={"image_b64": "not-valid-base64!!!"}, + ) + assert response.status_code == 422 +``` + +## Docker Deployment + +### Production Dockerfile for FastAPI ML API + +```dockerfile +FROM python:3.11-slim AS base + +WORKDIR /app +RUN pip install --no-cache-dir uv + +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +COPY src/ ./src/ + +EXPOSE 8000 + +CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] +``` + +### Uvicorn Configuration + +```python +"""Uvicorn runner with production settings.""" + +import uvicorn + +if __name__ == "__main__": + uvicorn.run( + "app.main:create_app", + factory=True, + host="0.0.0.0", + port=8000, + workers=4, + log_level="info", + access_log=True, + limit_concurrency=100, + timeout_keep_alive=30, + ) +``` + +## Anti-Patterns + +- **Never use `dict` for request/response models** — always define Pydantic schemas with explicit fields and validators. +- **Never load models inside endpoint functions** — use the lifespan context manager for startup/shutdown lifecycle. +- **Never block the event loop with synchronous inference** — use `await` with async model wrappers or `run_in_executor` for CPU-bound operations. +- **Never hardcode CORS origins in production** — load from configuration. +- **Never return raw numpy arrays or tensors** — serialize to lists or base64 in the response model. +- **Never skip input validation** — use Pydantic field validators for base64, image dimensions, and parameter bounds. + +## Integration with Other Skills + +- **Pydantic Strict** — All request/response models follow frozen BaseModel patterns. +- **Docker CV** — Production Dockerfiles with multi-stage builds for FastAPI + model serving. +- **ONNX / TensorRT** — Load optimized models in the lifespan handler. +- **Loguru** — Structured logging in middleware and exception handlers. +- **Testing** — Async test client with httpx for full endpoint coverage. diff --git a/skills/fastapi/skill.toml b/skills/fastapi/skill.toml new file mode 100644 index 0000000..1f6fc6f --- /dev/null +++ b/skills/fastapi/skill.toml @@ -0,0 +1,17 @@ +[skill] +name = "fastapi" +version = "1.0.0" +category = "infra" +tags = ["api", "serving", "fastapi", "rest", "async"] + +[dependencies] +requires = ["pydantic-strict", "loguru"] +recommends = ["docker-cv", "onnx", "testing"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +fastapi = ">=0.100" +uvicorn = ">=0.20" +httpx = ">=0.24" diff --git a/skills/gcp/SKILL.md b/skills/gcp/SKILL.md index 2e4c97e..811ec5d 100644 --- a/skills/gcp/SKILL.md +++ b/skills/gcp/SKILL.md @@ -1,3 +1,11 @@ +--- +name: gcp +description: > + Google Cloud Platform services for CV/ML projects. Covers Artifact Registry for + Docker images, Cloud Storage for datasets, Vertex AI for training jobs, and + gcloud CLI patterns for infrastructure management. +--- + # GCP Skill Google Cloud Platform services for CV/ML projects: Artifact Registry, Cloud Storage, Vertex AI training, and Docker image management. diff --git a/skills/gcp/skill.toml b/skills/gcp/skill.toml new file mode 100644 index 0000000..f8178e0 --- /dev/null +++ b/skills/gcp/skill.toml @@ -0,0 +1,16 @@ +[skill] +name = "gcp" +version = "1.0.0" +category = "infra" +tags = ["cloud", "gcp", "vertex-ai", "gcs", "deployment"] + +[dependencies] +requires = ["docker-cv"] +recommends = ["github-actions"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +google-cloud-storage = ">=2.0" +google-cloud-aiplatform = ">=1.30" diff --git a/skills/github-actions/SKILL.md b/skills/github-actions/SKILL.md index a44b2c3..5b0771c 100644 --- a/skills/github-actions/SKILL.md +++ b/skills/github-actions/SKILL.md @@ -1,3 +1,11 @@ +--- +name: github-actions +description: > + CI/CD workflow patterns for ML/CV projects using GitHub Actions. Covers tiered + pipelines (lint, test, build, deploy), GPU runner support, dependency caching, + matrix builds, and AI agent integration. +--- + # GitHub Actions Skill CI/CD workflow patterns for ML/CV projects with tiered pipelines, GPU runner support, caching, and AI agent integration. diff --git a/skills/github-actions/skill.toml b/skills/github-actions/skill.toml new file mode 100644 index 0000000..1e3210f --- /dev/null +++ b/skills/github-actions/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "github-actions" +version = "1.0.0" +category = "infra" +tags = ["ci-cd", "automation", "github", "workflows"] + +[dependencies] +requires = [] +recommends = ["testing", "code-quality", "docker-cv"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/github-repo-setup/SKILL.md b/skills/github-repo-setup/SKILL.md index c44787c..e698775 100644 --- a/skills/github-repo-setup/SKILL.md +++ b/skills/github-repo-setup/SKILL.md @@ -1,3 +1,11 @@ +--- +name: github-repo-setup +description: > + Best practices for initializing and configuring GitHub repositories for CV/ML projects. + Covers repository creation, branch protection rules, PR and issue templates, + CODEOWNERS, merge strategies, and gh CLI automation. +--- + # GitHub Repository Setup Skill Best practices for initializing and configuring GitHub repositories for CV/ML projects. Covers repository creation, branch protection, PR templates, issue templates, CODEOWNERS, merge strategy, and automation via the `gh` CLI. diff --git a/skills/github-repo-setup/skill.toml b/skills/github-repo-setup/skill.toml new file mode 100644 index 0000000..33140ea --- /dev/null +++ b/skills/github-repo-setup/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "github-repo-setup" +version = "1.0.0" +category = "infra" +tags = ["repository", "github", "setup", "initialization"] + +[dependencies] +requires = [] +recommends = ["github-actions", "pre-commit"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/gradio/README.md b/skills/gradio/README.md new file mode 100644 index 0000000..7a9e239 --- /dev/null +++ b/skills/gradio/README.md @@ -0,0 +1,38 @@ +# Gradio Skill + +The Gradio Skill provides expert patterns for building interactive ML demos and model serving UIs with Gradio 4.x. It covers `gr.Interface` for simple demos, `gr.Blocks` for complex multi-tab layouts, multi-modal inputs and outputs (image, video, text), ONNX and PyTorch model integration, custom flagging for feedback collection, and deployment to Hugging Face Spaces and Docker. + +## Purpose + +When you need to quickly build an interactive demo for a trained model -- whether for stakeholder presentations, user testing, dataset labeling feedback, or public deployment -- Gradio is the standard tool. This skill encodes best practices for structuring Gradio applications in ML/CV contexts: proper model lifecycle management at startup, typed Pydantic configurations, reusable component patterns, and production deployment strategies including Hugging Face Spaces and FastAPI mounting. + +## When to Use + +- When building interactive demos for image classification, object detection, or segmentation models. +- When you need a quick web UI for model prototyping and stakeholder presentations. +- When collecting user feedback on model predictions via flagging for active learning. +- When deploying public-facing demos to Hugging Face Spaces. +- When creating side-by-side model comparison tools for evaluation. +- When serving real-time webcam inference through a browser-based interface. +- When mounting an ML demo inside an existing FastAPI application. + +## Key Features + +- **gr.Interface** -- single-function demos with automatic input/output type inference and example caching. +- **gr.Blocks** -- complex layouts with tabs, rows, columns, and conditional visibility for multi-step workflows. +- **Multi-modal I/O** -- image, video, text, audio, JSON, and gallery components for diverse ML tasks. +- **Model serving** -- ONNX and PyTorch model loading at startup with Pydantic-configured inference pipelines. +- **gr.load** -- instant demos from Hugging Face Hub models without writing inference code. +- **Custom flagging** -- structured feedback collection for active learning and dataset improvement. +- **Deployment** -- Hugging Face Spaces, Docker, share links, authentication, and FastAPI mounting. +- **Reusable components** -- model selectors, preprocessing controls, and comparison layouts. + +## Related Skills + +- **[FastAPI](../fastapi/)** -- mount Gradio demos inside FastAPI apps for combined API + demo serving. +- **[Hugging Face](../huggingface/)** -- load pretrained models from the Hub and deploy to Hugging Face Spaces. +- **[PyTorch Lightning](../pytorch-lightning/)** -- load Lightning checkpoints and serve trained models through Gradio. +- **[Testing](../testing/)** -- test Gradio components with the Gradio test client and pytest fixtures. +- **[ONNX](../onnx/)** -- serve optimized ONNX models for low-latency Gradio inference. +- **[Pydantic Strict](../pydantic-strict/)** -- frozen BaseModel patterns for all Gradio configuration objects. +- **[Loguru](../loguru/)** -- structured logging in model loading, prediction callbacks, and flagging. diff --git a/skills/gradio/SKILL.md b/skills/gradio/SKILL.md new file mode 100644 index 0000000..9c2328f --- /dev/null +++ b/skills/gradio/SKILL.md @@ -0,0 +1,662 @@ +--- +name: gradio +description: > + Gradio patterns for building interactive ML demos and model serving UIs. + Covers gr.Interface, gr.Blocks layouts, image/video/text inputs and outputs, + model serving with gr.load, custom components, flagging and feedback collection, + deployment to Hugging Face Spaces, and integration with PyTorch/ONNX models. +--- + +# Gradio Skill + +You are building Gradio applications for interactive ML demos and model serving UIs. Follow these patterns exactly. + +## Core Philosophy + +Gradio provides the fastest path from a trained model to an interactive web demo. Every demo in this framework uses Gradio 4.x with typed interfaces, Pydantic-validated configurations, and Loguru-based logging. Use `gr.Blocks` for complex layouts and `gr.Interface` for simple single-function demos. Never expose raw model internals to the UI layer. + +## Interface Creation + +### Simple Interface with gr.Interface + +Use `gr.Interface` when wrapping a single prediction function with clearly defined inputs and outputs. This is the fastest way to prototype a demo. + +```python +"""Simple image classification demo with gr.Interface.""" + +from __future__ import annotations + +import gradio as gr +import numpy as np +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ClassificationConfig(BaseModel, frozen=True): + """Configuration for classification demo.""" + + model_path: str = "models/classifier.onnx" + labels_path: str = "models/labels.txt" + top_k: int = Field(default=5, ge=1, le=20) + image_size: tuple[int, int] = (224, 224) + + +def classify_image( + image: Image.Image, + config: ClassificationConfig = ClassificationConfig(), +) -> dict[str, float]: + """Run classification on a single image and return top-k predictions.""" + logger.info("Received image of size {}", image.size) + preprocessed = preprocess(image, config.image_size) + predictions = run_model(preprocessed, config.model_path) + labels = load_labels(config.labels_path) + + top_indices = np.argsort(predictions)[-config.top_k :][::-1] + results = {labels[i]: float(predictions[i]) for i in top_indices} + logger.info("Top prediction: {} ({:.3f})", list(results.keys())[0], list(results.values())[0]) + return results + + +demo = gr.Interface( + fn=classify_image, + inputs=gr.Image(type="pil", label="Upload Image"), + outputs=gr.Label(num_top_classes=5, label="Predictions"), + title="Image Classifier", + description="Upload an image to classify it using the trained model.", + examples=[["examples/cat.jpg"], ["examples/dog.jpg"]], + cache_examples=True, +) +``` + +### Complex Layouts with gr.Blocks + +Use `gr.Blocks` for multi-step workflows, side-by-side comparisons, tabs, and conditional visibility. This is the standard for production demos. + +```python +"""Object detection demo with gr.Blocks layout.""" + +from __future__ import annotations + +from pathlib import Path + +import gradio as gr +import numpy as np +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class DetectionConfig(BaseModel, frozen=True): + """Detection demo configuration.""" + + model_path: str = "models/detector.onnx" + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) + nms_threshold: float = Field(default=0.45, ge=0.0, le=1.0) + max_detections: int = Field(default=100, ge=1, le=500) + device: str = "cpu" + + +def build_detection_demo(config: DetectionConfig | None = None) -> gr.Blocks: + """Build the detection demo application.""" + config = config or DetectionConfig() + logger.info("Building detection demo with model: {}", config.model_path) + + with gr.Blocks(title="Object Detection", theme=gr.themes.Soft()) as demo: + gr.Markdown("# Object Detection Demo") + gr.Markdown("Upload an image or use the webcam to detect objects in real time.") + + with gr.Tab("Image Upload"): + with gr.Row(): + with gr.Column(scale=1): + input_image = gr.Image(type="pil", label="Input Image") + confidence_slider = gr.Slider( + minimum=0.0, + maximum=1.0, + value=config.confidence_threshold, + step=0.05, + label="Confidence Threshold", + ) + detect_btn = gr.Button("Detect Objects", variant="primary") + + with gr.Column(scale=1): + output_image = gr.Image(type="pil", label="Detections") + output_json = gr.JSON(label="Detection Results") + + detect_btn.click( + fn=run_detection, + inputs=[input_image, confidence_slider], + outputs=[output_image, output_json], + ) + + with gr.Tab("Webcam"): + webcam_input = gr.Image(sources=["webcam"], type="pil", label="Webcam Feed") + webcam_output = gr.Image(type="pil", label="Live Detections") + webcam_input.stream( + fn=run_detection_realtime, + inputs=[webcam_input], + outputs=[webcam_output], + ) + + with gr.Tab("Batch Processing"): + batch_input = gr.File( + file_count="multiple", + file_types=["image"], + label="Upload Multiple Images", + ) + batch_output = gr.Gallery(label="Results", columns=3) + batch_btn = gr.Button("Process Batch", variant="primary") + batch_btn.click( + fn=run_batch_detection, + inputs=[batch_input, confidence_slider], + outputs=[batch_output], + ) + + return demo +``` + +## Image, Video, and Text Inputs and Outputs + +### Multi-Modal Input/Output Patterns + +```python +"""Multi-modal Gradio interface patterns.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger + + +def build_multimodal_demo() -> gr.Blocks: + """Build a demo with image, video, and text I/O.""" + with gr.Blocks() as demo: + gr.Markdown("# Multi-Modal Analysis") + + with gr.Tab("Image Captioning"): + img_input = gr.Image(type="pil", label="Upload Image") + caption_output = gr.Textbox(label="Generated Caption", lines=3) + img_input.change(fn=generate_caption, inputs=[img_input], outputs=[caption_output]) + + with gr.Tab("Video Analysis"): + video_input = gr.Video(label="Upload Video") + video_output = gr.Video(label="Annotated Video") + frame_gallery = gr.Gallery(label="Key Frames", columns=4) + analysis_json = gr.JSON(label="Analysis Results") + + analyze_btn = gr.Button("Analyze Video", variant="primary") + analyze_btn.click( + fn=analyze_video, + inputs=[video_input], + outputs=[video_output, frame_gallery, analysis_json], + ) + + with gr.Tab("Visual Question Answering"): + with gr.Row(): + vqa_image = gr.Image(type="pil", label="Image") + with gr.Column(): + vqa_question = gr.Textbox(label="Question", placeholder="What is in this image?") + vqa_answer = gr.Textbox(label="Answer", interactive=False) + vqa_btn = gr.Button("Ask", variant="primary") + + vqa_btn.click( + fn=answer_question, + inputs=[vqa_image, vqa_question], + outputs=[vqa_answer], + ) + + return demo +``` + +## Model Serving with gr.load + +### Loading Models from Hugging Face Hub + +Use `gr.load` to quickly create a demo from a Hugging Face model without writing inference code. + +```python +"""Serve Hugging Face models directly with gr.load.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger + +# Load a model directly from the Hugging Face Hub +logger.info("Loading model from Hugging Face Hub") +demo = gr.load( + name="facebook/detr-resnet-50", + src="models", + title="DETR Object Detection", + description="Detect objects using DETR (DEtection TRansformer).", +) + +# Load a Hugging Face Space as a component +image_gen = gr.load( + name="stabilityai/stable-diffusion-2", + src="models", +) +``` + +### Serving Custom PyTorch and ONNX Models + +```python +"""Serve a custom ONNX model through Gradio.""" + +from __future__ import annotations + +from pathlib import Path + +import gradio as gr +import numpy as np +import onnxruntime as ort +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ONNXModelConfig(BaseModel, frozen=True): + """ONNX model serving configuration.""" + + model_path: Path = Path("models/model.onnx") + input_size: tuple[int, int] = (640, 640) + providers: list[str] = Field( + default_factory=lambda: ["CUDAExecutionProvider", "CPUExecutionProvider"] + ) + + +class ModelServer: + """Wraps an ONNX model for Gradio serving.""" + + def __init__(self, config: ONNXModelConfig) -> None: + self.config = config + logger.info("Loading ONNX model from {}", config.model_path) + self.session = ort.InferenceSession( + str(config.model_path), + providers=config.providers, + ) + self.input_name = self.session.get_inputs()[0].name + logger.info("Model loaded successfully with providers: {}", config.providers) + + def predict(self, image: Image.Image) -> tuple[Image.Image, dict]: + """Run inference and return annotated image and results dict.""" + logger.debug("Preprocessing image of size {}", image.size) + input_array = self._preprocess(image) + + outputs = self.session.run(None, {self.input_name: input_array}) + detections = self._postprocess(outputs, image.size) + + annotated = self._draw_detections(image, detections) + results = {"num_detections": len(detections), "detections": detections} + logger.info("Found {} detections", len(detections)) + return annotated, results + + def _preprocess(self, image: Image.Image) -> np.ndarray: + """Resize and normalize input image.""" + resized = image.resize(self.config.input_size) + array = np.array(resized, dtype=np.float32) / 255.0 + return np.transpose(array, (2, 0, 1))[np.newaxis, ...] + + def _postprocess(self, outputs: list, original_size: tuple[int, int]) -> list[dict]: + """Convert raw model outputs to detection dicts.""" + # Implementation depends on model architecture + ... + + def _draw_detections(self, image: Image.Image, detections: list[dict]) -> Image.Image: + """Draw bounding boxes on the image.""" + # Implementation uses PIL.ImageDraw + ... + + +def create_onnx_demo(config: ONNXModelConfig | None = None) -> gr.Blocks: + """Create a Gradio demo serving an ONNX model.""" + config = config or ONNXModelConfig() + server = ModelServer(config) + + with gr.Blocks(title="ONNX Model Demo") as demo: + gr.Markdown("# Custom ONNX Model Demo") + with gr.Row(): + input_img = gr.Image(type="pil", label="Input") + output_img = gr.Image(type="pil", label="Output") + results_json = gr.JSON(label="Results") + run_btn = gr.Button("Run Inference", variant="primary") + + run_btn.click( + fn=server.predict, + inputs=[input_img], + outputs=[output_img, results_json], + ) + + return demo +``` + +## Custom Components and Layouts + +### Reusable Component Patterns + +```python +"""Reusable Gradio component patterns.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger + + +def create_model_selector( + models: dict[str, str], + default: str | None = None, +) -> gr.Dropdown: + """Create a model selection dropdown with display names.""" + choices = list(models.keys()) + return gr.Dropdown( + choices=choices, + value=default or choices[0], + label="Select Model", + info="Choose a model for inference", + ) + + +def create_preprocessing_controls() -> tuple[gr.Slider, gr.Slider, gr.Checkbox]: + """Create standard image preprocessing controls.""" + brightness = gr.Slider(-1.0, 1.0, value=0.0, step=0.1, label="Brightness") + contrast = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Contrast") + grayscale = gr.Checkbox(value=False, label="Convert to Grayscale") + return brightness, contrast, grayscale + + +def build_comparison_layout() -> gr.Blocks: + """Build a side-by-side model comparison layout.""" + with gr.Blocks() as demo: + gr.Markdown("# Model Comparison") + input_image = gr.Image(type="pil", label="Input Image") + + with gr.Row(): + with gr.Column(): + gr.Markdown("### Model A") + model_a_selector = create_model_selector( + {"YOLOv8-n": "yolov8n", "YOLOv8-s": "yolov8s"}, + default="YOLOv8-n", + ) + output_a = gr.Image(type="pil", label="Model A Output") + metrics_a = gr.JSON(label="Model A Metrics") + + with gr.Column(): + gr.Markdown("### Model B") + model_b_selector = create_model_selector( + {"YOLOv8-m": "yolov8m", "YOLOv8-l": "yolov8l"}, + default="YOLOv8-m", + ) + output_b = gr.Image(type="pil", label="Model B Output") + metrics_b = gr.JSON(label="Model B Metrics") + + compare_btn = gr.Button("Compare Models", variant="primary") + compare_btn.click( + fn=compare_models, + inputs=[input_image, model_a_selector, model_b_selector], + outputs=[output_a, metrics_a, output_b, metrics_b], + ) + + return demo +``` + +## Flagging and Feedback Collection + +### Custom Flagging Callback + +Use flagging to collect user feedback on model predictions for dataset improvement and active learning. + +```python +"""Custom flagging callback for ML feedback collection.""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +import gradio as gr +from loguru import logger +from pydantic import BaseModel, Field + + +class FlaggedSample(BaseModel, frozen=True): + """A flagged sample with metadata.""" + + timestamp: str + flag_reason: str + input_hash: str + prediction: str + user_correction: str | None = None + + +class MLFlaggingCallback(gr.FlaggingCallback): + """Custom flagging callback that saves structured feedback.""" + + def __init__(self, output_dir: str = "flagged_data") -> None: + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + logger.info("Flagging callback initialized, saving to {}", self.output_dir) + + def setup(self, components: list, flagging_dir: str | Path) -> None: + """Set up the flagging directory.""" + self.flagging_dir = Path(flagging_dir) + self.flagging_dir.mkdir(parents=True, exist_ok=True) + + def flag( + self, + flag_data: list, + flag_option: str = "incorrect", + username: str | None = None, + ) -> int: + """Save a flagged sample with structured metadata.""" + sample = FlaggedSample( + timestamp=datetime.now(tz=timezone.utc).isoformat(), + flag_reason=flag_option, + input_hash=str(hash(str(flag_data[0]))), + prediction=str(flag_data[1]) if len(flag_data) > 1 else "", + user_correction=str(flag_data[2]) if len(flag_data) > 2 else None, + ) + output_path = self.output_dir / f"flag_{sample.timestamp}.json" + output_path.write_text(sample.model_dump_json(indent=2)) + logger.info("Flagged sample saved: {} (reason: {})", output_path, flag_option) + return 1 + + +# Usage in a demo +demo = gr.Interface( + fn=classify_image, + inputs=gr.Image(type="pil"), + outputs=gr.Label(), + flagging_callback=MLFlaggingCallback(output_dir="flagged_data"), + flagging_options=["incorrect", "low_confidence", "interesting"], +) +``` + +## Deployment + +### Sharing and Hugging Face Spaces + +```python +"""Deployment patterns for Gradio demos.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger + + +def launch_demo(demo: gr.Blocks, share: bool = False) -> None: + """Launch the Gradio demo with production settings.""" + logger.info("Launching Gradio demo (share={})", share) + demo.launch( + server_name="0.0.0.0", + server_port=7860, + share=share, + show_error=True, + max_threads=10, + auth=None, # Set ("user", "pass") for basic auth + ) + + +def launch_with_auth(demo: gr.Blocks) -> None: + """Launch with authentication for internal deployments.""" + logger.info("Launching Gradio demo with authentication") + demo.launch( + server_name="0.0.0.0", + server_port=7860, + auth=[("admin", "secure_password")], + auth_message="Enter credentials to access the demo.", + ) + + +def mount_on_fastapi(demo: gr.Blocks) -> None: + """Mount Gradio inside a FastAPI application.""" + from fastapi import FastAPI + + app = FastAPI(title="ML Demo API") + + @app.get("/api/health") + async def health() -> dict[str, str]: + return {"status": "healthy"} + + app = gr.mount_gradio_app(app, demo, path="/demo") + logger.info("Gradio demo mounted at /demo") +``` + +### Hugging Face Spaces Dockerfile + +```dockerfile +FROM python:3.11-slim + +WORKDIR /app + +RUN pip install --no-cache-dir uv +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +COPY src/ ./src/ +COPY models/ ./models/ + +EXPOSE 7860 + +CMD ["uv", "run", "python", "-m", "src.app", "--server-name", "0.0.0.0", "--server-port", "7860"] +``` + +### Hugging Face Spaces app.py entry point + +```python +"""Hugging Face Spaces entry point.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger + +from src.demo import build_detection_demo +from src.config import DetectionConfig + + +logger.info("Starting Hugging Face Spaces demo") +config = DetectionConfig() +demo = build_detection_demo(config) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", server_port=7860) +``` + +## Integration with PyTorch Models + +### PyTorch Lightning Model in Gradio + +```python +"""Serve a PyTorch Lightning model through Gradio.""" + +from __future__ import annotations + +from pathlib import Path + +import gradio as gr +import torch +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field +from torchvision import transforms + + +class TorchModelConfig(BaseModel, frozen=True): + """PyTorch model serving configuration.""" + + checkpoint_path: Path = Path("checkpoints/best.ckpt") + device: str = "cuda" if torch.cuda.is_available() else "cpu" + input_size: tuple[int, int] = (224, 224) + class_names: list[str] = Field(default_factory=list) + + +def load_pytorch_model(config: TorchModelConfig) -> torch.nn.Module: + """Load a PyTorch model from checkpoint.""" + logger.info("Loading PyTorch model from {}", config.checkpoint_path) + model = torch.load(config.checkpoint_path, map_location=config.device) + model.eval() + logger.info("Model loaded on device: {}", config.device) + return model + + +def create_pytorch_demo(config: TorchModelConfig | None = None) -> gr.Blocks: + """Create a Gradio demo for a PyTorch model.""" + config = config or TorchModelConfig() + model = load_pytorch_model(config) + + transform = transforms.Compose([ + transforms.Resize(config.input_size), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + @torch.inference_mode() + def predict(image: Image.Image) -> dict[str, float]: + """Run inference on a single image.""" + tensor = transform(image).unsqueeze(0).to(config.device) + output = model(tensor) + probabilities = torch.nn.functional.softmax(output[0], dim=0) + + top5_prob, top5_idx = torch.topk(probabilities, 5) + results = {} + for prob, idx in zip(top5_prob, top5_idx): + label = config.class_names[idx] if config.class_names else f"class_{idx}" + results[label] = float(prob) + + logger.info("Top prediction: {} ({:.3f})", list(results.keys())[0], list(results.values())[0]) + return results + + with gr.Blocks(title="PyTorch Model Demo") as demo: + gr.Markdown("# PyTorch Model Demo") + with gr.Row(): + input_img = gr.Image(type="pil", label="Upload Image") + output_label = gr.Label(num_top_classes=5, label="Predictions") + predict_btn = gr.Button("Classify", variant="primary") + predict_btn.click(fn=predict, inputs=[input_img], outputs=[output_label]) + + return demo +``` + +## Anti-Patterns + +- **Never load models inside Gradio callback functions** -- load models once at startup and reference them via closure or a server class. Reloading on every request causes massive latency. +- **Never use `gr.Interface` for complex multi-step workflows** -- use `gr.Blocks` with explicit layout control for anything beyond a single input-output function. +- **Never expose raw tensors or numpy arrays to Gradio outputs** -- always convert to PIL Images, JSON-serializable dicts, or strings before returning. +- **Never hardcode file paths in Gradio demos** -- use Pydantic config models to make paths configurable for different environments. +- **Never skip input validation** -- validate image sizes, file types, and parameter ranges before feeding data to the model. +- **Never use `share=True` in production deployments** -- use proper hosting (Hugging Face Spaces, Docker, or a reverse proxy) instead of Gradio's temporary share links. +- **Never block the main thread with long-running inference** -- use `gr.Progress` for progress tracking and consider async patterns for heavy workloads. +- **Never ignore logging** -- use Loguru for structured logging in all prediction functions, model loading, and error handling. + +## Integration with Other Skills + +- **Pydantic Strict** -- All configuration models follow frozen BaseModel patterns with validators. +- **Loguru** -- Structured logging in model loading, prediction functions, and flagging callbacks. +- **Hugging Face** -- Load pretrained models from the Hub and deploy demos to Hugging Face Spaces. +- **FastAPI** -- Mount Gradio demos inside FastAPI applications for combined API + demo serving. +- **ONNX** -- Serve optimized ONNX models through Gradio for low-latency inference. +- **PyTorch Lightning** -- Load Lightning checkpoints and serve trained models through Gradio. +- **Testing** -- Test Gradio components with the Gradio test client and pytest. diff --git a/skills/gradio/skill.toml b/skills/gradio/skill.toml new file mode 100644 index 0000000..8889988 --- /dev/null +++ b/skills/gradio/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "gradio" +version = "1.0.0" +category = "infra" +tags = ["demo", "ui", "gradio", "prototyping", "serving"] + +[dependencies] +requires = ["pydantic-strict", "loguru"] +recommends = ["huggingface", "fastapi"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +gradio = ">=4.0" diff --git a/skills/huggingface/README.md b/skills/huggingface/README.md new file mode 100644 index 0000000..0c64d9b --- /dev/null +++ b/skills/huggingface/README.md @@ -0,0 +1,34 @@ +# Hugging Face Skill + +The Hugging Face Skill provides expert patterns for working with the Hugging Face ecosystem — Transformers, Datasets, Tokenizers, PEFT, and the Model Hub. It covers pretrained model loading with AutoModel classes, dataset preprocessing, fine-tuning with the Trainer API, parameter-efficient fine-tuning with LoRA, pipeline inference, model publishing, and quantization for optimized deployment. + +## Purpose + +When you need to use pretrained models for vision or NLP tasks — whether for classification, detection, zero-shot learning, or fine-tuning on custom data — the Hugging Face ecosystem is the standard toolkit. This skill encodes best practices for loading models safely with version pinning, preprocessing datasets efficiently with batched mapping, fine-tuning with proper evaluation metrics and early stopping, and publishing models with complete model cards. + +## When to Use + +- When loading pretrained vision models (ResNet, ViT, DETR, CLIP) or text models (BERT, RoBERTa). +- When fine-tuning models on custom datasets with the Trainer API. +- When using PEFT/LoRA for parameter-efficient adaptation with limited data. +- When building quick inference pipelines for classification, detection, or zero-shot tasks. +- When publishing models and datasets to the Hugging Face Hub. +- When quantizing models to 4-bit or 8-bit for faster inference. + +## Key Features + +- **AutoModel loading** — type-safe model loading with revision pinning and device mapping. +- **Datasets library** — efficient loading, preprocessing, and batched tokenization. +- **Trainer API** — standardized fine-tuning with evaluation, early stopping, and checkpoint management. +- **PEFT / LoRA** — parameter-efficient fine-tuning that trains <1% of parameters. +- **Pipeline API** — quick inference for image classification, object detection, and zero-shot tasks. +- **Hub publishing** — model upload with model cards, metrics, and licensing. +- **Quantization** — 4-bit and 8-bit model loading for memory-efficient inference. + +## Related Skills + +- **[PyTorch Lightning](../pytorch-lightning/)** — custom training loops wrapping HF models in LightningModule. +- **[ONNX](../onnx/)** — export HF models for optimized inference with ONNX Runtime. +- **[W&B](../wandb/)** — experiment tracking integrated with HF Trainer via `report_to`. +- **[AWS SageMaker](../aws-sagemaker/)** — deploy HF models on SageMaker with HF Deep Learning Containers. +- **[FastAPI](../fastapi/)** — serve HF pipelines behind async REST endpoints. diff --git a/skills/huggingface/SKILL.md b/skills/huggingface/SKILL.md new file mode 100644 index 0000000..ab96597 --- /dev/null +++ b/skills/huggingface/SKILL.md @@ -0,0 +1,530 @@ +--- +name: huggingface +description: > + Hugging Face ecosystem patterns for NLP and vision. Covers Transformers + models, datasets library, tokenizers, pipelines, fine-tuning with Trainer, + PEFT/LoRA adapters, model hub publishing, and inference optimization. +--- + +# Hugging Face Skill + +You are working with the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Follow these patterns exactly. + +## Core Philosophy + +Hugging Face provides a unified API for loading, fine-tuning, and deploying pretrained models. Use `AutoModel` and `AutoTokenizer` classes for model loading — never hardcode model class names unless absolutely necessary. Use the `datasets` library for all data loading and preprocessing. Use the `Trainer` API for fine-tuning unless you need custom training loops. + +## Model Loading + +### AutoModel Pattern + +```python +"""Loading pretrained models with Hugging Face Transformers.""" + +from __future__ import annotations + +import torch +from transformers import ( + AutoConfig, + AutoModel, + AutoModelForImageClassification, + AutoModelForObjectDetection, + AutoModelForSequenceClassification, + AutoTokenizer, + AutoImageProcessor, +) +from pydantic import BaseModel, Field +from loguru import logger + + +class ModelConfig(BaseModel, frozen=True): + """Hugging Face model configuration.""" + + model_name: str = "microsoft/resnet-50" + revision: str = "main" + torch_dtype: str = "float32" + device_map: str | None = None + trust_remote_code: bool = False + cache_dir: str | None = None + + +def load_vision_model(config: ModelConfig) -> tuple: + """Load a vision model and its image processor.""" + logger.info("Loading model: {}", config.model_name) + + processor = AutoImageProcessor.from_pretrained( + config.model_name, + revision=config.revision, + cache_dir=config.cache_dir, + ) + + model = AutoModelForImageClassification.from_pretrained( + config.model_name, + revision=config.revision, + torch_dtype=getattr(torch, config.torch_dtype), + device_map=config.device_map, + trust_remote_code=config.trust_remote_code, + cache_dir=config.cache_dir, + ) + + logger.info("Model loaded: {} parameters", sum(p.numel() for p in model.parameters())) + return model, processor + + +def load_text_model(config: ModelConfig) -> tuple: + """Load a text model and tokenizer.""" + tokenizer = AutoTokenizer.from_pretrained( + config.model_name, + revision=config.revision, + cache_dir=config.cache_dir, + ) + + model = AutoModelForSequenceClassification.from_pretrained( + config.model_name, + revision=config.revision, + torch_dtype=getattr(torch, config.torch_dtype), + device_map=config.device_map, + cache_dir=config.cache_dir, + ) + + return model, tokenizer +``` + +## Datasets Library + +### Loading and Processing Datasets + +```python +"""Dataset loading and preprocessing with Hugging Face datasets.""" + +from __future__ import annotations + +from datasets import Dataset, DatasetDict, load_dataset +from transformers import AutoImageProcessor, AutoTokenizer +from loguru import logger + + +def load_image_dataset( + dataset_name: str, + processor: AutoImageProcessor, + split: str | None = None, +) -> DatasetDict | Dataset: + """Load and preprocess an image classification dataset.""" + dataset = load_dataset(dataset_name, split=split) + logger.info("Loaded dataset: {} rows", len(dataset) if isinstance(dataset, Dataset) else sum(len(s) for s in dataset.values())) + + def preprocess(batch: dict) -> dict: + images = batch["image"] + inputs = processor(images=images, return_tensors="pt") + inputs["labels"] = batch["label"] + return inputs + + processed = dataset.map( + preprocess, + batched=True, + batch_size=32, + remove_columns=dataset.column_names if isinstance(dataset, Dataset) else dataset["train"].column_names, + ) + + processed.set_format("torch") + return processed + + +def load_text_dataset( + dataset_name: str, + tokenizer: AutoTokenizer, + max_length: int = 512, +) -> DatasetDict: + """Load and tokenize a text dataset.""" + dataset = load_dataset(dataset_name) + + def tokenize(batch: dict) -> dict: + return tokenizer( + batch["text"], + padding="max_length", + truncation=True, + max_length=max_length, + return_tensors="pt", + ) + + tokenized = dataset.map(tokenize, batched=True, remove_columns=["text"]) + tokenized.set_format("torch") + return tokenized + + +def create_custom_dataset( + data_dir: str, + image_column: str = "image", + label_column: str = "label", +) -> Dataset: + """Create a dataset from a local directory of images.""" + dataset = load_dataset( + "imagefolder", + data_dir=data_dir, + ) + logger.info("Created dataset from {}: {} images", data_dir, len(dataset["train"])) + return dataset["train"] +``` + +## Fine-Tuning with Trainer + +### Standard Trainer Configuration + +```python +"""Fine-tuning with Hugging Face Trainer API.""" + +from __future__ import annotations + +from pathlib import Path + +import evaluate +import numpy as np +from pydantic import BaseModel, Field +from transformers import ( + AutoModelForImageClassification, + AutoImageProcessor, + EarlyStoppingCallback, + Trainer, + TrainingArguments, +) +from loguru import logger + + +class FinetuneConfig(BaseModel, frozen=True): + """Fine-tuning configuration.""" + + model_name: str = "microsoft/resnet-50" + output_dir: str = "outputs/finetuned" + num_train_epochs: int = 10 + per_device_train_batch_size: int = 32 + per_device_eval_batch_size: int = 64 + learning_rate: float = 5e-5 + weight_decay: float = 0.01 + warmup_ratio: float = 0.1 + fp16: bool = True + eval_strategy: str = "epoch" + save_strategy: str = "epoch" + load_best_model_at_end: bool = True + metric_for_best_model: str = "accuracy" + push_to_hub: bool = False + hub_model_id: str | None = None + + +def create_training_args(config: FinetuneConfig) -> TrainingArguments: + """Create TrainingArguments from config.""" + return TrainingArguments( + output_dir=config.output_dir, + num_train_epochs=config.num_train_epochs, + per_device_train_batch_size=config.per_device_train_batch_size, + per_device_eval_batch_size=config.per_device_eval_batch_size, + learning_rate=config.learning_rate, + weight_decay=config.weight_decay, + warmup_ratio=config.warmup_ratio, + fp16=config.fp16, + eval_strategy=config.eval_strategy, + save_strategy=config.save_strategy, + load_best_model_at_end=config.load_best_model_at_end, + metric_for_best_model=config.metric_for_best_model, + push_to_hub=config.push_to_hub, + hub_model_id=config.hub_model_id, + logging_steps=50, + dataloader_num_workers=4, + dataloader_pin_memory=True, + report_to=["tensorboard"], + ) + + +def compute_metrics(eval_pred) -> dict[str, float]: + """Compute accuracy for evaluation.""" + accuracy = evaluate.load("accuracy") + predictions, labels = eval_pred + predicted = np.argmax(predictions, axis=1) + return accuracy.compute(predictions=predicted, references=labels) + + +def finetune( + config: FinetuneConfig, + train_dataset, + eval_dataset, + num_labels: int, +) -> str: + """Fine-tune a pretrained model.""" + logger.info("Fine-tuning {} for {} epochs", config.model_name, config.num_train_epochs) + + model = AutoModelForImageClassification.from_pretrained( + config.model_name, + num_labels=num_labels, + ignore_mismatched_sizes=True, + ) + + training_args = create_training_args(config) + + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + compute_metrics=compute_metrics, + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], + ) + + trainer.train() + + # Save final model + output_path = Path(config.output_dir) / "best" + trainer.save_model(str(output_path)) + logger.info("Best model saved to {}", output_path) + + return str(output_path) +``` + +## PEFT / LoRA + +### Parameter-Efficient Fine-Tuning + +```python +"""PEFT fine-tuning with LoRA adapters.""" + +from __future__ import annotations + +from peft import ( + LoraConfig, + TaskType, + get_peft_model, + PeftModel, +) +from transformers import AutoModelForSequenceClassification +from loguru import logger + + +def create_lora_model( + model_name: str, + num_labels: int, + lora_r: int = 16, + lora_alpha: int = 32, + lora_dropout: float = 0.1, + target_modules: list[str] | None = None, +) -> PeftModel: + """Create a LoRA-adapted model for fine-tuning.""" + base_model = AutoModelForSequenceClassification.from_pretrained( + model_name, + num_labels=num_labels, + ) + + lora_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=lora_r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + target_modules=target_modules or ["query", "value"], + bias="none", + ) + + model = get_peft_model(base_model, lora_config) + + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = sum(p.numel() for p in model.parameters()) + logger.info( + "LoRA model: {:.1f}M trainable / {:.1f}M total ({:.1f}%)", + trainable / 1e6, + total / 1e6, + 100 * trainable / total, + ) + + return model + + +def load_lora_model( + base_model_name: str, + adapter_path: str, + num_labels: int, +) -> PeftModel: + """Load a saved LoRA adapter on top of a base model.""" + base_model = AutoModelForSequenceClassification.from_pretrained( + base_model_name, + num_labels=num_labels, + ) + model = PeftModel.from_pretrained(base_model, adapter_path) + return model +``` + +## Pipelines for Quick Inference + +### Using Pipeline API + +```python +"""Hugging Face pipeline patterns for quick inference.""" + +from __future__ import annotations + +from transformers import pipeline + + +def create_image_classifier( + model_name: str = "microsoft/resnet-50", + device: int = 0, +) -> pipeline: + """Create an image classification pipeline.""" + return pipeline( + "image-classification", + model=model_name, + device=device, + ) + + +def create_object_detector( + model_name: str = "facebook/detr-resnet-50", + device: int = 0, + threshold: float = 0.5, +) -> pipeline: + """Create an object detection pipeline.""" + return pipeline( + "object-detection", + model=model_name, + device=device, + threshold=threshold, + ) + + +def create_zero_shot_classifier( + model_name: str = "openai/clip-vit-base-patch32", + device: int = 0, +) -> pipeline: + """Create a zero-shot image classification pipeline.""" + return pipeline( + "zero-shot-image-classification", + model=model_name, + device=device, + ) + + +# Usage +classifier = create_image_classifier() +results = classifier("path/to/image.jpg") +# [{"label": "cat", "score": 0.97}, {"label": "dog", "score": 0.02}] + +detector = create_object_detector() +results = detector("path/to/image.jpg") +# [{"label": "person", "score": 0.99, "box": {"xmin": 10, "ymin": 20, "xmax": 200, "ymax": 400}}] +``` + +## Model Hub Publishing + +### Pushing Models to the Hub + +```python +"""Publishing models and datasets to Hugging Face Hub.""" + +from __future__ import annotations + +from pathlib import Path + +from huggingface_hub import HfApi, ModelCard, ModelCardData +from transformers import AutoModel, AutoTokenizer +from loguru import logger + + +def push_model_to_hub( + model_path: str, + repo_id: str, + private: bool = True, +) -> str: + """Push a trained model to Hugging Face Hub.""" + model = AutoModel.from_pretrained(model_path) + model.push_to_hub(repo_id, private=private) + + # Push tokenizer/processor if present + try: + tokenizer = AutoTokenizer.from_pretrained(model_path) + tokenizer.push_to_hub(repo_id, private=private) + except Exception: + pass + + logger.info("Model pushed to hub: {}", repo_id) + return f"https://huggingface.co/{repo_id}" + + +def create_model_card( + repo_id: str, + model_name: str, + dataset_name: str, + metrics: dict[str, float], + language: str = "en", +) -> ModelCard: + """Create and push a model card.""" + card_data = ModelCardData( + language=language, + license="apache-2.0", + model_name=model_name, + datasets=[dataset_name], + metrics=list(metrics.keys()), + ) + + card = ModelCard.from_template( + card_data, + model_id=repo_id, + model_description=f"Fine-tuned {model_name} on {dataset_name}.", + training_metrics=metrics, + ) + + card.push_to_hub(repo_id) + logger.info("Model card pushed to: {}", repo_id) + return card +``` + +## Quantization and Optimization + +### Model Quantization for Faster Inference + +```python +"""Model quantization with Hugging Face Optimum.""" + +from __future__ import annotations + +import torch +from transformers import AutoModelForImageClassification, BitsAndBytesConfig +from loguru import logger + + +def load_quantized_model( + model_name: str, + num_labels: int, + load_in_4bit: bool = True, +) -> AutoModelForImageClassification: + """Load a 4-bit or 8-bit quantized model.""" + bnb_config = BitsAndBytesConfig( + load_in_4bit=load_in_4bit, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_use_double_quant=True, + ) if load_in_4bit else BitsAndBytesConfig(load_in_8bit=True) + + model = AutoModelForImageClassification.from_pretrained( + model_name, + num_labels=num_labels, + quantization_config=bnb_config, + device_map="auto", + ) + + logger.info("Loaded quantized model ({} bit)", "4" if load_in_4bit else "8") + return model +``` + +## Anti-Patterns + +- **Never hardcode model class names** — use `AutoModel`, `AutoTokenizer`, `AutoImageProcessor` for flexibility. +- **Never download models inside training loops** — load once at initialization, cache with `cache_dir`. +- **Never skip `revision` parameter** — pin model versions for reproducibility. +- **Never tokenize the full dataset eagerly** — use `dataset.map(batched=True)` with lazy loading. +- **Never fine-tune all parameters when data is limited** — use PEFT/LoRA to reduce overfitting. +- **Never push models to public hub without a model card** — always include metrics, dataset, and license info. +- **Never ignore `trust_remote_code` warnings** — only set to `True` for trusted model sources. + +## Integration with Other Skills + +- **PyTorch Lightning** — Use Lightning Trainer for custom training loops with HF models. +- **W&B** — Pass `report_to=["wandb"]` in TrainingArguments for experiment tracking. +- **ONNX** — Export HF models via `optimum` for optimized inference. +- **AWS SageMaker** — Deploy HF models on SageMaker with the HF DLC (Deep Learning Container). +- **FastAPI** — Serve HF pipelines behind async API endpoints. +- **DVC** — Version datasets downloaded from Hugging Face Hub. diff --git a/skills/huggingface/skill.toml b/skills/huggingface/skill.toml new file mode 100644 index 0000000..bcf14fb --- /dev/null +++ b/skills/huggingface/skill.toml @@ -0,0 +1,17 @@ +[skill] +name = "huggingface" +version = "1.0.0" +category = "cv-ml" +tags = ["transformers", "datasets", "fine-tuning", "pretrained", "nlp", "vision"] + +[dependencies] +requires = ["pydantic-strict", "loguru"] +recommends = ["pytorch-lightning", "wandb", "onnx", "fastapi"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +transformers = ">=4.35" +datasets = ">=2.14" +peft = ">=0.6" diff --git a/skills/hydra-config/SKILL.md b/skills/hydra-config/SKILL.md index 552533b..f31063a 100644 --- a/skills/hydra-config/SKILL.md +++ b/skills/hydra-config/SKILL.md @@ -1,3 +1,11 @@ +--- +name: hydra-config +description: > + Manage complex hierarchical configurations for ML experiments using Hydra. + Covers structured configs, config composition, command-line overrides, multi-run + sweeps, config groups, and Pydantic validation integration. +--- + # Hydra Configuration Skill Use Hydra for managing complex, hierarchical configurations in machine learning and computer vision projects. This skill covers structured configs, config composition, command-line overrides, multi-run sweeps, and integration with Pydantic validation. diff --git a/skills/hydra-config/skill.toml b/skills/hydra-config/skill.toml new file mode 100644 index 0000000..a157fa8 --- /dev/null +++ b/skills/hydra-config/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "hydra-config" +version = "1.0.0" +category = "cv-ml" +tags = ["configuration", "hydra", "omegaconf", "experiment-management"] + +[dependencies] +requires = ["pydantic-strict"] +recommends = ["pytorch-lightning"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +hydra-core = ">=1.3" diff --git a/skills/kubernetes/README.md b/skills/kubernetes/README.md new file mode 100644 index 0000000..4dc5842 --- /dev/null +++ b/skills/kubernetes/README.md @@ -0,0 +1,35 @@ +# Kubernetes Skill + +The Kubernetes Skill provides production-grade deployment patterns for ML inference and training services on Kubernetes clusters. ML workloads have unique orchestration requirements: GPU scheduling with NVIDIA device plugins, long startup times for model loading, large persistent volumes for model weights, autoscaling based on inference throughput, and environment-specific configurations across dev, staging, and production. This skill addresses all of these with opinionated manifests, Helm charts, Kustomize overlays, and operational best practices tailored to computer vision and deep learning workloads. + +The skill covers the full deployment lifecycle: Deployment manifests with GPU resource requests and tolerations, Services and Ingress resources for routing traffic, Horizontal Pod Autoscalers tuned for inference workloads, ConfigMaps and Secrets for model configuration, PersistentVolumeClaims and init containers for model storage, Kubernetes Jobs for training runs, startup/liveness/readiness probes calibrated for slow model loading, namespace organization with resource quotas, and Kustomize overlays for managing environment differences without duplicating YAML. + +## Purpose + +When you need to deploy ML models to Kubernetes -- whether for real-time inference APIs, batch processing jobs, or GPU-accelerated training -- this skill provides the patterns and manifests to do it correctly. It encodes best practices for GPU scheduling, health checking, autoscaling, and multi-environment management that are specific to ML/CV workloads. + +## When to Use + +- When deploying ML inference services to a Kubernetes cluster (GKE, EKS, AKS, or on-premise). +- When you need GPU-accelerated pods with proper NVIDIA device plugin configuration and resource requests. +- When building Helm charts or Kustomize overlays for ML services across multiple environments. +- When configuring autoscaling, health probes, and persistent storage for model serving workloads. +- When orchestrating training jobs on Kubernetes with multi-GPU scheduling. + +## Key Features + +- **GPU resource scheduling** -- proper `nvidia.com/gpu` requests and limits with node selectors and tolerations for GPU node pools. +- **Health probes for ML** -- startup, liveness, and readiness probes calibrated for containers that take minutes to load large models. +- **Horizontal Pod Autoscaler** -- scaling policies tuned for inference workloads with stabilization windows and custom metrics. +- **Helm chart structure** -- complete chart layout with values files per environment for templated deployments. +- **Kustomize overlays** -- base manifests with dev, staging, and prod overlays to avoid YAML duplication. +- **Persistent model storage** -- PVCs and init containers for downloading model weights from object storage. +- **Namespace organization** -- environment separation with resource quotas to prevent GPU monopolization. +- **Training Jobs** -- Kubernetes Job manifests for one-off or scheduled training runs with GPU resources. + +## Related Skills + +- **[Docker CV](../docker-cv/)** -- container images deployed to Kubernetes are built using multi-stage Docker patterns with CUDA support. +- **[FastAPI](../fastapi/)** -- inference APIs running inside Kubernetes pods use FastAPI with health endpoints for probe integration. +- **[GCP](../gcp/)** -- GKE clusters, Artifact Registry for images, and Cloud Storage for model artifacts. +- **[AWS SageMaker](../aws-sagemaker/)** -- alternative managed deployment; Kubernetes provides more control when SageMaker constraints are too rigid. diff --git a/skills/kubernetes/SKILL.md b/skills/kubernetes/SKILL.md new file mode 100644 index 0000000..fcf521f --- /dev/null +++ b/skills/kubernetes/SKILL.md @@ -0,0 +1,776 @@ +--- +name: kubernetes +description: > + Kubernetes deployment patterns for ML inference and training services. Covers + GPU resource scheduling, Helm charts, Kustomize overlays, health probes, + autoscaling, persistent volumes for model storage, and namespace organization + for dev/staging/prod environments. +--- + +# Kubernetes Skill + +Kubernetes deployment patterns for ML inference and training services with GPU scheduling, Helm charts, autoscaling, and environment-specific overlays. + +## Deployment Manifests for ML Services + +Define Deployments with explicit resource requests and GPU scheduling for inference workloads. + +### Inference Deployment + +```yaml +# k8s/base/deployment.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: model-server + labels: + app: model-server + component: inference +spec: + replicas: 2 + selector: + matchLabels: + app: model-server + template: + metadata: + labels: + app: model-server + component: inference + spec: + containers: + - name: model-server + image: registry.example.com/ml-images/inference:v1.2.0 + ports: + - containerPort: 8000 + name: http + resources: + requests: + cpu: "2" + memory: "4Gi" + nvidia.com/gpu: "1" + limits: + cpu: "4" + memory: "8Gi" + nvidia.com/gpu: "1" + envFrom: + - configMapRef: + name: model-config + - secretRef: + name: model-secrets + volumeMounts: + - name: model-storage + mountPath: /models + readOnly: true + livenessProbe: + httpGet: + path: /health + port: http + initialDelaySeconds: 30 + periodSeconds: 15 + timeoutSeconds: 5 + failureThreshold: 3 + readinessProbe: + httpGet: + path: /health/ready + port: http + initialDelaySeconds: 60 + periodSeconds: 10 + timeoutSeconds: 5 + failureThreshold: 5 + startupProbe: + httpGet: + path: /health + port: http + initialDelaySeconds: 10 + periodSeconds: 10 + failureThreshold: 30 + volumes: + - name: model-storage + persistentVolumeClaim: + claimName: model-pvc + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + nodeSelector: + cloud.google.com/gke-accelerator: nvidia-tesla-t4 +``` + +## GPU Resource Requests + +Always set both `requests` and `limits` for `nvidia.com/gpu`. Kubernetes GPU scheduling requires exact counts -- fractional GPUs are not natively supported. + +```yaml +# GPU resource patterns +resources: + requests: + nvidia.com/gpu: "1" # Request exactly 1 GPU + limits: + nvidia.com/gpu: "1" # Must equal requests for GPUs + +# Multi-GPU training pod +resources: + requests: + cpu: "8" + memory: "32Gi" + nvidia.com/gpu: "4" + limits: + cpu: "16" + memory: "64Gi" + nvidia.com/gpu: "4" +``` + +### NVIDIA Device Plugin + +The NVIDIA device plugin must be deployed in the cluster to enable GPU scheduling. + +```bash +# Install the NVIDIA device plugin via Helm +helm repo add nvdp https://nvidia.github.io/k8s-device-plugin +helm repo update + +helm install nvidia-device-plugin nvdp/nvidia-device-plugin \ + --namespace kube-system \ + --set runtimeClassName=nvidia +``` + +## Service and Ingress Configuration + +### Service + +```yaml +# k8s/base/service.yaml +apiVersion: v1 +kind: Service +metadata: + name: model-server + labels: + app: model-server +spec: + type: ClusterIP + ports: + - port: 80 + targetPort: http + protocol: TCP + name: http + selector: + app: model-server +``` + +### Ingress + +```yaml +# k8s/base/ingress.yaml +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: model-server + annotations: + nginx.ingress.kubernetes.io/proxy-body-size: "50m" + nginx.ingress.kubernetes.io/proxy-read-timeout: "120" + cert-manager.io/cluster-issuer: letsencrypt-prod +spec: + ingressClassName: nginx + tls: + - hosts: + - api.ml.example.com + secretName: model-server-tls + rules: + - host: api.ml.example.com + http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: model-server + port: + name: http +``` + +## Horizontal Pod Autoscaler + +Scale inference pods based on CPU, memory, or custom metrics such as request latency or GPU utilization. + +```yaml +# k8s/base/hpa.yaml +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: model-server +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: model-server + minReplicas: 2 + maxReplicas: 10 + behavior: + scaleUp: + stabilizationWindowSeconds: 60 + policies: + - type: Pods + value: 2 + periodSeconds: 60 + scaleDown: + stabilizationWindowSeconds: 300 + policies: + - type: Pods + value: 1 + periodSeconds: 120 + metrics: + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: 70 + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: 80 + - type: Pods + pods: + metric: + name: inference_requests_per_second + target: + type: AverageValue + averageValue: "100" +``` + +## ConfigMaps and Secrets for Model Config + +### ConfigMap + +```yaml +# k8s/base/configmap.yaml +apiVersion: v1 +kind: ConfigMap +metadata: + name: model-config +data: + MODEL_NAME: "resnet50" + MODEL_VERSION: "v1.2.0" + MODEL_PATH: "/models/resnet50_v1.2.0.onnx" + BATCH_SIZE: "8" + NUM_WORKERS: "4" + LOG_LEVEL: "INFO" + CONFIDENCE_THRESHOLD: "0.5" +``` + +### Secret + +```yaml +# k8s/base/secret.yaml +apiVersion: v1 +kind: Secret +metadata: + name: model-secrets +type: Opaque +stringData: + WANDB_API_KEY: "" # Populated via Kustomize or sealed-secrets + S3_ACCESS_KEY: "" + S3_SECRET_KEY: "" +``` + +```bash +# Create secrets from command line (never commit plaintext secrets) +kubectl create secret generic model-secrets \ + --from-literal=WANDB_API_KEY="${WANDB_API_KEY}" \ + --from-literal=S3_ACCESS_KEY="${S3_ACCESS_KEY}" \ + --from-literal=S3_SECRET_KEY="${S3_SECRET_KEY}" \ + --namespace=ml-inference +``` + +## Persistent Volumes for Model Storage + +Use PersistentVolumeClaims to store large model weights independently of pod lifecycle. + +```yaml +# k8s/base/pvc.yaml +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: model-pvc +spec: + accessModes: + - ReadOnlyMany + storageClassName: standard + resources: + requests: + storage: 50Gi +``` + +### Init Container to Download Models + +```yaml +# Use an init container to pull model weights before the main container starts +spec: + initContainers: + - name: model-downloader + image: google/cloud-sdk:slim + command: + - gsutil + - cp + - gs://my-ml-bucket/models/resnet50_v1.2.0.onnx + - /models/resnet50_v1.2.0.onnx + volumeMounts: + - name: model-storage + mountPath: /models + containers: + - name: model-server + image: registry.example.com/ml-images/inference:v1.2.0 + volumeMounts: + - name: model-storage + mountPath: /models + readOnly: true + volumes: + - name: model-storage + emptyDir: + sizeLimit: 50Gi +``` + +## Helm Chart Patterns + +Organize Kubernetes manifests into a Helm chart for templated, reusable deployments. + +### Chart Structure + +``` +helm/model-server/ +├── Chart.yaml +├── values.yaml +├── values-dev.yaml +├── values-staging.yaml +├── values-prod.yaml +└── templates/ + ├── _helpers.tpl + ├── deployment.yaml + ├── service.yaml + ├── ingress.yaml + ├── hpa.yaml + ├── configmap.yaml + ├── secret.yaml + ├── pvc.yaml + └── NOTES.txt +``` + +### values.yaml + +```yaml +# helm/model-server/values.yaml +replicaCount: 2 + +image: + repository: registry.example.com/ml-images/inference + tag: "v1.2.0" + pullPolicy: IfNotPresent + +model: + name: resnet50 + version: v1.2.0 + path: /models/resnet50_v1.2.0.onnx + storageSizeGi: 50 + +resources: + requests: + cpu: "2" + memory: "4Gi" + nvidia.com/gpu: "1" + limits: + cpu: "4" + memory: "8Gi" + nvidia.com/gpu: "1" + +autoscaling: + enabled: true + minReplicas: 2 + maxReplicas: 10 + targetCPUUtilization: 70 + +ingress: + enabled: true + host: api.ml.example.com + tls: true + +probes: + liveness: + path: /health + initialDelaySeconds: 30 + periodSeconds: 15 + readiness: + path: /health/ready + initialDelaySeconds: 60 + periodSeconds: 10 + startup: + path: /health + initialDelaySeconds: 10 + failureThreshold: 30 + +nodeSelector: + cloud.google.com/gke-accelerator: nvidia-tesla-t4 + +tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule +``` + +### Helm Deployment Commands + +```bash +# Install the chart +helm install model-server ./helm/model-server \ + --namespace ml-inference \ + --create-namespace \ + --values helm/model-server/values-prod.yaml + +# Upgrade with new image tag +helm upgrade model-server ./helm/model-server \ + --namespace ml-inference \ + --set image.tag=v1.3.0 + +# Rollback to previous release +helm rollback model-server 1 --namespace ml-inference + +# Dry-run to preview rendered templates +helm template model-server ./helm/model-server \ + --values helm/model-server/values-staging.yaml \ + --debug +``` + +## Health Checks (Liveness, Readiness, and Startup Probes) + +ML containers need long startup times for model loading. Use all three probe types. + +```yaml +# Startup probe: allow up to 5 minutes for model loading (10s * 30 attempts) +startupProbe: + httpGet: + path: /health + port: 8000 + initialDelaySeconds: 10 + periodSeconds: 10 + failureThreshold: 30 + +# Liveness probe: restart if unresponsive +livenessProbe: + httpGet: + path: /health + port: 8000 + initialDelaySeconds: 0 + periodSeconds: 15 + timeoutSeconds: 5 + failureThreshold: 3 + +# Readiness probe: remove from service if not ready +readinessProbe: + httpGet: + path: /health/ready + port: 8000 + initialDelaySeconds: 0 + periodSeconds: 10 + timeoutSeconds: 5 + failureThreshold: 5 +``` + +### FastAPI Health Endpoints + +```python +from fastapi import FastAPI, Response, status + +app = FastAPI() + +model_loaded = False + + +@app.get("/health") +def health() -> dict[str, str]: + """Liveness probe -- is the process alive?""" + return {"status": "alive"} + + +@app.get("/health/ready") +def readiness(response: Response) -> dict[str, str]: + """Readiness probe -- is the model loaded and ready for inference?""" + if not model_loaded: + response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE + return {"status": "not_ready", "reason": "model loading"} + return {"status": "ready"} +``` + +## Namespace Organization + +Separate environments and workload types using namespaces with resource quotas. + +```bash +# Create namespaces for each environment +kubectl create namespace ml-dev +kubectl create namespace ml-staging +kubectl create namespace ml-prod + +# Create namespace for training jobs +kubectl create namespace ml-training +``` + +### Resource Quotas per Namespace + +```yaml +# k8s/namespaces/ml-prod-quota.yaml +apiVersion: v1 +kind: ResourceQuota +metadata: + name: gpu-quota + namespace: ml-prod +spec: + hard: + requests.nvidia.com/gpu: "8" + limits.nvidia.com/gpu: "8" + requests.cpu: "32" + requests.memory: "128Gi" + persistentvolumeclaims: "20" +``` + +## Kustomize Overlays for Dev/Staging/Prod + +Use Kustomize to manage environment-specific variations without duplicating manifests. + +### Directory Structure + +``` +k8s/ +├── base/ +│ ├── kustomization.yaml +│ ├── deployment.yaml +│ ├── service.yaml +│ ├── ingress.yaml +│ ├── hpa.yaml +│ ├── configmap.yaml +│ └── pvc.yaml +└── overlays/ + ├── dev/ + │ ├── kustomization.yaml + │ └── patches/ + │ ├── deployment-patch.yaml + │ └── hpa-patch.yaml + ├── staging/ + │ ├── kustomization.yaml + │ └── patches/ + │ └── deployment-patch.yaml + └── prod/ + ├── kustomization.yaml + └── patches/ + ├── deployment-patch.yaml + └── hpa-patch.yaml +``` + +### Base Kustomization + +```yaml +# k8s/base/kustomization.yaml +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +resources: + - deployment.yaml + - service.yaml + - ingress.yaml + - hpa.yaml + - configmap.yaml + - pvc.yaml +commonLabels: + app.kubernetes.io/name: model-server + app.kubernetes.io/managed-by: kustomize +``` + +### Dev Overlay + +```yaml +# k8s/overlays/dev/kustomization.yaml +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +namespace: ml-dev +resources: + - ../../base +patches: + - path: patches/deployment-patch.yaml + - path: patches/hpa-patch.yaml +images: + - name: registry.example.com/ml-images/inference + newTag: dev-latest +``` + +```yaml +# k8s/overlays/dev/patches/deployment-patch.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: model-server +spec: + replicas: 1 + template: + spec: + containers: + - name: model-server + resources: + requests: + cpu: "1" + memory: "2Gi" + nvidia.com/gpu: "1" + limits: + cpu: "2" + memory: "4Gi" + nvidia.com/gpu: "1" +``` + +```yaml +# k8s/overlays/dev/patches/hpa-patch.yaml +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: model-server +spec: + minReplicas: 1 + maxReplicas: 2 +``` + +### Prod Overlay + +```yaml +# k8s/overlays/prod/kustomization.yaml +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +namespace: ml-prod +resources: + - ../../base +patches: + - path: patches/deployment-patch.yaml + - path: patches/hpa-patch.yaml +images: + - name: registry.example.com/ml-images/inference + newTag: v1.2.0 +``` + +```yaml +# k8s/overlays/prod/patches/deployment-patch.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: model-server +spec: + replicas: 3 + template: + spec: + containers: + - name: model-server + resources: + requests: + cpu: "4" + memory: "8Gi" + nvidia.com/gpu: "1" + limits: + cpu: "8" + memory: "16Gi" + nvidia.com/gpu: "1" +``` + +### Apply Overlays + +```bash +# Apply dev overlay +kubectl apply -k k8s/overlays/dev/ + +# Apply staging overlay +kubectl apply -k k8s/overlays/staging/ + +# Apply prod overlay +kubectl apply -k k8s/overlays/prod/ + +# Preview rendered manifests without applying +kubectl kustomize k8s/overlays/prod/ +``` + +## Training Jobs with Kubernetes + +Use Kubernetes Jobs for one-off training runs. + +```yaml +# k8s/jobs/training-job.yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: training-resnet50 + namespace: ml-training +spec: + backoffLimit: 2 + ttlSecondsAfterFinished: 86400 + template: + spec: + restartPolicy: OnFailure + containers: + - name: trainer + image: registry.example.com/ml-images/training:v1.2.0 + command: ["python", "-m", "my_project.train"] + args: + - "--config=configs/train.yaml" + - "--epochs=100" + - "--batch-size=32" + resources: + requests: + cpu: "8" + memory: "32Gi" + nvidia.com/gpu: "4" + limits: + cpu: "16" + memory: "64Gi" + nvidia.com/gpu: "4" + volumeMounts: + - name: data + mountPath: /data + readOnly: true + - name: checkpoints + mountPath: /checkpoints + envFrom: + - secretRef: + name: training-secrets + volumes: + - name: data + persistentVolumeClaim: + claimName: training-data-pvc + - name: checkpoints + persistentVolumeClaim: + claimName: checkpoints-pvc + tolerations: + - key: nvidia.com/gpu + operator: Exists + effect: NoSchedule + nodeSelector: + cloud.google.com/gke-accelerator: nvidia-tesla-a100 +``` + +## Anti-Patterns to Avoid + +- Do not omit GPU resource limits -- without explicit `nvidia.com/gpu` limits, pods will not be scheduled on GPU nodes and will silently fall back to CPU. +- Do not use `latest` image tags in production -- always pin to a specific version tag or Git SHA for reproducibility and safe rollbacks. +- Do not skip startup probes for ML containers -- model loading can take minutes; without a startup probe, Kubernetes will kill pods before they are ready. +- Do not store model weights inside container images -- images become multi-gigabyte and slow to pull; use PVCs or init containers to download models. +- Do not set CPU requests too low for GPU pods -- GPU inference still requires CPU for preprocessing; under-provisioned CPU starves the pipeline. +- Do not hardcode environment-specific values in base manifests -- use Kustomize overlays or Helm values files for dev/staging/prod differences. +- Do not run pods as root -- always set `runAsNonRoot: true` in the pod security context. +- Do not skip resource quotas on shared clusters -- without quotas, a single team can monopolize all GPU resources. +- Do not expose inference services without TLS -- always terminate TLS at the Ingress or use a service mesh. +- Do not ignore pod disruption budgets -- set `minAvailable` to prevent all replicas from being evicted during node upgrades. + +## Best Practices + +1. **Always set resource requests and limits** -- GPU, CPU, and memory must all be specified for predictable scheduling. +2. **Use startup probes** -- ML containers need extended initialization time; startup probes prevent premature restarts. +3. **Separate namespaces by environment** -- dev, staging, and prod with resource quotas per namespace. +4. **Use Kustomize or Helm for environment management** -- never duplicate YAML across environments. +5. **Pin image tags** -- use semantic versions or Git SHAs, never `latest` in staging or prod. +6. **Run as non-root** -- set `securityContext.runAsNonRoot: true` on all pods. +7. **Use PVCs for model storage** -- decouple model weights from container images. +8. **Set pod disruption budgets** -- ensure minimum availability during cluster maintenance. +9. **Configure HPA with appropriate metrics** -- scale on request rate or GPU utilization, not just CPU. +10. **Use init containers for model downloads** -- pull weights from object storage before the main container starts. diff --git a/skills/kubernetes/skill.toml b/skills/kubernetes/skill.toml new file mode 100644 index 0000000..e49777c --- /dev/null +++ b/skills/kubernetes/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "kubernetes" +version = "1.0.0" +category = "infra" +tags = ["kubernetes", "k8s", "deployment", "orchestration", "helm"] + +[dependencies] +requires = ["docker-cv"] +recommends = ["fastapi", "gcp", "aws-sagemaker"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/library-review/SKILL.md b/skills/library-review/SKILL.md index 29f3bba..8b4dee7 100644 --- a/skills/library-review/SKILL.md +++ b/skills/library-review/SKILL.md @@ -1,3 +1,11 @@ +--- +name: library-review +description: > + Structured framework for evaluating third-party Python libraries before adoption. + Covers technology radar classification (Adopt/Trial/Assess/Hold), dependency risk + assessment, API wrapping strategies, and security review checklists. +--- + # Library Review and Evaluation Framework ## Overview diff --git a/skills/library-review/skill.toml b/skills/library-review/skill.toml new file mode 100644 index 0000000..df0b044 --- /dev/null +++ b/skills/library-review/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "library-review" +version = "1.0.0" +category = "core" +tags = ["evaluation", "library-selection", "architecture-decisions"] + +[dependencies] +requires = [] +recommends = ["master-skill"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/loguru/SKILL.md b/skills/loguru/SKILL.md index 3e4ae75..c8582c0 100644 --- a/skills/loguru/SKILL.md +++ b/skills/loguru/SKILL.md @@ -1,3 +1,11 @@ +--- +name: loguru +description: > + Structured logging for CV/ML projects using Loguru as a mandatory convention. + Covers sink configuration, structured JSON logging, log levels, context binding, + and replacing stdlib logging and print statements. +--- + # Loguru Skill Structured logging for CV/ML projects using loguru. This is a mandatory project convention — all repositories use loguru instead of stdlib `logging` or `print()`. diff --git a/skills/loguru/skill.toml b/skills/loguru/skill.toml new file mode 100644 index 0000000..06c7966 --- /dev/null +++ b/skills/loguru/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "loguru" +version = "1.0.0" +category = "core" +tags = ["logging", "structured-logging", "observability"] + +[dependencies] +requires = [] +recommends = ["master-skill"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +loguru = ">=0.7" diff --git a/skills/master-skill/SKILL.md b/skills/master-skill/SKILL.md index fa5e305..043e0ec 100644 --- a/skills/master-skill/SKILL.md +++ b/skills/master-skill/SKILL.md @@ -1,6 +1,14 @@ +--- +name: master-skill +description: > + Orchestrates new AI/CV project initialization using the whet framework. + Guides archetype selection, skill composition, directory scaffolding, and initial + configuration for new computer vision and deep learning projects. +--- + # Master Skill: Project Initialization -You are initializing a new AI/CV project using the ai-cv-claude-skills framework. +You are initializing a new AI/CV project using the whet framework. ## Initialization Process diff --git a/skills/master-skill/skill.toml b/skills/master-skill/skill.toml new file mode 100644 index 0000000..6811d69 --- /dev/null +++ b/skills/master-skill/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "master-skill" +version = "1.0.0" +category = "core" +tags = ["conventions", "python", "coding-standards"] + +[dependencies] +requires = [] +recommends = ["pydantic-strict", "code-quality", "loguru"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/matplotlib/SKILL.md b/skills/matplotlib/SKILL.md index 860708f..8e1d0b8 100644 --- a/skills/matplotlib/SKILL.md +++ b/skills/matplotlib/SKILL.md @@ -1,3 +1,11 @@ +--- +name: matplotlib +description: > + Comprehensive visualization patterns for ML and computer vision using Matplotlib. + Covers training curves, confusion matrices, image grids, bounding box overlays, + feature maps, publication-quality figures, and experiment tracker integration. +--- + # Matplotlib Skill Comprehensive visualization patterns for ML and computer vision projects. This skill covers training curves, confusion matrices, image grids, bounding box overlays, feature maps, dimensionality reduction plots, publication-quality figures, and integration with experiment tracking. diff --git a/skills/matplotlib/skill.toml b/skills/matplotlib/skill.toml new file mode 100644 index 0000000..aee91dd --- /dev/null +++ b/skills/matplotlib/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "matplotlib" +version = "1.0.0" +category = "cv-ml" +tags = ["visualization", "plotting", "charts", "figures"] + +[dependencies] +requires = [] +recommends = ["opencv"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +matplotlib = ">=3.8" diff --git a/skills/mlflow/SKILL.md b/skills/mlflow/SKILL.md index 37ca4a0..79c4709 100644 --- a/skills/mlflow/SKILL.md +++ b/skills/mlflow/SKILL.md @@ -1,3 +1,11 @@ +--- +name: mlflow +description: > + MLflow integration for experiment tracking, model registry, and model serving in + ML projects. Covers self-hosted setup, metric logging, artifact management, + model versioning, and opt-in integration patterns. +--- + # MLflow Tracking Integration for ML Projects ## Overview diff --git a/skills/mlflow/skill.toml b/skills/mlflow/skill.toml new file mode 100644 index 0000000..d10e54c --- /dev/null +++ b/skills/mlflow/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "mlflow" +version = "1.0.0" +category = "experiment-tracking" +tags = ["experiment-tracking", "model-registry", "mlflow", "deployment"] + +[dependencies] +requires = ["loguru"] +recommends = ["pytorch-lightning"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +mlflow = ">=2.10" diff --git a/skills/onnx/SKILL.md b/skills/onnx/SKILL.md index 7a12f58..7217147 100644 --- a/skills/onnx/SKILL.md +++ b/skills/onnx/SKILL.md @@ -1,3 +1,11 @@ +--- +name: onnx +description: > + Export PyTorch models to ONNX format for optimized inference with ONNX Runtime. + Covers dynamic axes configuration, model optimization, graph surgery, validation + against PyTorch outputs, and production deployment patterns. +--- + # ONNX Model Export and Inference ## Overview diff --git a/skills/onnx/skill.toml b/skills/onnx/skill.toml new file mode 100644 index 0000000..44a8865 --- /dev/null +++ b/skills/onnx/skill.toml @@ -0,0 +1,16 @@ +[skill] +name = "onnx" +version = "1.0.0" +category = "cv-ml" +tags = ["model-export", "inference", "onnx", "optimization"] + +[dependencies] +requires = ["loguru"] +recommends = ["tensorrt", "pytorch-lightning"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +onnx = ">=1.15" +onnxruntime = ">=1.17" diff --git a/skills/opencv/SKILL.md b/skills/opencv/SKILL.md index b7029e9..8811b2d 100644 --- a/skills/opencv/SKILL.md +++ b/skills/opencv/SKILL.md @@ -1,3 +1,11 @@ +--- +name: opencv +description: > + Type-safe OpenCV abstractions for computer vision projects. Covers video I/O, + image processing wrappers, drawing utilities, color space conversions, camera + capture, and clean API design over OpenCV's C-style interface. +--- + # OpenCV Skill Comprehensive OpenCV abstractions for computer vision projects. This skill provides clean, type-safe wrappers around OpenCV's C-style API, covering video reading/writing, image abstractions, drawing utilities, color space conversions, and camera capture. diff --git a/skills/opencv/skill.toml b/skills/opencv/skill.toml new file mode 100644 index 0000000..4463509 --- /dev/null +++ b/skills/opencv/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "opencv" +version = "1.0.0" +category = "cv-ml" +tags = ["computer-vision", "image-processing", "video", "opencv"] + +[dependencies] +requires = ["abstraction-patterns"] +recommends = ["matplotlib"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +opencv-python = ">=4.8" diff --git a/skills/pixi/SKILL.md b/skills/pixi/SKILL.md index a3bb03e..6c50fca 100644 --- a/skills/pixi/SKILL.md +++ b/skills/pixi/SKILL.md @@ -1,3 +1,11 @@ +--- +name: pixi +description: > + Manage Python project environments and dependencies using Pixi. Covers pixi.toml + configuration, conda/PyPI dependency management, task definitions, lock files, + cross-platform environments, and CUDA toolkit setup. +--- + # Pixi Skill You are managing Python project environments and dependencies using pixi. Follow these patterns exactly. diff --git a/skills/pixi/skill.toml b/skills/pixi/skill.toml new file mode 100644 index 0000000..3eda6ab --- /dev/null +++ b/skills/pixi/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "pixi" +version = "1.0.0" +category = "infra" +tags = ["package-management", "environment", "conda", "pixi"] + +[dependencies] +requires = [] +recommends = ["code-quality"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/pre-commit/SKILL.md b/skills/pre-commit/SKILL.md index 206bed7..b507706 100644 --- a/skills/pre-commit/SKILL.md +++ b/skills/pre-commit/SKILL.md @@ -1,3 +1,11 @@ +--- +name: pre-commit +description: > + Configure pre-commit hooks for Python and ML projects to enforce code quality + at commit time. Covers Ruff, MyPy, YAML validation, large file prevention, + secret detection, and CI integration with pre-commit.ci. +--- + # Pre-commit Hooks for Python and ML Projects ## Overview diff --git a/skills/pre-commit/skill.toml b/skills/pre-commit/skill.toml new file mode 100644 index 0000000..378b794 --- /dev/null +++ b/skills/pre-commit/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "pre-commit" +version = "1.0.0" +category = "infra" +tags = ["git-hooks", "automation", "pre-commit", "code-quality"] + +[dependencies] +requires = ["code-quality"] +recommends = ["testing"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/pydantic-strict/SKILL.md b/skills/pydantic-strict/SKILL.md index 6e3f4af..5dbb92c 100644 --- a/skills/pydantic-strict/SKILL.md +++ b/skills/pydantic-strict/SKILL.md @@ -1,3 +1,11 @@ +--- +name: pydantic-strict +description: > + Configuration and data validation using Pydantic V2 with strict typing for AI/CV + projects. Covers frozen models, discriminated unions, custom validators, settings + management, and load-time validation patterns. +--- + # Pydantic Strict Skill You are writing configuration and data validation code using Pydantic V2 with strict typing. Follow these patterns exactly. diff --git a/skills/pydantic-strict/skill.toml b/skills/pydantic-strict/skill.toml new file mode 100644 index 0000000..24ef7a3 --- /dev/null +++ b/skills/pydantic-strict/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "pydantic-strict" +version = "1.0.0" +category = "core" +tags = ["validation", "config", "pydantic", "data-models"] + +[dependencies] +requires = [] +recommends = ["master-skill"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +pydantic = ">=2.0" diff --git a/skills/pypi/SKILL.md b/skills/pypi/SKILL.md index 45bab8b..7e8f819 100644 --- a/skills/pypi/SKILL.md +++ b/skills/pypi/SKILL.md @@ -1,3 +1,11 @@ +--- +name: pypi +description: > + Complete workflow for packaging and publishing Python projects to PyPI. Covers + pyproject.toml configuration, src layout, version management, build system setup, + trusted publishers via GitHub Actions, and TestPyPI staging. +--- + # PyPI Publishing Skill Complete workflow for packaging and publishing Python projects to PyPI. This skill covers `pyproject.toml` configuration, version management, build system setup, src layout, package metadata, entry points, dependency specification, building, publishing to PyPI and TestPyPI, trusted publishers via GitHub Actions, and release automation. diff --git a/skills/pypi/skill.toml b/skills/pypi/skill.toml new file mode 100644 index 0000000..840f44f --- /dev/null +++ b/skills/pypi/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "pypi" +version = "1.0.0" +category = "infra" +tags = ["packaging", "publishing", "pypi", "distribution"] + +[dependencies] +requires = ["code-quality"] +recommends = ["github-actions"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/pytorch-lightning/SKILL.md b/skills/pytorch-lightning/SKILL.md index e338a0c..214e6c9 100644 --- a/skills/pytorch-lightning/SKILL.md +++ b/skills/pytorch-lightning/SKILL.md @@ -1,3 +1,11 @@ +--- +name: pytorch-lightning +description: > + PyTorch Lightning patterns for AI/CV training pipelines. Covers LightningModule + design, LightningDataModule, Trainer configuration, callbacks, logging integration, + distributed training, and checkpoint management. +--- + # PyTorch Lightning Skill You are writing PyTorch Lightning code for AI/CV projects. Follow these patterns exactly. diff --git a/skills/pytorch-lightning/skill.toml b/skills/pytorch-lightning/skill.toml new file mode 100644 index 0000000..eccd97e --- /dev/null +++ b/skills/pytorch-lightning/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "pytorch-lightning" +version = "1.0.0" +category = "cv-ml" +tags = ["training", "deep-learning", "pytorch", "lightning"] + +[dependencies] +requires = ["pydantic-strict", "loguru"] +recommends = ["hydra-config", "wandb"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +pytorch-lightning = ">=2.0" diff --git a/skills/tensorboard/SKILL.md b/skills/tensorboard/SKILL.md index 4b45063..b3207a7 100644 --- a/skills/tensorboard/SKILL.md +++ b/skills/tensorboard/SKILL.md @@ -1,3 +1,11 @@ +--- +name: tensorboard +description: > + TensorBoard visualization and logging for PyTorch ML projects. Covers scalar + metrics, image logging, model graph visualization, histogram tracking, profiling, + and zero-setup local experiment monitoring. +--- + # TensorBoard Logging for ML Projects ## Overview diff --git a/skills/tensorboard/skill.toml b/skills/tensorboard/skill.toml new file mode 100644 index 0000000..28d8997 --- /dev/null +++ b/skills/tensorboard/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "tensorboard" +version = "1.0.0" +category = "experiment-tracking" +tags = ["visualization", "tensorboard", "training-monitoring"] + +[dependencies] +requires = ["loguru"] +recommends = ["pytorch-lightning"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +tensorboard = ">=2.14" diff --git a/skills/tensorrt/SKILL.md b/skills/tensorrt/SKILL.md index 8e7fa2f..48c9314 100644 --- a/skills/tensorrt/SKILL.md +++ b/skills/tensorrt/SKILL.md @@ -1,3 +1,11 @@ +--- +name: tensorrt +description: > + Maximize inference performance on NVIDIA GPUs by converting ONNX models to TensorRT + engines. Covers precision modes (FP16/INT8), dynamic shapes, engine building, + calibration, benchmarking, and Triton Inference Server deployment. +--- + # TensorRT Skill Maximize inference performance on NVIDIA GPUs by converting ONNX models to TensorRT engines. This skill requires the ONNX skill — always export and slim with ONNX first, then convert to TensorRT. diff --git a/skills/tensorrt/skill.toml b/skills/tensorrt/skill.toml new file mode 100644 index 0000000..014dec9 --- /dev/null +++ b/skills/tensorrt/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "tensorrt" +version = "1.0.0" +category = "cv-ml" +tags = ["gpu-inference", "nvidia", "tensorrt", "optimization"] + +[dependencies] +requires = ["onnx"] +recommends = ["docker-cv"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +tensorrt = ">=8.6" diff --git a/skills/testing/SKILL.md b/skills/testing/SKILL.md index 07241cc..19e75ec 100644 --- a/skills/testing/SKILL.md +++ b/skills/testing/SKILL.md @@ -1,3 +1,11 @@ +--- +name: testing +description: > + Comprehensive pytest patterns for ML and computer vision projects. Covers test + structure, fixtures, parametrized tests, CV-specific assertions, tensor shape + validation, mocking, performance testing, and CI integration. +--- + # Testing Skill Comprehensive pytest patterns for ML and computer vision projects. This skill covers test structure, fixtures, parametrized tests, CV-specific testing strategies, mocking, performance testing, and CI integration. diff --git a/skills/testing/skill.toml b/skills/testing/skill.toml new file mode 100644 index 0000000..f416e26 --- /dev/null +++ b/skills/testing/skill.toml @@ -0,0 +1,16 @@ +[skill] +name = "testing" +version = "1.0.0" +category = "core" +tags = ["testing", "pytest", "coverage", "tdd"] + +[dependencies] +requires = ["pydantic-strict"] +recommends = ["code-quality"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +pytest = ">=7.4" +pytest-cov = ">=4.1" diff --git a/skills/vscode/SKILL.md b/skills/vscode/SKILL.md index 77b6dfe..bb3bc0f 100644 --- a/skills/vscode/SKILL.md +++ b/skills/vscode/SKILL.md @@ -1,3 +1,11 @@ +--- +name: vscode +description: > + Configure VS Code for productive computer vision and ML development. Covers + workspace settings, Ruff and MyPy integration, Python debugging configurations, + remote SSH GPU server setup, and recommended extensions. +--- + # VS Code Skill Configure VS Code for productive computer vision and machine learning development with integrated linting, debugging, and remote GPU server support. diff --git a/skills/vscode/skill.toml b/skills/vscode/skill.toml new file mode 100644 index 0000000..17abb8a --- /dev/null +++ b/skills/vscode/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "vscode" +version = "1.0.0" +category = "infra" +tags = ["editor", "ide", "vscode", "configuration"] + +[dependencies] +requires = [] +recommends = ["code-quality"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/wandb/SKILL.md b/skills/wandb/SKILL.md index 1792fc4..23a0746 100644 --- a/skills/wandb/SKILL.md +++ b/skills/wandb/SKILL.md @@ -1,3 +1,11 @@ +--- +name: wandb +description: > + Weights & Biases (W&B) integration for ML experiment tracking and collaboration. + Covers metric logging, artifact management, hyperparameter sweeps, model registry, + dataset versioning, and opt-in graceful degradation patterns. +--- + # Weights & Biases (W&B) Integration for ML Projects ## Overview diff --git a/skills/wandb/skill.toml b/skills/wandb/skill.toml new file mode 100644 index 0000000..6e52c17 --- /dev/null +++ b/skills/wandb/skill.toml @@ -0,0 +1,15 @@ +[skill] +name = "wandb" +version = "1.0.0" +category = "experiment-tracking" +tags = ["experiment-tracking", "visualization", "wandb", "metrics"] + +[dependencies] +requires = ["loguru"] +recommends = ["pytorch-lightning"] + +[compatibility] +python = ">=3.11" + +[compatibility.libraries] +wandb = ">=0.16" diff --git a/src/whet/__init__.py b/src/whet/__init__.py new file mode 100644 index 0000000..2cb7ff7 --- /dev/null +++ b/src/whet/__init__.py @@ -0,0 +1,5 @@ +"""whet — Sharpen your AI coder.""" + +from __future__ import annotations + +__version__ = "0.1.0" diff --git a/src/whet/__main__.py b/src/whet/__main__.py new file mode 100644 index 0000000..22ae91a --- /dev/null +++ b/src/whet/__main__.py @@ -0,0 +1,7 @@ +"""Allow running whet as `python -m whet`.""" + +from __future__ import annotations + +from whet.cli import app + +app() diff --git a/src/whet/adapters/__init__.py b/src/whet/adapters/__init__.py new file mode 100644 index 0000000..6d1956a --- /dev/null +++ b/src/whet/adapters/__init__.py @@ -0,0 +1,3 @@ +"""Platform adapters for installing skills to different AI coding agents.""" + +from __future__ import annotations diff --git a/src/whet/adapters/antigravity.py b/src/whet/adapters/antigravity.py new file mode 100644 index 0000000..6093d9c --- /dev/null +++ b/src/whet/adapters/antigravity.py @@ -0,0 +1,51 @@ +"""Google Antigravity adapter — native SKILL.md support.""" + +from __future__ import annotations + +import shutil +from pathlib import Path + +from whet.core.skill import Skill + + +class AntigravityAdapter: + """Adapter for Google Antigravity. + + Antigravity also uses SKILL.md natively, so this adapter copies + skill directories to the Antigravity skills location. + """ + + @property + def name(self) -> str: + return "antigravity" + + def install_skill(self, skill: Skill, target_dir: Path) -> Path: + """Copy the skill directory to Antigravity's skills location.""" + dest = target_dir / skill.name + dest.mkdir(parents=True, exist_ok=True) + + src_skill_md = skill.skill_md_path + if src_skill_md.exists(): + shutil.copy2(src_skill_md, dest / "SKILL.md") + + src_readme = skill.path / "README.md" + if src_readme.exists(): + shutil.copy2(src_readme, dest / "README.md") + + return dest + + def remove_skill(self, skill_name: str, target_dir: Path) -> bool: + """Remove a skill directory from Antigravity's skills location.""" + skill_dir = target_dir / skill_name + if skill_dir.is_dir(): + shutil.rmtree(skill_dir) + return True + return False + + def list_installed(self, target_dir: Path) -> list[str]: + """List installed skills.""" + if not target_dir.is_dir(): + return [] + return sorted( + d.name for d in target_dir.iterdir() if d.is_dir() and (d / "SKILL.md").exists() + ) diff --git a/src/whet/adapters/base.py b/src/whet/adapters/base.py new file mode 100644 index 0000000..7d53b82 --- /dev/null +++ b/src/whet/adapters/base.py @@ -0,0 +1,35 @@ +"""Base adapter protocol for platform-specific skill installation.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Protocol + +from whet.core.skill import Skill + + +class PlatformAdapter(Protocol): + """Protocol for platform-specific skill installation.""" + + @property + def name(self) -> str: + """Platform name.""" + ... + + def install_skill(self, skill: Skill, target_dir: Path) -> Path: + """Install a skill to the target directory. + + Returns the path where the skill was installed. + """ + ... + + def remove_skill(self, skill_name: str, target_dir: Path) -> bool: + """Remove an installed skill. + + Returns True if the skill was found and removed. + """ + ... + + def list_installed(self, target_dir: Path) -> list[str]: + """List names of installed skills.""" + ... diff --git a/src/whet/adapters/claude.py b/src/whet/adapters/claude.py new file mode 100644 index 0000000..5491a6a --- /dev/null +++ b/src/whet/adapters/claude.py @@ -0,0 +1,53 @@ +"""Claude Code adapter — native SKILL.md support.""" + +from __future__ import annotations + +import shutil +from pathlib import Path + +from whet.core.skill import Skill + + +class ClaudeAdapter: + """Adapter for Claude Code. + + Claude Code natively uses SKILL.md files, so the adapter simply copies + the skill directory to the target location. + """ + + @property + def name(self) -> str: + return "claude" + + def install_skill(self, skill: Skill, target_dir: Path) -> Path: + """Copy the skill directory to Claude's skills location.""" + dest = target_dir / skill.name + dest.mkdir(parents=True, exist_ok=True) + + # Copy SKILL.md (the primary file the LLM reads) + src_skill_md = skill.skill_md_path + if src_skill_md.exists(): + shutil.copy2(src_skill_md, dest / "SKILL.md") + + # Copy README.md if present + src_readme = skill.path / "README.md" + if src_readme.exists(): + shutil.copy2(src_readme, dest / "README.md") + + return dest + + def remove_skill(self, skill_name: str, target_dir: Path) -> bool: + """Remove a skill directory from Claude's skills location.""" + skill_dir = target_dir / skill_name + if skill_dir.is_dir(): + shutil.rmtree(skill_dir) + return True + return False + + def list_installed(self, target_dir: Path) -> list[str]: + """List installed skills by scanning for SKILL.md files.""" + if not target_dir.is_dir(): + return [] + return sorted( + d.name for d in target_dir.iterdir() if d.is_dir() and (d / "SKILL.md").exists() + ) diff --git a/src/whet/adapters/copilot.py b/src/whet/adapters/copilot.py new file mode 100644 index 0000000..8bb4a79 --- /dev/null +++ b/src/whet/adapters/copilot.py @@ -0,0 +1,98 @@ +"""GitHub Copilot adapter — aggregates skills into a single instructions file.""" + +from __future__ import annotations + +from pathlib import Path + +from whet.core.skill import Skill + + +class CopilotAdapter: + """Adapter for GitHub Copilot. + + Copilot uses a single .github/copilot-instructions.md file. + Skills are aggregated into sections within this file. + """ + + INSTRUCTIONS_FILE = "copilot-instructions.md" + MARKER_START = "" + MARKER_END = "" + + @property + def name(self) -> str: + return "copilot" + + def install_skill(self, skill: Skill, target_dir: Path) -> Path: + """Append skill content to the Copilot instructions file. + + Note: For Copilot, use install_skills() to batch-install, + as each skill is a section in a single file. + """ + target_dir.mkdir(parents=True, exist_ok=True) + dest = target_dir / self.INSTRUCTIONS_FILE + content = skill.read_skill_md() + content = _strip_frontmatter(content) + + start = f"" + end = f"" + section = f"\n\n{start}\n{content}\n{end}\n" + + if dest.exists(): + existing = dest.read_text() + # Replace existing section or append + start_marker = f"" + end_marker = f"" + if start_marker in existing: + before = existing[: existing.index(start_marker)] + after = existing[existing.index(end_marker) + len(end_marker) :] + dest.write_text(before + section.strip() + after) + else: + dest.write_text(existing + section) + else: + header = f"{self.MARKER_START}\n# AI Coding Skills (managed by whet)\n" + dest.write_text(header + section + f"\n{self.MARKER_END}\n") + + return dest + + def remove_skill(self, skill_name: str, target_dir: Path) -> bool: + """Remove a skill section from the Copilot instructions file.""" + dest = target_dir / self.INSTRUCTIONS_FILE + if not dest.exists(): + return False + + content = dest.read_text() + start_marker = f"" + end_marker = f"" + + if start_marker not in content: + return False + + before = content[: content.index(start_marker)] + after = content[content.index(end_marker) + len(end_marker) :] + dest.write_text(before.rstrip() + after.lstrip("\n")) + return True + + def list_installed(self, target_dir: Path) -> list[str]: + """List installed skills by scanning for whet markers.""" + dest = target_dir / self.INSTRUCTIONS_FILE + if not dest.exists(): + return [] + + content = dest.read_text() + names: list[str] = [] + for line in content.split("\n"): + if line.strip().startswith(""): + name = line.strip().removeprefix("") + names.append(name) + return sorted(names) + + +def _strip_frontmatter(content: str) -> str: + """Remove YAML frontmatter from markdown content.""" + if not content.startswith("---"): + return content + lines = content.split("\n") + for i, line in enumerate(lines[1:], start=1): + if line.strip() == "---": + return "\n".join(lines[i + 1 :]).lstrip("\n") + return content diff --git a/src/whet/adapters/cursor.py b/src/whet/adapters/cursor.py new file mode 100644 index 0000000..1a4aa50 --- /dev/null +++ b/src/whet/adapters/cursor.py @@ -0,0 +1,58 @@ +"""Cursor adapter — converts SKILL.md to Cursor rules format.""" + +from __future__ import annotations + +from pathlib import Path + +from whet.core.skill import Skill + + +class CursorAdapter: + """Adapter for Cursor. + + Cursor uses .cursor/rules/ with markdown rule files. + Strips YAML frontmatter and writes as .md rule files. + """ + + @property + def name(self) -> str: + return "cursor" + + def install_skill(self, skill: Skill, target_dir: Path) -> Path: + """Write skill as a Cursor rule file.""" + target_dir.mkdir(parents=True, exist_ok=True) + dest = target_dir / f"{skill.name}.md" + + content = skill.read_skill_md() + # Strip YAML frontmatter for Cursor + content = _strip_frontmatter(content) + + dest.write_text(content) + return dest + + def remove_skill(self, skill_name: str, target_dir: Path) -> bool: + """Remove a Cursor rule file.""" + rule_file = target_dir / f"{skill_name}.md" + if rule_file.is_file(): + rule_file.unlink() + return True + return False + + def list_installed(self, target_dir: Path) -> list[str]: + """List installed rules by scanning .md files.""" + if not target_dir.is_dir(): + return [] + return sorted(f.stem for f in target_dir.glob("*.md")) + + +def _strip_frontmatter(content: str) -> str: + """Remove YAML frontmatter from markdown content.""" + if not content.startswith("---"): + return content + + lines = content.split("\n") + for i, line in enumerate(lines[1:], start=1): + if line.strip() == "---": + return "\n".join(lines[i + 1 :]).lstrip("\n") + + return content diff --git a/src/whet/adapters/detect.py b/src/whet/adapters/detect.py new file mode 100644 index 0000000..25c0000 --- /dev/null +++ b/src/whet/adapters/detect.py @@ -0,0 +1,30 @@ +"""Auto-detect platform from project directory markers.""" + +from __future__ import annotations + +from pathlib import Path + +from whet.core.config import Platform + +# Detection priority order +_MARKERS: list[tuple[str, Platform]] = [ + (".claude", Platform.CLAUDE), + (".agent", Platform.ANTIGRAVITY), + (".cursor", Platform.CURSOR), + (".github", Platform.COPILOT), +] + + +def detect_platform(project_dir: Path | None = None) -> Platform | None: + """Detect the AI platform from project directory markers. + + Checks for platform-specific directories in priority order. + Returns None if no platform markers are found. + """ + root = project_dir or Path.cwd() + + for marker, platform in _MARKERS: + if (root / marker).is_dir(): + return platform + + return None diff --git a/src/whet/cli/__init__.py b/src/whet/cli/__init__.py new file mode 100644 index 0000000..b52b387 --- /dev/null +++ b/src/whet/cli/__init__.py @@ -0,0 +1,64 @@ +"""whet CLI — Sharpen your AI coder.""" + +from __future__ import annotations + +import typer +from rich.console import Console + +from whet import __version__ + +console = Console() + +app = typer.Typer( + name="whet", + help="Sharpen your AI coder — install expert skills into AI coding agents.", + no_args_is_help=True, + add_completion=False, + rich_markup_mode="rich", +) + +BANNER = r""" + [bold cyan]██╗ ██╗██╗ ██╗███████╗████████╗[/bold cyan] + [bold cyan]██║ ██║██║ ██║██╔════╝╚══██╔══╝[/bold cyan] + [bold cyan]██║ █╗ ██║███████║█████╗ ██║[/bold cyan] + [bold cyan]██║███╗██║██╔══██║██╔══╝ ██║[/bold cyan] + [bold cyan]╚███╔███╔╝██║ ██║███████╗ ██║[/bold cyan] + [bold cyan] ╚══╝╚══╝ ╚═╝ ╚═╝╚══════╝ ╚═╝[/bold cyan] + [dim]sharpen your AI coder[/dim] +""" + + +def version_callback(value: bool) -> None: # noqa: FBT001 + """Print version and exit.""" + if value: + console.print(BANNER) + console.print(f" [bold]v{__version__}[/bold] · claude · antigravity · cursor\n") + raise typer.Exit() + + +@app.callback() +def main( + version: bool | None = typer.Option( # noqa: UP007 + None, + "--version", + "-v", + help="Show version and exit.", + callback=version_callback, + is_eager=True, + ), +) -> None: + """whet — Sharpen your AI coder. + + Install expert skills into AI coding agents (Claude Code, Antigravity, Cursor, Copilot). + """ + + +# Import and register sub-commands +from whet.cli import config as _config_mod # noqa: E402, F401 +from whet.cli import doctor as _doctor_mod # noqa: E402, F401 +from whet.cli import init as _init_mod # noqa: E402, F401 +from whet.cli import install as _install_mod # noqa: E402, F401 +from whet.cli import settings as _settings_mod # noqa: E402, F401 +from whet.cli import skills as _skills_mod # noqa: E402, F401 +from whet.cli import target as _target_mod # noqa: E402, F401 +from whet.cli import update as _update_mod # noqa: E402, F401 diff --git a/src/whet/cli/config.py b/src/whet/cli/config.py new file mode 100644 index 0000000..6149ecb --- /dev/null +++ b/src/whet/cli/config.py @@ -0,0 +1,49 @@ +"""Config command — get/set whet configuration.""" + +from __future__ import annotations + +import typer +from rich.console import Console + +from whet.cli import app +from whet.core.config import Platform + +console = Console() + + +@app.command() +def config( + key: str | None = typer.Argument(None, help="Configuration key."), # noqa: UP007 + value: str | None = typer.Argument(None, help="Value to set."), # noqa: UP007 +) -> None: + """Get or set whet configuration. + + Without arguments, shows all configuration. + With a key, shows that value. With key and value, sets it. + """ + from whet.core.config import WhetConfig + + cfg = WhetConfig() + + if key is None: + console.print("[bold]whet configuration[/bold]\n") + console.print(f" target: {cfg.target.value}") + console.print(f" skills_dir: {cfg.skills_dir}") + console.print(f" agents_dir: {cfg.agents_dir}") + console.print(f" archetypes_dir: {cfg.archetypes_dir}") + return + + if key == "target": + if value: + try: + platform = Platform(value) + except ValueError as err: + valid = ", ".join(p.value for p in Platform) + console.print(f"[red]Invalid platform '{value}'. Valid: {valid}[/red]") + raise typer.Exit(code=1) from err + console.print(f"[green]✓[/green] target = {platform.value}") + else: + console.print(f"target = {cfg.target.value}") + else: + console.print(f"[yellow]Unknown config key: {key}[/yellow]") + raise typer.Exit(code=1) diff --git a/src/whet/cli/doctor.py b/src/whet/cli/doctor.py new file mode 100644 index 0000000..c7abbac --- /dev/null +++ b/src/whet/cli/doctor.py @@ -0,0 +1,181 @@ +"""Doctor command — health check for whet installation.""" + +from __future__ import annotations + +import typer +from rich.console import Console + +from whet.adapters.detect import detect_platform +from whet.cli import app +from whet.core.config import PLATFORM_PATHS, WhetConfig +from whet.registry.loader import discover_skills + +console = Console() + + +def _check_mark(ok: bool) -> str: + return "[green]✓[/green]" if ok else "[red]✗[/red]" + + +def _warn_mark() -> str: + return "[yellow]![/yellow]" + + +@app.command() +def doctor() -> None: + """Run health checks on your whet installation.""" + console.print("[bold]whet doctor[/bold]\n") + issues = 0 + warnings = 0 + + cfg = WhetConfig() + + # Check 1: Skills directory + if cfg.skills_dir.is_dir(): + skills = discover_skills(cfg.skills_dir) + n = len(skills) + console.print(f" {_check_mark(True)} Skills directory: {cfg.skills_dir} ({n} skills)") + else: + console.print(f" {_check_mark(False)} Skills directory not found: {cfg.skills_dir}") + issues += 1 + skills = [] + + # Check 2: Agents directory + if cfg.agents_dir.is_dir(): + agents = [ + d.name for d in cfg.agents_dir.iterdir() if d.is_dir() and (d / "SKILL.md").exists() + ] + n_agents = len(agents) + console.print( + f" {_check_mark(True)} Agents directory: {cfg.agents_dir} ({n_agents} agents)" + ) + else: + console.print(f" {_warn_mark()} Agents directory not found: {cfg.agents_dir}") + warnings += 1 + agents = [] + + # Check 3: Archetypes directory + if cfg.archetypes_dir.is_dir(): + archetypes = [ + d.name + for d in cfg.archetypes_dir.iterdir() + if d.is_dir() and (d / "README.md").exists() + ] + n_arch = len(archetypes) + console.print( + f" {_check_mark(True)} Archetypes directory: " + f"{cfg.archetypes_dir} ({n_arch} archetypes)" + ) + else: + console.print(f" {_warn_mark()} Archetypes directory not found: {cfg.archetypes_dir}") + warnings += 1 + + # Check 4: Platform detection + detected = detect_platform() + if detected: + console.print(f" {_check_mark(True)} Platform detected: {detected.value}") + else: + console.print(f" {_warn_mark()} No platform auto-detected in current directory") + warnings += 1 + + # Check 5: Installed skills + if detected: + from whet.cli.skills import _get_adapter + + adapter = _get_adapter(detected) + paths = PLATFORM_PATHS[detected] + + lc = len(adapter.list_installed(paths.local_dir)) + gc = len(adapter.list_installed(paths.global_dir)) + + if lc > 0 or gc > 0: + console.print(f" {_check_mark(True)} Installed: {lc} local, {gc} global") + else: + console.print(f" {_warn_mark()} No skills installed for {detected.value}") + warnings += 1 + + # Check 6: SKILL.md frontmatter + if skills: + missing_fm = [s for s in skills if not s.description] + if missing_fm: + n_fm = len(missing_fm) + console.print(f" {_warn_mark()} {n_fm} skills missing YAML frontmatter") + for s in missing_fm[:5]: + console.print(f" - {s.name}") + warnings += 1 + else: + console.print(f" {_check_mark(True)} All skills have YAML frontmatter") + + # Check 7: skill.toml files + if skills: + missing_toml = [s for s in skills if not s.has_toml] + if missing_toml: + n_toml = len(missing_toml) + console.print(f" {_warn_mark()} {n_toml} skills missing skill.toml") + for s in missing_toml[:5]: + console.print(f" - {s.name}") + warnings += 1 + else: + console.print(f" {_check_mark(True)} All skills have skill.toml metadata") + + # Check 8: agent.toml files + if agents: + missing_agent_toml = [a for a in agents if not (cfg.agents_dir / a / "agent.toml").exists()] + if missing_agent_toml: + n_at = len(missing_agent_toml) + console.print(f" {_warn_mark()} {n_at} agents missing agent.toml") + for a in missing_agent_toml[:5]: + console.print(f" - {a}") + warnings += 1 + else: + console.print(f" {_check_mark(True)} All agents have agent.toml metadata") + + # Check 9: Settings template + from whet.settings.engine import get_settings_target, get_template_path + + plat = detected.value if detected else cfg.target.value + template_path = get_template_path(plat) + if template_path.exists(): + console.print(f" {_check_mark(True)} Settings template: {template_path.name}") + + # Check if settings are applied + local_target = get_settings_target(plat, "local") + if local_target.exists(): + console.print(f" {_check_mark(True)} Local settings: {local_target}") + else: + console.print( + f" {_warn_mark()} No local settings. Run: [bold]whet settings apply[/bold]" + ) + warnings += 1 + else: + console.print(f" {_warn_mark()} No settings template for {plat}") + warnings += 1 + + # Check 10: Dependency consistency + if skills: + available = {s.name for s in skills} + broken_deps = [] + for skill in skills: + if skill.metadata and skill.metadata.dependencies.requires: + for dep in skill.metadata.dependencies.requires: + if dep not in available: + broken_deps.append((skill.name, dep)) + + if broken_deps: + n_bd = len(broken_deps) + console.print(f" {_warn_mark()} {n_bd} broken dependency reference(s)") + for skill_name, dep in broken_deps[:5]: + console.print(f" - {skill_name} requires '{dep}' (not found)") + warnings += 1 + else: + console.print(f" {_check_mark(True)} All skill dependencies resolve") + + # Summary + console.print() + if issues: + console.print(f"[red]{issues} error(s), {warnings} warning(s)[/red]") + raise typer.Exit(code=1) + elif warnings: + console.print(f"[yellow]{warnings} warning(s), 0 errors[/yellow]") + else: + console.print("[bold green]All checks passed.[/bold green]") diff --git a/src/whet/cli/init.py b/src/whet/cli/init.py new file mode 100644 index 0000000..ba44828 --- /dev/null +++ b/src/whet/cli/init.py @@ -0,0 +1,116 @@ +"""Init command — scaffold a new project from an archetype.""" + +from __future__ import annotations + +from pathlib import Path + +import typer +from rich.console import Console +from rich.table import Table + +from whet.cli import app +from whet.core.config import WhetConfig +from whet.scaffold.engine import ( + ScaffoldContext, + discover_archetypes, + load_archetype, + render_template, +) + +console = Console() + + +@app.command(name="init") +def init_project( + archetype_name: str | None = typer.Argument( # noqa: UP007 + None, help="Archetype to scaffold from." + ), + output_dir: str | None = typer.Option( # noqa: UP007 + None, "--output", "-o", help="Output directory (default: current dir)." + ), + project_name: str | None = typer.Option( # noqa: UP007 + None, "--name", "-n", help="Project name." + ), + author: str | None = typer.Option( # noqa: UP007 + None, "--author", "-a", help="Author name." + ), +) -> None: + """Scaffold a new project from an archetype template. + + Without arguments, lists available archetypes. + """ + cfg = WhetConfig() + + if archetype_name is None: + _list_archetypes(cfg) + return + + archetype = load_archetype(cfg.archetypes_dir / archetype_name) + if not archetype: + console.print(f"[red]Archetype '{archetype_name}' not found.[/red]") + console.print("[dim]Run 'whet init' to see available archetypes.[/dim]") + raise typer.Exit(code=1) + + name = project_name or archetype_name + dest = Path(output_dir) if output_dir else Path.cwd() / name + + if dest.exists() and any(dest.iterdir()): + console.print(f"[red]Directory '{dest}' already exists and is not empty.[/red]") + raise typer.Exit(code=1) + + context = ScaffoldContext( + project_name=name, + description=archetype.metadata.description, + author=author or "", + ) + + console.print(f"[bold]Scaffolding: {archetype.metadata.name}[/bold]\n") + console.print(f" Project: {context.project_name}") + console.print(f" Package: {context.package_name}") + console.print(f" Directory: {dest}") + console.print() + + render_template(archetype, dest, context) + + console.print(f"[bold green]✓ Project scaffolded at {dest}[/bold green]\n") + + # Show required skills + if archetype.skills.required: + console.print("[bold]Required skills:[/bold]") + for skill in archetype.skills.required: + console.print(f" - {skill}") + console.print( + f"\nRun: [bold]cd {dest.name} && whet add {' '.join(archetype.skills.required)}[/bold]" + ) + + if archetype.skills.recommended: + console.print("\n[bold]Recommended skills:[/bold]") + for skill in archetype.skills.recommended: + console.print(f" - {skill}") + + +def _list_archetypes(cfg: WhetConfig) -> None: + """List available archetypes.""" + archetypes = discover_archetypes(cfg.archetypes_dir) + + if not archetypes: + console.print("[dim]No archetypes found.[/dim]") + raise typer.Exit() + + table = Table(title="Available Archetypes") + table.add_column("Name", style="cyan", no_wrap=True) + table.add_column("Category", style="green") + table.add_column("Description") + table.add_column("Template", style="dim") + + for arch in archetypes: + has_tmpl = "yes" if arch.has_template else "minimal" + table.add_row( + arch.metadata.name, + arch.metadata.category, + arch.metadata.description, + has_tmpl, + ) + + console.print(table) + console.print("\n[dim]Usage: whet init [/dim]") diff --git a/src/whet/cli/install.py b/src/whet/cli/install.py new file mode 100644 index 0000000..4a8ed74 --- /dev/null +++ b/src/whet/cli/install.py @@ -0,0 +1,73 @@ +"""Install command — bulk install skills to a platform.""" + +from __future__ import annotations + +import typer +from rich.console import Console + +from whet.cli import app +from whet.core.config import WhetConfig +from whet.registry.loader import discover_skills + +console = Console() + + +@app.command() +def install( + scope_global: bool = typer.Option(False, "--global", "-g", help="Install to global directory."), + scope_local: bool = typer.Option(False, "--local", "-l", help="Install to local project."), + category: str | None = typer.Option(None, "--category", "--cat", help="Only install category."), +) -> None: + """Install the full skill collection.""" + if not scope_global and not scope_local: + scope_local = True + + cfg = _get_config() + adapter = _get_adapter(cfg.target) + all_skills = discover_skills(cfg.skills_dir) + + if category: + all_skills = [s for s in all_skills if s.category == category] + + if not all_skills: + console.print("[yellow]No skills found to install.[/yellow]") + raise typer.Exit(code=1) + + paths = cfg.get_platform_paths() + + if scope_global: + _install_to(adapter, all_skills, paths.global_dir, "global") + + if scope_local: + _install_to(adapter, all_skills, paths.local_dir, "local") + + +def _install_to(adapter, skills, target_dir, scope_label): # type: ignore[no-untyped-def] + """Install skills to a target directory.""" + console.print(f"\n[bold]Installing {len(skills)} skills ({scope_label})...[/bold]") + + for skill in skills: + adapter.install_skill(skill, target_dir) + console.print(f" [green]✓[/green] {skill.name}") + + console.print(f"\n[bold green]✓ Installed {len(skills)} skills to {target_dir}[/bold green]") + + +def _get_config() -> WhetConfig: + return WhetConfig() + + +def _get_adapter(platform): # type: ignore[no-untyped-def] + from whet.adapters.antigravity import AntigravityAdapter + from whet.adapters.claude import ClaudeAdapter + from whet.adapters.copilot import CopilotAdapter + from whet.adapters.cursor import CursorAdapter + from whet.core.config import Platform + + adapters = { + Platform.CLAUDE: ClaudeAdapter, + Platform.ANTIGRAVITY: AntigravityAdapter, + Platform.CURSOR: CursorAdapter, + Platform.COPILOT: CopilotAdapter, + } + return adapters[platform]() diff --git a/src/whet/cli/settings.py b/src/whet/cli/settings.py new file mode 100644 index 0000000..4cf5c7b --- /dev/null +++ b/src/whet/cli/settings.py @@ -0,0 +1,172 @@ +"""Settings commands — generate, apply, and diff platform settings.""" + +from __future__ import annotations + +import typer +from rich.console import Console +from rich.table import Table + +from whet.cli import app +from whet.core.config import Platform, WhetConfig +from whet.settings.engine import ( + diff_settings, + get_settings_target, + get_template_path, + load_existing, + load_template, + merge_settings, + write_settings, +) + +console = Console() + +settings_app = typer.Typer( + name="settings", + help="Manage platform settings (permissions, allowed commands).", + no_args_is_help=True, +) +app.add_typer(settings_app) + + +@settings_app.command() +def generate( + platform: str | None = typer.Option( # noqa: UP007 + None, "--platform", "-p", help="Target platform." + ), +) -> None: + """Generate optimized settings for a platform.""" + cfg = WhetConfig() + plat = platform or cfg.target.value + + try: + Platform(plat) + except ValueError as err: + valid = ", ".join(p.value for p in Platform) + console.print(f"[red]Invalid platform '{plat}'. Valid: {valid}[/red]") + raise typer.Exit(code=1) from err + + template_path = get_template_path(plat) + + if not template_path.exists(): + console.print(f"[yellow]No settings template for '{plat}'.[/yellow]") + console.print("[dim]Available templates:[/dim]") + templates_dir = template_path.parent + for f in sorted(templates_dir.glob("*.json")): + console.print(f" {f.stem}") + raise typer.Exit(code=1) + + template = load_template(template_path) + n = len(template.permissions.get("allow", [])) + + console.print(f"[bold]Generated settings for {plat}[/bold]\n") + console.print(f" Permissions: {n} allow rules") + console.print(f" Template: {template_path}") + console.print("\n Run [bold]whet settings diff[/bold] to review") + console.print(" Run [bold]whet settings apply[/bold] to write") + + +@settings_app.command() +def diff( + platform: str | None = typer.Option( # noqa: UP007 + None, "--platform", "-p", help="Target platform." + ), + scope: str = typer.Option("local", "--scope", "-s", help="Scope: local or global."), +) -> None: + """Preview changes between current settings and template.""" + cfg = WhetConfig() + plat = platform or cfg.target.value + + template_path = get_template_path(plat) + if not template_path.exists(): + console.print(f"[red]No template for '{plat}'.[/red]") + raise typer.Exit(code=1) + + template = load_template(template_path) + target_path = get_settings_target(plat, scope) + existing = load_existing(target_path) + + new_entries, kept_entries, all_merged = diff_settings(existing, template) + + console.print(f"[bold]Settings diff for {plat} ({scope})[/bold]\n") + console.print(f" Target: {target_path}") + + if existing: + console.print(f" Current: {len(kept_entries)} permissions") + else: + console.print(" Current: [dim]no file[/dim]") + + console.print(f" Template: {len(template.permissions.get('allow', []))} permissions") + console.print(f" Result: {len(all_merged)} permissions\n") + + if new_entries: + table = Table(title="New permissions to add") + table.add_column("Permission", style="green") + for entry in new_entries: + table.add_row(f"+ {entry}") + console.print(table) + else: + console.print(" [dim]No new permissions — settings are up to date.[/dim]") + + +@settings_app.command() +def apply( + platform: str | None = typer.Option( # noqa: UP007 + None, "--platform", "-p", help="Target platform." + ), + scope_global: bool = typer.Option(False, "--global", "-g", help="Apply to global settings."), + scope_local: bool = typer.Option( + False, "--local", "-l", help="Apply to local project settings." + ), + force: bool = typer.Option( + False, "--force", "-f", help="Overwrite existing settings entirely." + ), +) -> None: + """Apply settings template to platform configuration. + + By default merges with existing settings (keeps current permissions, + adds new ones from template). Use --force to replace entirely. + """ + if not scope_global and not scope_local: + scope_local = True + + cfg = WhetConfig() + plat = platform or cfg.target.value + + template_path = get_template_path(plat) + if not template_path.exists(): + console.print(f"[red]No template for '{plat}'.[/red]") + raise typer.Exit(code=1) + + template = load_template(template_path) + + scopes = [] + if scope_global: + scopes.append("global") + if scope_local: + scopes.append("local") + + for scope in scopes: + target_path = get_settings_target(plat, scope) + existing = load_existing(target_path) + + if existing and not force: + merged = merge_settings(existing, template) + new_entries, _, _ = diff_settings(existing, template) + write_settings(target_path, merged) + n_new = len(new_entries) + n_total = len(merged.permissions.get("allow", [])) + console.print( + f" [green]✓[/green] Merged {scope}: +{n_new} new, {n_total} total → {target_path}" + ) + elif existing and force: + # Back up existing + backup = target_path.with_suffix(".json.bak") + backup.write_text(target_path.read_text()) + write_settings(target_path, template) + n = len(template.permissions.get("allow", [])) + console.print(f" [green]✓[/green] Replaced {scope}: {n} permissions → {target_path}") + console.print(f" [dim]Backup: {backup}[/dim]") + else: + write_settings(target_path, template) + n = len(template.permissions.get("allow", [])) + console.print(f" [green]✓[/green] Created {scope}: {n} permissions → {target_path}") diff --git a/src/whet/cli/skills.py b/src/whet/cli/skills.py new file mode 100644 index 0000000..e009c3f --- /dev/null +++ b/src/whet/cli/skills.py @@ -0,0 +1,200 @@ +"""Skill management commands: add, remove, list, search, info.""" + +from __future__ import annotations + +import typer +from rich.console import Console +from rich.table import Table + +from whet.cli import app +from whet.core.config import Platform, WhetConfig +from whet.registry.loader import discover_skills, load_skill +from whet.registry.resolver import resolve_dependencies +from whet.registry.search import search_skills + +console = Console() + + +def _get_config() -> WhetConfig: + return WhetConfig() + + +def _get_adapter(platform: Platform): # type: ignore[no-untyped-def] + from whet.adapters.antigravity import AntigravityAdapter + from whet.adapters.claude import ClaudeAdapter + from whet.adapters.copilot import CopilotAdapter + from whet.adapters.cursor import CursorAdapter + + adapters = { + Platform.CLAUDE: ClaudeAdapter, + Platform.ANTIGRAVITY: AntigravityAdapter, + Platform.CURSOR: CursorAdapter, + Platform.COPILOT: CopilotAdapter, + } + return adapters[platform]() + + +@app.command() +def add( + skills: list[str] = typer.Argument(help="Skill name(s) to add."), + scope: bool = typer.Option(False, "--global", "-g", help="Install globally."), +) -> None: + """Add skill(s) to the current project or globally.""" + cfg = _get_config() + adapter = _get_adapter(cfg.target) + all_skills = discover_skills(cfg.skills_dir) + available = {s.name: s for s in all_skills} + + # Resolve dependencies + to_install = resolve_dependencies(skills, available) + + paths = cfg.get_platform_paths() + target_dir = paths.global_dir if scope else paths.local_dir + + installed_count = 0 + for name in to_install: + skill = available.get(name) + if not skill: + console.print(f"[yellow]Warning:[/yellow] Skill '{name}' not found, skipping.") + continue + adapter.install_skill(skill, target_dir) + extra = " (dependency)" if name not in skills else "" + console.print(f" [green]✓[/green] {name}{extra}") + installed_count += 1 + + console.print(f"\n[bold]Installed {installed_count} skill(s) to {target_dir}[/bold]") + + +@app.command() +def remove( + skill_name: str = typer.Argument(help="Skill name to remove."), + scope: bool = typer.Option(False, "--global", "-g", help="Remove from global installation."), +) -> None: + """Remove a skill from the current project or global installation.""" + cfg = _get_config() + adapter = _get_adapter(cfg.target) + paths = cfg.get_platform_paths() + target_dir = paths.global_dir if scope else paths.local_dir + + if adapter.remove_skill(skill_name, target_dir): + console.print(f" [green]✓[/green] Removed {skill_name}") + else: + console.print(f" [red]✗[/red] Skill '{skill_name}' not found in {target_dir}") + raise typer.Exit(code=1) + + +@app.command(name="list") +def list_skills( + installed: bool = typer.Option(False, "--installed", "-i", help="Show only installed skills."), + category: str | None = typer.Option(None, "--category", "--cat", help="Filter by category."), # noqa: UP007 +) -> None: + """Browse available skills.""" + cfg = _get_config() + + if installed: + adapter = _get_adapter(cfg.target) + paths = cfg.get_platform_paths() + # Check both local and global + local_installed = adapter.list_installed(paths.local_dir) + global_installed = adapter.list_installed(paths.global_dir) + all_installed = sorted(set(local_installed + global_installed)) + + if not all_installed: + console.print("[dim]No skills installed.[/dim]") + raise typer.Exit() + + table = Table(title="Installed Skills") + table.add_column("Name", style="cyan") + table.add_column("Scope", style="green") + + for name in all_installed: + scope_label = [] + if name in local_installed: + scope_label.append("local") + if name in global_installed: + scope_label.append("global") + table.add_row(name, ", ".join(scope_label)) + + console.print(table) + return + + all_skills = discover_skills(cfg.skills_dir) + if category: + all_skills = [s for s in all_skills if s.category == category] + + if not all_skills: + console.print("[dim]No skills found.[/dim]") + raise typer.Exit() + + table = Table(title="Available Skills") + table.add_column("Name", style="cyan", no_wrap=True) + table.add_column("Category", style="green") + table.add_column("Description") + + for skill in all_skills: + desc = skill.description[:80] + "..." if len(skill.description) > 80 else skill.description + table.add_row(skill.name, skill.category, desc) + + console.print(table) + console.print(f"\n[dim]{len(all_skills)} skills available[/dim]") + + +@app.command() +def search( + query: str = typer.Argument(help="Search query (matches name, description, tags)."), +) -> None: + """Search skills by name, description, or tags.""" + cfg = _get_config() + all_skills = discover_skills(cfg.skills_dir) + results = search_skills(all_skills, query=query) + + if not results: + console.print(f"[dim]No skills matching '{query}'[/dim]") + raise typer.Exit() + + table = Table(title=f"Search results for '{query}'") + table.add_column("Name", style="cyan", no_wrap=True) + table.add_column("Category", style="green") + table.add_column("Tags", style="dim") + table.add_column("Description") + + for skill in results: + tags = ", ".join(skill.tags[:4]) + desc = skill.description[:60] + "..." if len(skill.description) > 60 else skill.description + table.add_row(skill.name, skill.category, tags, desc) + + console.print(table) + + +@app.command() +def info( + skill_name: str = typer.Argument(help="Skill name."), +) -> None: + """Show detailed information about a skill.""" + cfg = _get_config() + skill = load_skill(cfg.skills_dir, skill_name) + + if not skill: + console.print(f"[red]Skill '{skill_name}' not found.[/red]") + raise typer.Exit(code=1) + + console.print(f"\n[bold cyan]{skill.name}[/bold cyan]") + console.print(f"[dim]{skill.description}[/dim]\n") + + if skill.metadata: + m = skill.metadata + console.print(f" [bold]Category:[/bold] {m.category}") + console.print(f" [bold]Version:[/bold] {m.version}") + console.print(f" [bold]Tags:[/bold] {', '.join(m.tags)}") + + if m.dependencies.requires: + console.print(f" [bold]Requires:[/bold] {', '.join(m.dependencies.requires)}") + if m.dependencies.recommends: + console.print(f" [bold]Recommends:[/bold] {', '.join(m.dependencies.recommends)}") + + if m.compatibility.libraries: + console.print(" [bold]Libraries:[/bold]") + for lib, ver in m.compatibility.libraries.items(): + console.print(f" {lib} {ver}") + + console.print(f"\n [dim]Path: {skill.path}[/dim]") diff --git a/src/whet/cli/target.py b/src/whet/cli/target.py new file mode 100644 index 0000000..5e0bcb7 --- /dev/null +++ b/src/whet/cli/target.py @@ -0,0 +1,44 @@ +"""Target command — set and show the active platform.""" + +from __future__ import annotations + +import typer +from rich.console import Console + +from whet.adapters.detect import detect_platform +from whet.cli import app +from whet.core.config import PLATFORM_PATHS, Platform + +console = Console() + + +@app.command() +def target( + platform: Platform | None = typer.Argument(None, help="Platform to target."), # noqa: UP007 +) -> None: + """Set or show the target platform. + + Without arguments, shows the current target and auto-detected platform. + """ + if platform: + paths = PLATFORM_PATHS[platform] + console.print(f"[bold green]✓ Target set to {platform.value}[/bold green]") + console.print(f" Global: {paths.global_dir}") + console.print(f" Local: {paths.local_dir}") + return + + # Show current state + detected = detect_platform() + console.print("[bold]Platform Detection[/bold]\n") + + if detected: + console.print(f" Auto-detected: [cyan]{detected.value}[/cyan]") + else: + console.print(" Auto-detected: [dim]none[/dim]") + + console.print("\n[bold]Available Platforms[/bold]\n") + for plat, paths in PLATFORM_PATHS.items(): + marker = " [green]✓[/green]" if plat == detected else "" + console.print(f" [cyan]{plat.value}[/cyan]{marker}") + console.print(f" Global: {paths.global_dir}") + console.print(f" Local: {paths.local_dir}") diff --git a/src/whet/cli/update.py b/src/whet/cli/update.py new file mode 100644 index 0000000..6705c5b --- /dev/null +++ b/src/whet/cli/update.py @@ -0,0 +1,43 @@ +"""Update command — update whet to the latest version.""" + +from __future__ import annotations + +import subprocess +import sys + +from rich.console import Console + +from whet import __version__ +from whet.cli import app + +console = Console() + + +@app.command() +def update() -> None: + """Update whet to the latest version.""" + console.print(f"[bold]Current version:[/bold] v{__version__}\n") + console.print("Checking for updates...") + + try: + result = subprocess.run( # noqa: S603 + [sys.executable, "-m", "pip", "install", "--upgrade", "whet"], + capture_output=True, + text=True, + check=False, + ) + + if result.returncode == 0: + if "already satisfied" in result.stdout.lower(): + console.print("[green]✓ Already up to date.[/green]") + else: + console.print("[bold green]✓ Updated successfully.[/bold green]") + console.print("[dim]Restart your shell to use the new version.[/dim]") + else: + console.print("[yellow]Could not update via pip. Try:[/yellow]") + console.print(" [bold]uv tool upgrade whet[/bold]") + console.print(" or") + console.print(" [bold]uvx --upgrade whet list[/bold]") + except FileNotFoundError: + console.print("[yellow]pip not found. Try:[/yellow]") + console.print(" [bold]uv tool upgrade whet[/bold]") diff --git a/src/whet/core/__init__.py b/src/whet/core/__init__.py new file mode 100644 index 0000000..ea453a2 --- /dev/null +++ b/src/whet/core/__init__.py @@ -0,0 +1,3 @@ +"""Core domain models for whet.""" + +from __future__ import annotations diff --git a/src/whet/core/config.py b/src/whet/core/config.py new file mode 100644 index 0000000..c711356 --- /dev/null +++ b/src/whet/core/config.py @@ -0,0 +1,59 @@ +"""Global and local configuration model.""" + +from __future__ import annotations + +from enum import Enum +from pathlib import Path + +from pydantic import BaseModel, Field + + +class Platform(str, Enum): + """Supported AI coding agent platforms.""" + + CLAUDE = "claude" + ANTIGRAVITY = "antigravity" + CURSOR = "cursor" + COPILOT = "copilot" + + +class PlatformPaths(BaseModel): + """Platform-specific paths for skill installation.""" + + global_dir: Path + local_dir: Path + + +PLATFORM_PATHS: dict[Platform, PlatformPaths] = { + Platform.CLAUDE: PlatformPaths( + global_dir=Path.home() / ".claude" / "skills", + local_dir=Path(".claude") / "skills", + ), + Platform.ANTIGRAVITY: PlatformPaths( + global_dir=Path.home() / ".gemini" / "antigravity" / "skills", + local_dir=Path(".agent") / "skills", + ), + Platform.CURSOR: PlatformPaths( + global_dir=Path.home() / ".cursor" / "rules", + local_dir=Path(".cursor") / "rules", + ), + Platform.COPILOT: PlatformPaths( + global_dir=Path.home() / ".github", + local_dir=Path(".github"), + ), +} + + +class WhetConfig(BaseModel): + """Whet configuration persisted to disk.""" + + target: Platform = Platform.CLAUDE + skills_dir: Path = Field(default_factory=lambda: Path(__file__).resolve().parents[3] / "skills") + agents_dir: Path = Field(default_factory=lambda: Path(__file__).resolve().parents[3] / "agents") + archetypes_dir: Path = Field( + default_factory=lambda: Path(__file__).resolve().parents[3] / "archetypes" + ) + + def get_platform_paths(self) -> PlatformPaths: + """Get paths for the current target platform.""" + return PLATFORM_PATHS[self.target] diff --git a/src/whet/core/skill.py b/src/whet/core/skill.py new file mode 100644 index 0000000..3eed8fc --- /dev/null +++ b/src/whet/core/skill.py @@ -0,0 +1,175 @@ +"""Skill metadata model.""" + +from __future__ import annotations + +import sys +from pathlib import Path + +from pydantic import BaseModel, Field + +if sys.version_info >= (3, 12): + import tomllib +else: + import tomli as tomllib + + +class SkillDependencies(BaseModel): + """Skill dependency specification.""" + + requires: list[str] = Field(default_factory=list) + recommends: list[str] = Field(default_factory=list) + + +class SkillCompatibility(BaseModel): + """Skill compatibility specification.""" + + python: str = ">=3.11" + libraries: dict[str, str] = Field(default_factory=dict) + + +class SkillMetadata(BaseModel): + """Machine-readable skill metadata from skill.toml.""" + + name: str + version: str = "1.0.0" + category: str = "core" + tags: list[str] = Field(default_factory=list) + + dependencies: SkillDependencies = Field(default_factory=SkillDependencies) + compatibility: SkillCompatibility = Field(default_factory=SkillCompatibility) + + +class SkillFrontmatter(BaseModel): + """YAML frontmatter parsed from SKILL.md.""" + + name: str + description: str = "" + + +class Skill(BaseModel): + """Complete skill representation combining SKILL.md and skill.toml data.""" + + name: str + description: str = "" + category: str = "core" + tags: list[str] = Field(default_factory=list) + path: Path + metadata: SkillMetadata | None = None + + @property + def skill_md_path(self) -> Path: + """Path to the SKILL.md file.""" + return self.path / "SKILL.md" + + @property + def skill_toml_path(self) -> Path: + """Path to the skill.toml file.""" + return self.path / "skill.toml" + + @property + def has_toml(self) -> bool: + """Whether this skill has a skill.toml metadata file.""" + return self.skill_toml_path.exists() + + def read_skill_md(self) -> str: + """Read the full SKILL.md content.""" + return self.skill_md_path.read_text() + + @classmethod + def from_directory(cls, path: Path) -> Skill: + """Load a skill from its directory.""" + name = path.name + description = "" + category = "core" + tags: list[str] = [] + metadata: SkillMetadata | None = None + + # Parse YAML frontmatter from SKILL.md + skill_md = path / "SKILL.md" + if skill_md.exists(): + content = skill_md.read_text() + frontmatter = _parse_frontmatter(content) + if frontmatter: + name = frontmatter.get("name", name) + description = frontmatter.get("description", "") + + # Parse skill.toml if present + skill_toml = path / "skill.toml" + if skill_toml.exists(): + with open(skill_toml, "rb") as f: + raw = tomllib.load(f) + skill_data = raw.get("skill", {}) + deps_data = raw.get("dependencies", {}) + compat_data = raw.get("compatibility", {}) + + metadata = SkillMetadata( + name=skill_data.get("name", name), + version=skill_data.get("version", "1.0.0"), + category=skill_data.get("category", "core"), + tags=skill_data.get("tags", []), + dependencies=SkillDependencies(**deps_data), + compatibility=SkillCompatibility(**compat_data), + ) + category = metadata.category + tags = metadata.tags + + return cls( + name=name, + description=description.strip() if isinstance(description, str) else "", + category=category, + tags=tags, + path=path, + metadata=metadata, + ) + + +def _parse_frontmatter(content: str) -> dict[str, str] | None: + """Parse YAML frontmatter from a markdown file. + + Returns None if no frontmatter is found. + """ + if not content.startswith("---"): + return None + + lines = content.split("\n") + end_idx = -1 + for i, line in enumerate(lines[1:], start=1): + if line.strip() == "---": + end_idx = i + break + + if end_idx == -1: + return None + + # Simple YAML parsing for flat key-value pairs + result: dict[str, str] = {} + current_key = "" + current_value = "" + + for line in lines[1:end_idx]: + stripped = line.strip() + if not stripped or stripped.startswith("#"): + continue + + if ":" in stripped and not stripped.startswith(" ") and not stripped.startswith("-"): + # Save previous key-value + if current_key: + result[current_key] = current_value.strip() + + key, _, value = stripped.partition(":") + current_key = key.strip() + value = value.strip() + # Handle YAML block scalar indicator + current_value = "" if value == ">" else value + elif current_key and stripped: + # Continuation line for block scalar + if current_value: + current_value += " " + stripped + else: + current_value = stripped + + # Save last key-value + if current_key: + result[current_key] = current_value.strip() + + return result diff --git a/src/whet/py.typed b/src/whet/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/whet/registry/__init__.py b/src/whet/registry/__init__.py new file mode 100644 index 0000000..422a707 --- /dev/null +++ b/src/whet/registry/__init__.py @@ -0,0 +1,3 @@ +"""Skill registry — discovery, loading, and search.""" + +from __future__ import annotations diff --git a/src/whet/registry/loader.py b/src/whet/registry/loader.py new file mode 100644 index 0000000..f6a227c --- /dev/null +++ b/src/whet/registry/loader.py @@ -0,0 +1,33 @@ +"""Scan and parse skills from disk.""" + +from __future__ import annotations + +from pathlib import Path + +from whet.core.skill import Skill + + +def discover_skills(skills_dir: Path) -> list[Skill]: + """Discover all skills in a directory. + + Scans for subdirectories containing SKILL.md and loads each as a Skill. + """ + if not skills_dir.is_dir(): + return [] + + skills: list[Skill] = [] + for child in sorted(skills_dir.iterdir()): + if child.is_dir() and not child.name.startswith("."): + skill_md = child / "SKILL.md" + if skill_md.exists(): + skills.append(Skill.from_directory(child)) + + return skills + + +def load_skill(skills_dir: Path, name: str) -> Skill | None: + """Load a single skill by name.""" + skill_path = skills_dir / name + if skill_path.is_dir() and (skill_path / "SKILL.md").exists(): + return Skill.from_directory(skill_path) + return None diff --git a/src/whet/registry/resolver.py b/src/whet/registry/resolver.py new file mode 100644 index 0000000..833cc36 --- /dev/null +++ b/src/whet/registry/resolver.py @@ -0,0 +1,36 @@ +"""Dependency resolution for skills.""" + +from __future__ import annotations + +from whet.core.skill import Skill + + +def resolve_dependencies( + requested: list[str], + available: dict[str, Skill], +) -> list[str]: + """Resolve skill dependencies, returning an ordered install list. + + Given a list of requested skill names, adds any required dependencies + and returns a topologically-sorted list. + """ + resolved: list[str] = [] + seen: set[str] = set() + + def _visit(name: str) -> None: + if name in seen: + return + seen.add(name) + + skill = available.get(name) + if skill and skill.metadata: + for dep in skill.metadata.dependencies.requires: + if dep in available: + _visit(dep) + + resolved.append(name) + + for name in requested: + _visit(name) + + return resolved diff --git a/src/whet/registry/search.py b/src/whet/registry/search.py new file mode 100644 index 0000000..719df16 --- /dev/null +++ b/src/whet/registry/search.py @@ -0,0 +1,36 @@ +"""Search and filter skills.""" + +from __future__ import annotations + +from whet.core.skill import Skill + + +def search_skills( + skills: list[Skill], + query: str | None = None, + category: str | None = None, + tag: str | None = None, +) -> list[Skill]: + """Search skills by query, category, or tag. + + Query matches against name, description, and tags. + """ + results = skills + + if category: + results = [s for s in results if s.category == category] + + if tag: + results = [s for s in results if tag in s.tags] + + if query: + q = query.lower() + results = [ + s + for s in results + if q in s.name.lower() + or q in s.description.lower() + or any(q in t.lower() for t in s.tags) + ] + + return results diff --git a/src/whet/scaffold/__init__.py b/src/whet/scaffold/__init__.py new file mode 100644 index 0000000..d46f2af --- /dev/null +++ b/src/whet/scaffold/__init__.py @@ -0,0 +1 @@ +"""Project scaffolding engine for whet archetypes.""" diff --git a/src/whet/scaffold/engine.py b/src/whet/scaffold/engine.py new file mode 100644 index 0000000..45561fc --- /dev/null +++ b/src/whet/scaffold/engine.py @@ -0,0 +1,245 @@ +"""Template rendering engine for project scaffolding.""" + +from __future__ import annotations + +import shutil +import sys +from pathlib import Path +from string import Template + +from pydantic import BaseModel, Field + +if sys.version_info >= (3, 12): + import tomllib +else: + import tomli as tomllib + + +class ArchetypeMetadata(BaseModel, frozen=True): + """Archetype metadata from archetype.toml.""" + + name: str + version: str = "1.0.0" + category: str = "core" + tags: list[str] = Field(default_factory=list) + description: str = "" + + +class ArchetypeSkills(BaseModel, frozen=True): + """Skills associated with an archetype.""" + + required: list[str] = Field(default_factory=list) + recommended: list[str] = Field(default_factory=list) + + +class Archetype(BaseModel, frozen=True): + """Complete archetype representation.""" + + metadata: ArchetypeMetadata + skills: ArchetypeSkills = Field(default_factory=ArchetypeSkills) + path: Path + template_dir: Path + + @property + def has_template(self) -> bool: + """Check if the archetype has template files.""" + if not self.template_dir.is_dir(): + return False + return any(self.template_dir.iterdir()) + + +def load_archetype(archetype_dir: Path) -> Archetype | None: + """Load an archetype from its directory.""" + toml_path = archetype_dir / "archetype.toml" + if not toml_path.exists(): + return None + + with open(toml_path, "rb") as f: + raw = tomllib.load(f) + + arch_data = raw.get("archetype", {}) + skills_data = raw.get("skills", {}) + + metadata = ArchetypeMetadata( + name=arch_data.get("name", archetype_dir.name), + version=arch_data.get("version", "1.0.0"), + category=arch_data.get("category", "core"), + tags=arch_data.get("tags", []), + description=arch_data.get("description", ""), + ) + + skills = ArchetypeSkills( + required=skills_data.get("required", []), + recommended=skills_data.get("recommended", []), + ) + + return Archetype( + metadata=metadata, + skills=skills, + path=archetype_dir, + template_dir=archetype_dir / "template", + ) + + +def discover_archetypes(archetypes_dir: Path) -> list[Archetype]: + """Discover all archetypes in the archetypes directory.""" + if not archetypes_dir.is_dir(): + return [] + + archetypes = [] + for d in sorted(archetypes_dir.iterdir()): + if d.is_dir(): + arch = load_archetype(d) + if arch: + archetypes.append(arch) + + return archetypes + + +class ScaffoldContext(BaseModel): + """Variables available during template rendering.""" + + project_name: str + project_slug: str = "" + package_name: str = "" + description: str = "" + author: str = "" + python_version: str = "3.11" + + def model_post_init(self, __context: object) -> None: + """Derive slug and package name from project name.""" + if not self.project_slug: + self.project_slug = self.project_name.lower().replace(" ", "-").replace("_", "-") + if not self.package_name: + self.package_name = self.project_slug.replace("-", "_") + + +def render_template( + archetype: Archetype, + output_dir: Path, + context: ScaffoldContext, +) -> Path: + """Render an archetype template to the output directory. + + Copies files from the archetype's template directory, performing + variable substitution on file contents and directory/file names. + """ + if not archetype.has_template: + # No template files — create a minimal project structure + return _create_minimal_project(output_dir, context) + + output_dir.mkdir(parents=True, exist_ok=True) + substitutions = context.model_dump() + + for src_path in archetype.template_dir.rglob("*"): + if src_path.is_dir(): + continue + + # Compute relative path with variable substitution in names + rel = src_path.relative_to(archetype.template_dir) + dest_rel = Path(Template(str(rel)).safe_substitute(substitutions)) + dest_path = output_dir / dest_rel + dest_path.parent.mkdir(parents=True, exist_ok=True) + + # Substitute variables in text files + if _is_text_file(src_path): + content = src_path.read_text() + rendered = Template(content).safe_substitute(substitutions) + dest_path.write_text(rendered) + else: + shutil.copy2(src_path, dest_path) + + return output_dir + + +def _create_minimal_project(output_dir: Path, context: ScaffoldContext) -> Path: + """Create a minimal project when no template exists.""" + output_dir.mkdir(parents=True, exist_ok=True) + + # Create src layout + pkg_dir = output_dir / "src" / context.package_name + pkg_dir.mkdir(parents=True, exist_ok=True) + (pkg_dir / "__init__.py").write_text(f'"""{context.project_name}."""\n') + + # Create tests + tests_dir = output_dir / "tests" + tests_dir.mkdir(exist_ok=True) + (tests_dir / "__init__.py").write_text("") + + # Create pyproject.toml + pyproject = f"""[project] +name = "{context.project_slug}" +version = "0.1.0" +description = "{context.description}" +authors = [{{name = "{context.author}"}}] +requires-python = ">={context.python_version}" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/{context.package_name}"] + +[tool.ruff] +line-length = 100 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "UP", "S", "B", "A", "C4", "T20", "SIM"] +ignore = ["S101"] + +[tool.mypy] +python_version = "3.11" +strict = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +""" + (output_dir / "pyproject.toml").write_text(pyproject) + + # Create README + readme = f"""# {context.project_name} + +{context.description} + +## Setup + +```bash +uv sync +``` + +## Development + +```bash +uv run pytest tests/ -v +uv run ruff check . +uv run mypy src/ --strict +``` +""" + (output_dir / "README.md").write_text(readme) + + return output_dir + + +def _is_text_file(path: Path) -> bool: + """Check if a file is likely text (for template substitution).""" + text_extensions = { + ".py", + ".md", + ".txt", + ".toml", + ".yaml", + ".yml", + ".json", + ".cfg", + ".ini", + ".sh", + ".bash", + ".dockerfile", + ".gitignore", + ".env", + } + if path.suffix.lower() in text_extensions: + return True + return path.name.lower() in {"dockerfile", "makefile", "justfile", ".gitignore", ".env"} diff --git a/src/whet/settings/__init__.py b/src/whet/settings/__init__.py new file mode 100644 index 0000000..a3e0d6b --- /dev/null +++ b/src/whet/settings/__init__.py @@ -0,0 +1 @@ +"""Settings engine for generating and applying platform-specific settings.""" diff --git a/src/whet/settings/engine.py b/src/whet/settings/engine.py new file mode 100644 index 0000000..7349576 --- /dev/null +++ b/src/whet/settings/engine.py @@ -0,0 +1,109 @@ +"""Settings merge and diff engine.""" + +from __future__ import annotations + +import json +from pathlib import Path + +from pydantic import BaseModel, Field + + +class SettingsTemplate(BaseModel, frozen=True): + """A settings template with permissions.""" + + permissions: dict[str, list[str]] = Field(default_factory=dict) + + +def load_template(template_path: Path) -> SettingsTemplate: + """Load a settings template from a JSON file.""" + raw = json.loads(template_path.read_text()) + return SettingsTemplate(**raw) + + +def load_existing(settings_path: Path) -> SettingsTemplate | None: + """Load existing settings file, or None if not found.""" + if not settings_path.exists(): + return None + raw = json.loads(settings_path.read_text()) + return SettingsTemplate(**raw) + + +def merge_settings( + existing: SettingsTemplate, + template: SettingsTemplate, +) -> SettingsTemplate: + """Merge template permissions into existing settings. + + - Keeps all existing allow entries + - Appends new entries from template + - Deduplicates the result + """ + existing_allow = existing.permissions.get("allow", []) + template_allow = template.permissions.get("allow", []) + + merged = list(dict.fromkeys(existing_allow + template_allow)) + + return SettingsTemplate(permissions={"allow": merged}) + + +def diff_settings( + existing: SettingsTemplate | None, + template: SettingsTemplate, +) -> tuple[list[str], list[str], list[str]]: + """Compute the diff between existing and template settings. + + Returns (new_entries, existing_entries, all_merged). + """ + existing_allow = set(existing.permissions.get("allow", [])) if existing else set() + template_allow = set(template.permissions.get("allow", [])) + + new_entries = sorted(template_allow - existing_allow) + kept_entries = sorted(existing_allow) + all_merged = sorted(existing_allow | template_allow) + + return new_entries, kept_entries, all_merged + + +def write_settings(settings_path: Path, template: SettingsTemplate) -> None: + """Write settings to disk as formatted JSON.""" + settings_path.parent.mkdir(parents=True, exist_ok=True) + content = json.dumps( + {"permissions": template.permissions}, + indent=2, + ) + settings_path.write_text(content + "\n") + + +def get_template_path(platform: str) -> Path: + """Get the path to a platform's settings template.""" + templates_dir = Path(__file__).resolve().parents[3] / "settings" + template_file = templates_dir / f"{platform}.json" + if template_file.exists(): + return template_file + # Fall back to base template + return templates_dir / "base.json" + + +def get_settings_target(platform: str, scope: str) -> Path: + """Get the target path for settings based on platform and scope.""" + from whet.core.config import PLATFORM_PATHS, Platform + + plat = Platform(platform) + paths = PLATFORM_PATHS[plat] + + if scope == "global": + if plat == Platform.CLAUDE: + return Path.home() / ".claude" / "settings.json" + if plat == Platform.ANTIGRAVITY: + return Path.home() / ".gemini" / "settings.json" + if plat == Platform.CURSOR: + return Path.home() / ".cursor" / "settings.json" + return paths.global_dir / "settings.json" + else: + if plat == Platform.CLAUDE: + return Path(".claude") / "settings.local.json" + if plat == Platform.ANTIGRAVITY: + return Path(".agent") / "settings.json" + if plat == Platform.CURSOR: + return Path(".cursor") / "settings.json" + return paths.local_dir / "settings.json" diff --git a/tests/test_agents.py b/tests/test_agents.py index 034f8c8..da683b2 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -2,12 +2,18 @@ from __future__ import annotations +import sys from pathlib import Path import pytest AGENTS_DIR = Path("agents") +if sys.version_info >= (3, 12): + import tomllib +else: + import tomli as tomllib + def get_all_agents() -> list[Path]: """Get all agent directories.""" @@ -29,20 +35,28 @@ def test_agent_has_readme(agent_dir: Path) -> None: assert readme.exists(), f"Missing README.md in {agent_dir.name}" -def test_blocking_agents_have_actions() -> None: - """Test that blocking agents have GitHub Actions.""" - blocking_agents = ["code-review", "test-engineer"] - - for agent_name in blocking_agents: - agent_dir = AGENTS_DIR / agent_name - action_yml = agent_dir / "action.yml" - assert action_yml.exists(), f"Missing action.yml for blocking agent {agent_name}" - +@pytest.mark.parametrize("agent_dir", get_all_agents(), ids=lambda d: d.name) +def test_agent_has_toml(agent_dir: Path) -> None: + """Test agent has agent.toml metadata file.""" + toml_path = agent_dir / "agent.toml" + assert toml_path.exists(), f"Missing agent.toml in {agent_dir.name}" -def test_all_expected_agents_exist() -> None: - """Test all expected agents exist.""" - expected = {"expert-coder", "ml-engineer", "code-review", "test-engineer"} - actual = {d.name for d in get_all_agents()} - missing = expected - actual - assert not missing, f"Missing agents: {missing}" +def test_blocking_agents_have_actions() -> None: + """Test that blocking agents have GitHub Actions.""" + for agent_dir in get_all_agents(): + toml_path = agent_dir / "agent.toml" + if toml_path.exists(): + with open(toml_path, "rb") as f: + data = tomllib.load(f) + if data.get("agent", {}).get("type") == "blocking": + action_yml = agent_dir / "action.yml" + assert action_yml.exists(), ( + f"Missing action.yml for blocking agent {agent_dir.name}" + ) + + +def test_minimum_agent_count() -> None: + """Test that we have at least the expected number of agents.""" + agents = get_all_agents() + assert len(agents) >= 4, f"Expected at least 4 agents, found {len(agents)}" diff --git a/tests/test_archetypes.py b/tests/test_archetypes.py index d38b2de..f6555b9 100644 --- a/tests/test_archetypes.py +++ b/tests/test_archetypes.py @@ -26,10 +26,16 @@ def test_archetype_has_readme(archetype_dir: Path) -> None: assert len(content) > 500, f"README too short in {archetype_dir.name}" +@pytest.mark.parametrize("archetype_dir", get_all_archetypes(), ids=lambda d: d.name) +def test_archetype_has_toml(archetype_dir: Path) -> None: + """Test archetype has archetype.toml metadata file.""" + toml_path = archetype_dir / "archetype.toml" + assert toml_path.exists(), f"Missing archetype.toml in {archetype_dir.name}" + + @pytest.mark.parametrize("archetype_dir", get_all_archetypes(), ids=lambda d: d.name) def test_archetype_has_structure(archetype_dir: Path) -> None: """Test archetype has expected structure.""" - # Should have template directory or structure documentation has_template = (archetype_dir / "template").exists() readme = archetype_dir / "README.md" has_structure_doc = "```" in readme.read_text() if readme.exists() else False @@ -37,18 +43,7 @@ def test_archetype_has_structure(archetype_dir: Path) -> None: assert has_template or has_structure_doc, f"Archetype {archetype_dir.name} missing structure" -def test_all_expected_archetypes_exist() -> None: - """Test all expected archetypes exist.""" - expected = { - "pytorch-training-project", - "cv-inference-service", - "research-notebook", - "library-package", - "data-processing-pipeline", - "model-zoo", - } - - actual = {d.name for d in get_all_archetypes()} - - missing = expected - actual - assert not missing, f"Missing archetypes: {missing}" +def test_minimum_archetype_count() -> None: + """Test that we have at least the expected number of archetypes.""" + archetypes = get_all_archetypes() + assert len(archetypes) >= 6, f"Expected at least 6 archetypes, found {len(archetypes)}" diff --git a/tests/test_skills_completeness.py b/tests/test_skills_completeness.py index d896507..1c1953a 100644 --- a/tests/test_skills_completeness.py +++ b/tests/test_skills_completeness.py @@ -30,7 +30,7 @@ def test_skill_md_has_content(skill_dir: Path) -> None: skill_md = skill_dir / "SKILL.md" content = skill_md.read_text() - # Check minimum length (1000+ chars for substantial content) + # Check minimum length assert len(content) > 500, f"SKILL.md in {skill_dir.name} is too short ({len(content)} chars)" # Check has code examples @@ -40,49 +40,50 @@ def test_skill_md_has_content(skill_dir: Path) -> None: assert "# " in content, f"SKILL.md in {skill_dir.name} missing headers" +@pytest.mark.parametrize("skill_dir", get_all_skills(), ids=lambda d: d.name) +def test_skill_md_has_frontmatter(skill_dir: Path) -> None: + """Test that SKILL.md has YAML frontmatter with name and description.""" + skill_md = skill_dir / "SKILL.md" + content = skill_md.read_text() + + assert content.startswith("---"), f"SKILL.md in {skill_dir.name} missing YAML frontmatter" + + # Find closing delimiter + lines = content.split("\n") + end_idx = -1 + for i, line in enumerate(lines[1:], start=1): + if line.strip() == "---": + end_idx = i + break + assert end_idx > 0, f"SKILL.md in {skill_dir.name} has unclosed frontmatter" + + frontmatter = "\n".join(lines[1:end_idx]) + assert "name:" in frontmatter, f"SKILL.md in {skill_dir.name} frontmatter missing 'name'" + assert "description:" in frontmatter, ( + f"SKILL.md in {skill_dir.name} frontmatter missing 'description'" + ) + + +@pytest.mark.parametrize("skill_dir", get_all_skills(), ids=lambda d: d.name) +def test_skill_has_toml(skill_dir: Path) -> None: + """Test that skill has a skill.toml metadata file.""" + toml_path = skill_dir / "skill.toml" + assert toml_path.exists(), f"Missing skill.toml in {skill_dir.name}" + + @pytest.mark.parametrize("skill_dir", get_all_skills(), ids=lambda d: d.name) def test_readme_explains_skill(skill_dir: Path) -> None: """Test that README explains the skill.""" readme = skill_dir / "README.md" content = readme.read_text().lower() - # Should explain purpose assert any(word in content for word in ["purpose", "what", "when", "how", "overview", "use"]), ( f"README in {skill_dir.name} doesn't explain purpose" ) -def test_all_expected_skills_exist() -> None: - """Test that all expected skills are present.""" - expected 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}, + { name = "rich" }, + { name = "tomli", marker = "python_full_version < '3.12'" }, + { name = "typer" }, +] + +[package.optional-dependencies] +dev = [ + { name = "mypy" }, + { name = "pytest" }, + { name = "pytest-cov" }, + { name = "ruff" }, + { name = "types-pyyaml" }, +] +docs = [ + { name = "mkdocs-material" }, + { name = "mkdocstrings" }, + { name = "mkdocstrings-python" }, +] + +[package.metadata] +requires-dist = [ + { name = "mkdocs-material", marker = "extra == 'docs'", specifier = ">=9.5" }, + { name = "mkdocstrings", marker = "extra == 'docs'", specifier = ">=0.24" }, + { name = "mkdocstrings-python", marker = "extra == 'docs'", specifier = ">=1.11" }, + { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11" }, + { name = "pydantic", specifier = ">=2.0" }, + { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.4" }, + { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=4.1" }, + { name = "rich", specifier = ">=13.0" }, + { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8" }, + { name = "tomli", marker = "python_full_version < '3.12'", specifier = ">=2.0" }, + { name = "typer", specifier = ">=0.15" }, + { name = "types-pyyaml", marker = "extra == 'dev'", specifier = ">=6.0" }, +] +provides-extras = ["dev", "docs"] From 2bff4a41abe60530892d987f374cf7ac35308e69 Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Mon, 9 Feb 2026 20:17:25 -0500 Subject: [PATCH 02/21] fix: add mypy overrides and type annotations for CI - Add proper type annotations to _install_to and _get_adapter in install.py (fixes no-untyped-call errors under --strict) - Add mypy overrides for typer (no stubs), yaml, and tomli modules - Disable untyped-decorator warnings for whet.cli.* (Typer decorators) Co-Authored-By: Claude Opus 4.6 --- pyproject.toml | 17 +++++++++++++++++ src/whet/cli/install.py | 15 ++++++++++----- 2 files changed, 27 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 569365f..0a92fcc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -85,6 +85,23 @@ strict = true warn_return_any = true warn_unused_ignores = true +[[tool.mypy.overrides]] +module = ["typer", "typer.*"] +ignore_missing_imports = true +disallow_untyped_decorators = false + +[[tool.mypy.overrides]] +module = ["whet.cli.*"] +disallow_untyped_decorators = false + +[[tool.mypy.overrides]] +module = ["yaml"] +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = ["tomli"] +ignore_missing_imports = true + [tool.pytest.ini_options] testpaths = ["tests"] python_files = ["test_*.py"] diff --git a/src/whet/cli/install.py b/src/whet/cli/install.py index 4a8ed74..21a7767 100644 --- a/src/whet/cli/install.py +++ b/src/whet/cli/install.py @@ -2,11 +2,15 @@ from __future__ import annotations +from pathlib import Path + import typer from rich.console import Console +from whet.adapters.base import PlatformAdapter from whet.cli import app -from whet.core.config import WhetConfig +from whet.core.config import Platform, WhetConfig +from whet.core.skill import Skill from whet.registry.loader import discover_skills console = Console() @@ -42,7 +46,9 @@ def install( _install_to(adapter, all_skills, paths.local_dir, "local") -def _install_to(adapter, skills, target_dir, scope_label): # type: ignore[no-untyped-def] +def _install_to( + adapter: PlatformAdapter, skills: list[Skill], target_dir: Path, scope_label: str +) -> None: """Install skills to a target directory.""" console.print(f"\n[bold]Installing {len(skills)} skills ({scope_label})...[/bold]") @@ -57,14 +63,13 @@ def _get_config() -> WhetConfig: return WhetConfig() -def _get_adapter(platform): # type: ignore[no-untyped-def] +def _get_adapter(platform: Platform) -> PlatformAdapter: from whet.adapters.antigravity import AntigravityAdapter from whet.adapters.claude import ClaudeAdapter from whet.adapters.copilot import CopilotAdapter from whet.adapters.cursor import CursorAdapter - from whet.core.config import Platform - adapters = { + adapters: dict[Platform, type[PlatformAdapter]] = { Platform.CLAUDE: ClaudeAdapter, Platform.ANTIGRAVITY: AntigravityAdapter, Platform.CURSOR: CursorAdapter, From fe3949c20e9b8458668a05ace56d4a6fb292d69f Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Mon, 9 Feb 2026 20:38:34 -0500 Subject: [PATCH 03/21] feat: expand whet install to include agents and settings - `whet install` now installs both skills (30) and agents (6) by default - Add --skills-only and --agents-only flags for granular control - Add --with-settings / -s flag to apply settings template with merge - Add discover_agents() to registry loader (reuses Skill model) - Settings merge uses existing engine (preserves user customizations) Closes #4 Co-Authored-By: Claude Opus 4.6 --- src/whet/cli/install.py | 97 +++++++++++++++++++++++++++++-------- src/whet/registry/loader.py | 25 +++++++--- 2 files changed, 95 insertions(+), 27 deletions(-) diff --git a/src/whet/cli/install.py b/src/whet/cli/install.py index 21a7767..8cc399b 100644 --- a/src/whet/cli/install.py +++ b/src/whet/cli/install.py @@ -1,4 +1,4 @@ -"""Install command — bulk install skills to a platform.""" +"""Install command — bulk install skills, agents, and settings to a platform.""" from __future__ import annotations @@ -11,7 +11,7 @@ from whet.cli import app from whet.core.config import Platform, WhetConfig from whet.core.skill import Skill -from whet.registry.loader import discover_skills +from whet.registry.loader import discover_agents, discover_skills console = Console() @@ -21,42 +21,97 @@ def install( scope_global: bool = typer.Option(False, "--global", "-g", help="Install to global directory."), scope_local: bool = typer.Option(False, "--local", "-l", help="Install to local project."), category: str | None = typer.Option(None, "--category", "--cat", help="Only install category."), + skills_only: bool = typer.Option(False, "--skills-only", help="Only install skills."), + agents_only: bool = typer.Option(False, "--agents-only", help="Only install agents."), + with_settings: bool = typer.Option( + False, "--with-settings", "-s", help="Also apply settings template." + ), ) -> None: - """Install the full skill collection.""" + """Install skills, agents, and optionally settings.""" if not scope_global and not scope_local: scope_local = True cfg = _get_config() adapter = _get_adapter(cfg.target) - all_skills = discover_skills(cfg.skills_dir) + paths = cfg.get_platform_paths() - if category: - all_skills = [s for s in all_skills if s.category == category] + include_skills = not agents_only + include_agents = not skills_only - if not all_skills: - console.print("[yellow]No skills found to install.[/yellow]") - raise typer.Exit(code=1) + all_skills: list[Skill] = [] + all_agents: list[Skill] = [] - paths = cfg.get_platform_paths() + if include_skills: + all_skills = discover_skills(cfg.skills_dir) + if category: + all_skills = [s for s in all_skills if s.category == category] + + if include_agents: + all_agents = discover_agents(cfg.agents_dir) + + if not all_skills and not all_agents: + console.print("[yellow]No skills or agents found to install.[/yellow]") + raise typer.Exit(code=1) if scope_global: - _install_to(adapter, all_skills, paths.global_dir, "global") + if all_skills: + _install_to(adapter, all_skills, paths.global_dir, "skills", "global") + if all_agents: + _install_to(adapter, all_agents, paths.global_dir, "agents", "global") + if with_settings: + _apply_settings(cfg.target.value, "global") if scope_local: - _install_to(adapter, all_skills, paths.local_dir, "local") + if all_skills: + _install_to(adapter, all_skills, paths.local_dir, "skills", "local") + if all_agents: + _install_to(adapter, all_agents, paths.local_dir, "agents", "local") + if with_settings: + _apply_settings(cfg.target.value, "local") def _install_to( - adapter: PlatformAdapter, skills: list[Skill], target_dir: Path, scope_label: str + adapter: PlatformAdapter, + items: list[Skill], + target_dir: Path, + item_type: str, + scope_label: str, ) -> None: - """Install skills to a target directory.""" - console.print(f"\n[bold]Installing {len(skills)} skills ({scope_label})...[/bold]") - - for skill in skills: - adapter.install_skill(skill, target_dir) - console.print(f" [green]✓[/green] {skill.name}") - - console.print(f"\n[bold green]✓ Installed {len(skills)} skills to {target_dir}[/bold green]") + """Install skills or agents to a target directory.""" + console.print(f"\n[bold]Installing {len(items)} {item_type} ({scope_label})...[/bold]") + + for item in items: + adapter.install_skill(item, target_dir) + console.print(f" [green]✓[/green] {item.name}") + + console.print( + f"\n[bold green]✓ Installed {len(items)} {item_type} to {target_dir}[/bold green]" + ) + + +def _apply_settings(platform: str, scope: str) -> None: + """Apply settings template with merge support.""" + from whet.settings.engine import ( + get_settings_target, + get_template_path, + load_existing, + load_template, + merge_settings, + write_settings, + ) + + template_path = get_template_path(platform) + settings_path = get_settings_target(platform, scope) + template = load_template(template_path) + existing = load_existing(settings_path) + + if existing: + merged = merge_settings(existing, template) + write_settings(settings_path, merged) + console.print(f"\n[bold green]✓ Merged settings into {settings_path}[/bold green]") + else: + write_settings(settings_path, template) + console.print(f"\n[bold green]✓ Applied settings to {settings_path}[/bold green]") def _get_config() -> WhetConfig: diff --git a/src/whet/registry/loader.py b/src/whet/registry/loader.py index f6a227c..0d04832 100644 --- a/src/whet/registry/loader.py +++ b/src/whet/registry/loader.py @@ -1,4 +1,4 @@ -"""Scan and parse skills from disk.""" +"""Scan and parse skills and agents from disk.""" from __future__ import annotations @@ -12,17 +12,30 @@ def discover_skills(skills_dir: Path) -> list[Skill]: Scans for subdirectories containing SKILL.md and loads each as a Skill. """ - if not skills_dir.is_dir(): + return _discover(skills_dir) + + +def discover_agents(agents_dir: Path) -> list[Skill]: + """Discover all agents in a directory. + + Agents use the same SKILL.md format as skills. + """ + return _discover(agents_dir) + + +def _discover(base_dir: Path) -> list[Skill]: + """Discover all SKILL.md-based items in a directory.""" + if not base_dir.is_dir(): return [] - skills: list[Skill] = [] - for child in sorted(skills_dir.iterdir()): + items: list[Skill] = [] + for child in sorted(base_dir.iterdir()): if child.is_dir() and not child.name.startswith("."): skill_md = child / "SKILL.md" if skill_md.exists(): - skills.append(Skill.from_directory(child)) + items.append(Skill.from_directory(child)) - return skills + return items def load_skill(skills_dir: Path, name: str) -> Skill | None: From a3552b64deb95dbe1f3dd522eec00ee9f43081a0 Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Mon, 9 Feb 2026 20:42:19 -0500 Subject: [PATCH 04/21] fix: settings engine handles real-world settings files - load_existing() now extracts only permissions.allow, ignoring extra keys like defaultMode, hooks, statusLine that exist in real settings - write_settings() preserves all existing keys in the file, only updating permissions.allow (non-destructive merge) - Fixes crash when merging into ~/.claude/settings.json with hooks Co-Authored-By: Claude Opus 4.6 --- src/whet/settings/engine.py | 45 +++++++++++++++++++++++++++++-------- 1 file changed, 36 insertions(+), 9 deletions(-) diff --git a/src/whet/settings/engine.py b/src/whet/settings/engine.py index 7349576..48fec29 100644 --- a/src/whet/settings/engine.py +++ b/src/whet/settings/engine.py @@ -4,6 +4,7 @@ import json from pathlib import Path +from typing import Any from pydantic import BaseModel, Field @@ -20,12 +21,26 @@ def load_template(template_path: Path) -> SettingsTemplate: return SettingsTemplate(**raw) -def load_existing(settings_path: Path) -> SettingsTemplate | None: - """Load existing settings file, or None if not found.""" +def load_existing_raw(settings_path: Path) -> dict[str, Any] | None: + """Load existing settings file as raw dict, or None if not found.""" if not settings_path.exists(): return None - raw = json.loads(settings_path.read_text()) - return SettingsTemplate(**raw) + return json.loads(settings_path.read_text()) # type: ignore[no-any-return] + + +def load_existing(settings_path: Path) -> SettingsTemplate | None: + """Load existing settings file as SettingsTemplate, or None if not found. + + Only extracts the permissions.allow list; ignores other keys. + """ + raw = load_existing_raw(settings_path) + if raw is None: + return None + perms = raw.get("permissions", {}) + allow = perms.get("allow", []) + if not isinstance(allow, list): + allow = [] + return SettingsTemplate(permissions={"allow": allow}) def merge_settings( @@ -65,12 +80,24 @@ def diff_settings( def write_settings(settings_path: Path, template: SettingsTemplate) -> None: - """Write settings to disk as formatted JSON.""" + """Write settings to disk, preserving existing non-permission keys.""" settings_path.parent.mkdir(parents=True, exist_ok=True) - content = json.dumps( - {"permissions": template.permissions}, - indent=2, - ) + + # Load existing file to preserve keys we don't manage (hooks, statusLine, etc.) + existing_raw: dict[str, Any] = {} + if settings_path.exists(): + existing_raw = json.loads(settings_path.read_text()) + + # Merge: keep all existing keys, update permissions.allow + existing_perms = existing_raw.get("permissions", {}) + if isinstance(existing_perms, dict): + existing_perms["allow"] = template.permissions.get("allow", []) + else: + existing_perms = template.permissions + + existing_raw["permissions"] = existing_perms + + content = json.dumps(existing_raw, indent=2) settings_path.write_text(content + "\n") From 4eb81af314fff8cf29e5bc9f8055737a69b0110c Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Tue, 10 Feb 2026 13:26:36 -0500 Subject: [PATCH 05/21] fix: persist target platform selection to disk (#5) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary - `whet target ` and `whet config target ` now persist the selection to `~/.config/whet/config.json` - All 8 CLI call sites switched from `WhetConfig()` (hardcoded Claude default) to `WhetConfig.load()` (reads from disk, falls back to defaults) - `whet target` (no args) now shows the current persisted target alongside auto-detection ## Test plan - [x] 203 tests pass (`uv run pytest tests/ -v`) - [x] mypy strict passes (`uv run mypy src/whet/ tests/ --strict`) - [x] ruff clean (`uv run ruff check . && uv run ruff format --check .`) - [x] Manual: `whet target antigravity` → `whet config` shows `target: antigravity` - [x] Manual: `whet target claude` → `whet install --global` uses `~/.claude/skills` 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.6 --- .gitignore | 4 ++++ src/whet/cli/config.py | 8 ++++---- src/whet/cli/doctor.py | 2 +- src/whet/cli/init.py | 2 +- src/whet/cli/install.py | 2 +- src/whet/cli/settings.py | 6 +++--- src/whet/cli/skills.py | 2 +- src/whet/cli/target.py | 7 ++++++- src/whet/core/config.py | 17 +++++++++++++++++ 9 files changed, 38 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index 57b6c11..19b49e5 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,10 @@ venv/ env/ .pixi/ +# Ruff / mypy caches +.ruff_cache/ +.mypy_cache/ + # IDE .vscode/ .idea/ diff --git a/src/whet/cli/config.py b/src/whet/cli/config.py index 6149ecb..487c926 100644 --- a/src/whet/cli/config.py +++ b/src/whet/cli/config.py @@ -6,7 +6,7 @@ from rich.console import Console from whet.cli import app -from whet.core.config import Platform +from whet.core.config import Platform, WhetConfig console = Console() @@ -21,9 +21,7 @@ def config( Without arguments, shows all configuration. With a key, shows that value. With key and value, sets it. """ - from whet.core.config import WhetConfig - - cfg = WhetConfig() + cfg = WhetConfig.load() if key is None: console.print("[bold]whet configuration[/bold]\n") @@ -41,6 +39,8 @@ def config( valid = ", ".join(p.value for p in Platform) console.print(f"[red]Invalid platform '{value}'. Valid: {valid}[/red]") raise typer.Exit(code=1) from err + cfg = cfg.model_copy(update={"target": platform}) + cfg.save() console.print(f"[green]✓[/green] target = {platform.value}") else: console.print(f"target = {cfg.target.value}") diff --git a/src/whet/cli/doctor.py b/src/whet/cli/doctor.py index c7abbac..42bea73 100644 --- a/src/whet/cli/doctor.py +++ b/src/whet/cli/doctor.py @@ -28,7 +28,7 @@ def doctor() -> None: issues = 0 warnings = 0 - cfg = WhetConfig() + cfg = WhetConfig.load() # Check 1: Skills directory if cfg.skills_dir.is_dir(): diff --git a/src/whet/cli/init.py b/src/whet/cli/init.py index ba44828..5b23744 100644 --- a/src/whet/cli/init.py +++ b/src/whet/cli/init.py @@ -39,7 +39,7 @@ def init_project( Without arguments, lists available archetypes. """ - cfg = WhetConfig() + cfg = WhetConfig.load() if archetype_name is None: _list_archetypes(cfg) diff --git a/src/whet/cli/install.py b/src/whet/cli/install.py index 8cc399b..5ba643b 100644 --- a/src/whet/cli/install.py +++ b/src/whet/cli/install.py @@ -115,7 +115,7 @@ def _apply_settings(platform: str, scope: str) -> None: def _get_config() -> WhetConfig: - return WhetConfig() + return WhetConfig.load() def _get_adapter(platform: Platform) -> PlatformAdapter: diff --git a/src/whet/cli/settings.py b/src/whet/cli/settings.py index 4cf5c7b..6729c59 100644 --- a/src/whet/cli/settings.py +++ b/src/whet/cli/settings.py @@ -35,7 +35,7 @@ def generate( ), ) -> None: """Generate optimized settings for a platform.""" - cfg = WhetConfig() + cfg = WhetConfig.load() plat = platform or cfg.target.value try: @@ -73,7 +73,7 @@ def diff( scope: str = typer.Option("local", "--scope", "-s", help="Scope: local or global."), ) -> None: """Preview changes between current settings and template.""" - cfg = WhetConfig() + cfg = WhetConfig.load() plat = platform or cfg.target.value template_path = get_template_path(plat) @@ -129,7 +129,7 @@ def apply( if not scope_global and not scope_local: scope_local = True - cfg = WhetConfig() + cfg = WhetConfig.load() plat = platform or cfg.target.value template_path = get_template_path(plat) diff --git a/src/whet/cli/skills.py b/src/whet/cli/skills.py index e009c3f..51408ab 100644 --- a/src/whet/cli/skills.py +++ b/src/whet/cli/skills.py @@ -16,7 +16,7 @@ def _get_config() -> WhetConfig: - return WhetConfig() + return WhetConfig.load() def _get_adapter(platform: Platform): # type: ignore[no-untyped-def] diff --git a/src/whet/cli/target.py b/src/whet/cli/target.py index 5e0bcb7..4d789ce 100644 --- a/src/whet/cli/target.py +++ b/src/whet/cli/target.py @@ -7,7 +7,7 @@ from whet.adapters.detect import detect_platform from whet.cli import app -from whet.core.config import PLATFORM_PATHS, Platform +from whet.core.config import PLATFORM_PATHS, Platform, WhetConfig console = Console() @@ -21,6 +21,9 @@ def target( Without arguments, shows the current target and auto-detected platform. """ if platform: + cfg = WhetConfig.load() + cfg = cfg.model_copy(update={"target": platform}) + cfg.save() paths = PLATFORM_PATHS[platform] console.print(f"[bold green]✓ Target set to {platform.value}[/bold green]") console.print(f" Global: {paths.global_dir}") @@ -28,7 +31,9 @@ def target( return # Show current state + cfg = WhetConfig.load() detected = detect_platform() + console.print(f"[bold]Current target:[/bold] [cyan]{cfg.target.value}[/cyan]\n") console.print("[bold]Platform Detection[/bold]\n") if detected: diff --git a/src/whet/core/config.py b/src/whet/core/config.py index c711356..e9a5fcc 100644 --- a/src/whet/core/config.py +++ b/src/whet/core/config.py @@ -2,11 +2,14 @@ from __future__ import annotations +import json from enum import Enum from pathlib import Path from pydantic import BaseModel, Field +CONFIG_PATH = Path.home() / ".config" / "whet" / "config.json" + class Platform(str, Enum): """Supported AI coding agent platforms.""" @@ -54,6 +57,20 @@ class WhetConfig(BaseModel): default_factory=lambda: Path(__file__).resolve().parents[3] / "archetypes" ) + @classmethod + def load(cls) -> WhetConfig: + """Load config from disk, falling back to defaults.""" + if CONFIG_PATH.exists(): + data = json.loads(CONFIG_PATH.read_text()) + return cls(**data) + return cls() + + def save(self) -> None: + """Persist user-set values to disk.""" + CONFIG_PATH.parent.mkdir(parents=True, exist_ok=True) + data = {"target": self.target.value} + CONFIG_PATH.write_text(json.dumps(data, indent=2) + "\n") + def get_platform_paths(self) -> PlatformPaths: """Get paths for the current target platform.""" return PLATFORM_PATHS[self.target] From 5bf59a8e9c19bc945ff1d31a89c4e29610e23e4e Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Thu, 23 Jul 2026 16:23:47 -0400 Subject: [PATCH 06/21] Skills refactor: trim bloat, add model-evaluation + pydantic-ai, pushy trigger descriptions (#6) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit P0/P1 cleanup from the skill-library review. **32 skills** total. ## What changed ### 1. Trim bloat (progressive disclosure) All **19 SKILL.md bodies over Anthropic's 500-line guideline** are now under it — restated "what is X" prose and near-duplicate code blocks removed, every distinct pattern/gotcha/anti-pattern kept. | Skill | Before | After | |---|---|---| | kubernetes | 776 | 480 | | onnx | 720 | 499 | | abstraction-patterns | 687 | 464 | | matplotlib | 675 | 452 | | github-actions | 632 | 412 | | pypi | 616 | 335 | | …13 more | — | all <500 | Avg SKILL.md 551 → ~440 lines; ~2.2k net lines removed. (Next step, not in this PR: split survivors into thin index + `references/`.) ### 2. New skills (real coverage gaps) - **model-evaluation** — detection mAP/IoU, confusion matrices, per-class + per-size breakdowns, threshold selection, per-slice failure analysis, eval-as-CI gate. Built on **`supervision`** (`import supervision as sv`), the detection-native library already used alongside RF-DETR/YOLOX, with `torchmetrics` retained for classification/calibration. Feeds GSD's `eval-planner`; previously only generic `testing` existed. - **pydantic-ai** — typed LLM/VLM structured outputs; VLM-in-the-loop auto-labeling (Gemini/Moondream → validated detections). The supervision usage was verified against **0.29.1** and encodes the real traps: import from `supervision.metrics` (the top-level `sv.MeanAveragePrecision` is deprecated for 0.31.0 and inconsistent with pycocotools); `MetricTarget.MASKS` is silently ignored by mAP in released versions (it scores boxes — use pycocotools for mask mAP); don't pre-filter confidence before mAP; `-1` is an "absent" sentinel, not a score. ### 3. `pydantic-strict` → `pydantic` Renamed for a simpler name and broader scope (strict remains the default stance). Updates the dir, docs page, nav, archetype refs, and all `skill.toml` cross-refs — no dangling references. ### 4. Pushy "use-when" descriptions Every skill's `description:` rewritten as a third-person, trigger-first "Use this skill when…" with keyword coverage and explicit disambiguation between adjacent skills (code-quality vs pre-commit vs vscode; docker-cv vs kubernetes vs gcp; pydantic vs pydantic-ai; mlflow vs wandb vs tensorboard). ### 5. Env manager: pixi stays canonical Kept **pixi** as the ML env manager (handles CUDA/system deps beyond Python). Incidental run-commands are tool-agnostic (`pytest`, not `pixi run pytest`); dependency installs use `pixi add`. ### 6. No vertex-ai skill — folded into `gcp` instead A separate `vertex-ai` skill was **duplicating** the `gcp` skill, which already covers Vertex AI training jobs (custom + custom-container, GPU selection reference, prebuilt containers) alongside GCS and Artifact Registry. Instead, `gcp` gains the one genuinely non-obvious bit: > **Retrieving Artifacts After Training** — the training VM is ephemeral, so write to `AIP_MODEL_DIR` and explicitly sync artifacts down when the job ends. ## Verification - `uv run pytest tests/` → **213 passed** - `uv run ruff check .` → clean - `uv run ruff format --check .` → clean ## Notes for review - The `pydantic` rename touches archetype `.toml` skill lists and cross-refs — worth a glance that nothing dangles. - Worth sanity-checking the trimmed skills you use most (pytorch-lightning, onnx, fastapi) for anything cut that you wanted kept. - `model-evaluation`'s supervision snippets assume `sv.DetectionDataset.from_coco`; swap to `from_yolo` if that matches your loader. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 --- CLAUDE.md | 4 +- README.md | 2 +- agents/data-engineer/README.md | 2 +- agents/data-engineer/SKILL.md | 2 +- agents/expert-coder/README.md | 2 +- .../cv-inference-service/archetype.toml | 2 +- .../data-processing-pipeline/archetype.toml | 2 +- archetypes/library-package/archetype.toml | 2 +- archetypes/model-zoo/archetype.toml | 2 +- .../pytorch-training-project/archetype.toml | 2 +- docs/agents/data-engineer.md | 2 +- docs/agents/expert-coder.md | 4 +- docs/examples/full-workflow.md | 4 +- docs/getting-started/quick-start.md | 4 +- docs/guides/creating-projects.md | 2 +- docs/index.md | 10 +- docs/skills/abstraction-patterns.md | 2 +- docs/skills/fastapi.md | 2 +- docs/skills/hydra-config.md | 2 +- docs/skills/index.md | 6 +- docs/skills/loguru.md | 2 +- docs/skills/model-evaluation.md | 72 +++ docs/skills/onnx.md | 2 +- docs/skills/pydantic-ai.md | 32 ++ .../{pydantic-strict.md => pydantic.md} | 8 +- mkdocs.yml | 4 +- skills/README.md | 2 +- skills/abstraction-patterns/README.md | 2 +- skills/abstraction-patterns/SKILL.md | 328 +++---------- skills/abstraction-patterns/skill.toml | 2 +- skills/aws-sagemaker/SKILL.md | 387 +++++---------- skills/aws-sagemaker/skill.toml | 2 +- skills/code-quality/README.md | 2 +- skills/code-quality/SKILL.md | 140 ++---- skills/docker-cv/SKILL.md | 10 +- skills/dvc/SKILL.md | 9 +- skills/fastapi/README.md | 2 +- skills/fastapi/SKILL.md | 218 ++------- skills/fastapi/skill.toml | 2 +- skills/gcp/SKILL.md | 273 +++-------- skills/github-actions/SKILL.md | 439 +++++------------ skills/github-repo-setup/README.md | 1 + skills/github-repo-setup/SKILL.md | 9 +- skills/gradio/README.md | 2 +- skills/gradio/SKILL.md | 445 +++++------------- skills/gradio/skill.toml | 2 +- skills/huggingface/SKILL.md | 104 +--- skills/huggingface/skill.toml | 2 +- skills/hydra-config/README.md | 2 +- skills/hydra-config/SKILL.md | 9 +- skills/hydra-config/skill.toml | 2 +- skills/kubernetes/SKILL.md | 439 +++-------------- skills/library-review/README.md | 2 +- skills/library-review/SKILL.md | 97 +--- skills/loguru/README.md | 2 +- skills/loguru/SKILL.md | 98 +--- skills/master-skill/README.md | 4 +- skills/master-skill/SKILL.md | 10 +- skills/master-skill/skill.toml | 2 +- skills/matplotlib/SKILL.md | 344 +++----------- skills/mlflow/SKILL.md | 10 +- skills/model-evaluation/README.md | 45 ++ skills/model-evaluation/SKILL.md | 272 +++++++++++ skills/model-evaluation/skill.toml | 13 + skills/onnx/SKILL.md | 285 ++--------- skills/opencv/SKILL.md | 304 ++++-------- skills/pixi/SKILL.md | 9 +- skills/pre-commit/SKILL.md | 9 +- skills/pydantic-ai/README.md | 28 ++ skills/pydantic-ai/SKILL.md | 206 ++++++++ skills/pydantic-ai/skill.toml | 13 + .../{pydantic-strict => pydantic}/README.md | 4 +- skills/{pydantic-strict => pydantic}/SKILL.md | 173 +------ .../{pydantic-strict => pydantic}/skill.toml | 2 +- skills/pypi/SKILL.md | 442 ++++------------- skills/pytorch-lightning/README.md | 2 +- skills/pytorch-lightning/SKILL.md | 165 ++----- skills/pytorch-lightning/skill.toml | 2 +- skills/tensorboard/SKILL.md | 9 +- skills/tensorrt/SKILL.md | 264 +++-------- skills/testing/SKILL.md | 277 +++-------- skills/testing/skill.toml | 2 +- skills/vscode/SKILL.md | 9 +- skills/wandb/SKILL.md | 9 +- 84 files changed, 1927 insertions(+), 4201 deletions(-) create mode 100644 docs/skills/model-evaluation.md create mode 100644 docs/skills/pydantic-ai.md rename docs/skills/{pydantic-strict.md => pydantic.md} (88%) create mode 100644 skills/model-evaluation/README.md create mode 100644 skills/model-evaluation/SKILL.md create mode 100644 skills/model-evaluation/skill.toml create mode 100644 skills/pydantic-ai/README.md create mode 100644 skills/pydantic-ai/SKILL.md create mode 100644 skills/pydantic-ai/skill.toml rename skills/{pydantic-strict => pydantic}/README.md (77%) rename skills/{pydantic-strict => pydantic}/SKILL.md (75%) rename skills/{pydantic-strict => pydantic}/skill.toml (90%) diff --git a/CLAUDE.md b/CLAUDE.md index 363c24a..420186a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -13,7 +13,7 @@ Distribution: `uvx whet` (one-shot) / `uv tool install whet` (permanent) ### What whet provides -- **30 Skills** — Best-practice knowledge modules (PyTorch Lightning, Pydantic, Docker, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, etc.) +- **32 Skills** — Best-practice knowledge modules (PyTorch Lightning, Pydantic, Docker, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, Model Evaluation, etc.) - **6 Agents** — Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer) - **6+ Archetypes** — Complete project templates for common CV/ML project types - **CLI** — `whet add`, `whet install`, `whet list`, `whet search`, `whet doctor` @@ -36,7 +36,7 @@ whet/ ├── tests/ # Self-tests (dynamic discovery, no hardcoded counts) ├── .github/workflows/ # CI/CD (test, lint, docs) — uses uv ├── pyproject.toml # uv-managed, publishable to PyPI -├── justfile # Task runner (replaces pixi tasks) +├── justfile # Task runner (wraps uv commands) └── mkdocs.yml # Documentation site config ``` diff --git a/README.md b/README.md index 567ead1..9315944 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ whet is a CLI tool that installs curated expert skill definitions into AI coding ### Flagship Collection: CV/ML -- **30 Skills** — PyTorch Lightning, Pydantic, Docker, ONNX, TensorRT, OpenCV, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, and more +- **32 Skills** — PyTorch Lightning, Pydantic, Docker, ONNX, TensorRT, OpenCV, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, Model Evaluation, and more - **6 Agents** — Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer - **6+ Archetypes** — Training pipelines, inference services, notebooks, packages diff --git a/agents/data-engineer/README.md b/agents/data-engineer/README.md index a642530..5ef162f 100644 --- a/agents/data-engineer/README.md +++ b/agents/data-engineer/README.md @@ -41,6 +41,6 @@ Data Engineer Agent: "For medical imaging at this scale, I recommend: ## Related Skills - `dvc` — Dataset versioning, pipeline reproducibility, and remote storage -- `pydantic-strict` — Validated configuration objects and schema definitions +- `pydantic` — Validated configuration objects and schema definitions - `testing` — Unit tests for transforms, integration tests for pipeline stages - `pytorch-lightning` — DataModule integration for feeding validated data into training diff --git a/agents/data-engineer/SKILL.md b/agents/data-engineer/SKILL.md index c6512fe..17888b3 100644 --- a/agents/data-engineer/SKILL.md +++ b/agents/data-engineer/SKILL.md @@ -933,7 +933,7 @@ def get_migration_path(from_version: str, to_version: str) -> list[tuple[str, st ## Integration with Other Skills - **DVC** — Data versioning and pipeline reproducibility for tracking dataset lineage. -- **Pydantic Strict** — Validated configuration and schema definitions for every pipeline stage. +- **Pydantic** — Validated configuration and schema definitions for every pipeline stage. - **Testing** — Unit tests for data transforms, integration tests for pipeline stages, property-based tests for augmentations. - **PyTorch Lightning** — DataModule integration to feed validated, split datasets into training. - **Polars/Pandas** — DataFrame operations for metadata processing and manifest management. diff --git a/agents/expert-coder/README.md b/agents/expert-coder/README.md index 300011c..dff0839 100644 --- a/agents/expert-coder/README.md +++ b/agents/expert-coder/README.md @@ -35,7 +35,7 @@ Expert Coder: "I'll create a pipeline with proper abstractions: ## Related Skills -- `pydantic-strict` — Config and data structure patterns +- `pydantic` — Config and data structure patterns - `abstraction-patterns` — When and how to abstract - `code-quality` — Type hints and formatting rules - `pytorch-lightning` — ML-specific patterns diff --git a/archetypes/cv-inference-service/archetype.toml b/archetypes/cv-inference-service/archetype.toml index d3fadc3..6b25b40 100644 --- a/archetypes/cv-inference-service/archetype.toml +++ b/archetypes/cv-inference-service/archetype.toml @@ -6,5 +6,5 @@ tags = ["inference", "deployment", "fastapi", "onnx"] description = "FastAPI + ONNX deployment service for computer vision models" [skills] -required = ["onnx", "pydantic-strict", "loguru", "docker-cv", "testing"] +required = ["onnx", "pydantic", "loguru", "docker-cv", "testing"] recommended = ["tensorrt", "gcp", "github-actions"] diff --git a/archetypes/data-processing-pipeline/archetype.toml b/archetypes/data-processing-pipeline/archetype.toml index fdb552c..7d60808 100644 --- a/archetypes/data-processing-pipeline/archetype.toml +++ b/archetypes/data-processing-pipeline/archetype.toml @@ -6,5 +6,5 @@ tags = ["data", "etl", "pipeline", "processing"] description = "ETL workflows for dataset processing and preparation" [skills] -required = ["pydantic-strict", "loguru", "testing", "dvc"] +required = ["pydantic", "loguru", "testing", "dvc"] recommended = ["docker-cv", "gcp", "github-actions"] diff --git a/archetypes/library-package/archetype.toml b/archetypes/library-package/archetype.toml index fe263e6..fa78413 100644 --- a/archetypes/library-package/archetype.toml +++ b/archetypes/library-package/archetype.toml @@ -6,5 +6,5 @@ tags = ["package", "library", "pypi", "reusable"] description = "Reusable Python package for PyPI distribution" [skills] -required = ["pydantic-strict", "code-quality", "testing", "pypi", "loguru"] +required = ["pydantic", "code-quality", "testing", "pypi", "loguru"] recommended = ["github-actions", "pre-commit"] diff --git a/archetypes/model-zoo/archetype.toml b/archetypes/model-zoo/archetype.toml index 37ded93..a51cf72 100644 --- a/archetypes/model-zoo/archetype.toml +++ b/archetypes/model-zoo/archetype.toml @@ -6,5 +6,5 @@ tags = ["models", "pretrained", "collection", "benchmarks"] description = "Collection of pretrained models with benchmarking and evaluation" [skills] -required = ["pytorch-lightning", "onnx", "pydantic-strict", "loguru", "testing"] +required = ["pytorch-lightning", "onnx", "pydantic", "loguru", "testing"] recommended = ["dvc", "wandb", "docker-cv"] diff --git a/archetypes/pytorch-training-project/archetype.toml b/archetypes/pytorch-training-project/archetype.toml index ab711e9..5eac2c0 100644 --- a/archetypes/pytorch-training-project/archetype.toml +++ b/archetypes/pytorch-training-project/archetype.toml @@ -6,5 +6,5 @@ tags = ["training", "pytorch", "lightning", "deep-learning"] description = "Model training with PyTorch Lightning, Hydra config, and experiment tracking" [skills] -required = ["pytorch-lightning", "hydra-config", "pydantic-strict", "loguru", "testing"] +required = ["pytorch-lightning", "hydra-config", "pydantic", "loguru", "testing"] recommended = ["wandb", "docker-cv", "github-actions"] diff --git a/docs/agents/data-engineer.md b/docs/agents/data-engineer.md index 56cad7e..fcf6c8e 100644 --- a/docs/agents/data-engineer.md +++ b/docs/agents/data-engineer.md @@ -37,7 +37,7 @@ Data Engineer: "I recommend this approach: ## Related Skills - `dvc` — Data versioning patterns -- `pydantic-strict` — Data validation models +- `pydantic` — Data validation models - `testing` — Data pipeline testing strategies ## Full Reference diff --git a/docs/agents/expert-coder.md b/docs/agents/expert-coder.md index 786ba16..580c83e 100644 --- a/docs/agents/expert-coder.md +++ b/docs/agents/expert-coder.md @@ -23,7 +23,7 @@ The Expert Coder reads the relevant skill files and generates code following all You: "Create a DataModule for COCO object detection" Expert Coder: -1. Reads pytorch-lightning, pydantic-strict, abstraction-patterns skills +1. Reads pytorch-lightning, pydantic, abstraction-patterns skills 2. Creates DataConfig with Pydantic validation 3. Implements LightningDataModule with proper hooks 4. Wraps data loading in abstractions @@ -61,4 +61,4 @@ cap = cv2.VideoCapture("video.mp4") # Direct library use ## Related Skills -The Expert Coder reads: `pydantic-strict`, `abstraction-patterns`, `code-quality`, `pytorch-lightning`, and others as needed. +The Expert Coder reads: `pydantic`, `abstraction-patterns`, `code-quality`, `pytorch-lightning`, and others as needed. diff --git a/docs/examples/full-workflow.md b/docs/examples/full-workflow.md index 6ef8f7a..9ae931f 100644 --- a/docs/examples/full-workflow.md +++ b/docs/examples/full-workflow.md @@ -14,7 +14,7 @@ Claude generates the full project structure using the Master Skill, including W& ## Step 2: Define Configuration Models -The Expert Coder creates Pydantic configs following the `pydantic-strict` skill: +The Expert Coder creates Pydantic configs following the `pydantic` skill: ```python # src/yolo_detector/configs.py @@ -240,7 +240,7 @@ Every PR must pass both before merge. | Decision | Skill Used | |----------|-----------| -| Config validation | `pydantic-strict` | +| Config validation | `pydantic` | | Model structure | `pytorch-lightning` | | Type annotations | `code-quality` | | Video/image I/O | `abstraction-patterns` | diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md index d57a2af..064eef1 100644 --- a/docs/getting-started/quick-start.md +++ b/docs/getting-started/quick-start.md @@ -108,7 +108,7 @@ pytorch-lightning + hydra-config + wandb + code-quality + testing ### Inference Service ``` -fastapi + onnx + docker-cv + pydantic-strict + code-quality + testing +fastapi + onnx + docker-cv + pydantic + code-quality + testing ``` ### Research Project @@ -118,7 +118,7 @@ pytorch-lightning + matplotlib + hydra-config + tensorboard ### Library Package ``` -pydantic-strict + code-quality + testing + pypi + pre-commit + github-actions +pydantic + code-quality + testing + pypi + pre-commit + github-actions ``` ## What Happens Under the Hood diff --git a/docs/guides/creating-projects.md b/docs/guides/creating-projects.md index 11c7ae9..8fa90f3 100644 --- a/docs/guides/creating-projects.md +++ b/docs/guides/creating-projects.md @@ -130,7 +130,7 @@ Claude: You: "Now implement a RetinaFace detector" Claude: -1. Reads expert-coder, pytorch-lightning, pydantic-strict skills +1. Reads expert-coder, pytorch-lightning, pydantic skills 2. Creates ModelConfig with Pydantic 3. Implements RetinaFaceModule(pl.LightningModule) 4. Wraps backbone loading in abstraction diff --git a/docs/index.md b/docs/index.md index ec33219..7345b4c 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,13 +6,13 @@ ## What Is This? -AI/CV Claude Skills is a curated collection of **30 specialized skills**, **6 agent personas**, and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. +AI/CV Claude Skills is a curated collection of **32 specialized skills**, **6 agent personas**, and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. Each skill is a focused knowledge module that Claude Code loads on demand. When you tell Claude to "use the PyTorch Lightning skill," it gains deep understanding of Lightning best practices, project structure, and common patterns -- producing code that looks like it was written by a specialist. ## Key Features -- **30 Domain-Specific Skills** -- From PyTorch Lightning training loops to ONNX model export, each skill encodes expert knowledge about a specific tool or pattern. +- **32 Domain-Specific Skills** -- From PyTorch Lightning training loops to ONNX model export and model evaluation, each skill encodes expert knowledge about a specific tool or pattern. - **6 Agent Personas** -- Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Reviewer, Test Engineer) that adjust Claude's strictness, focus, and output style. - **6 Project Archetypes** -- Complete project templates for common CV/ML project types: training pipelines, inference services, research notebooks, library packages, data pipelines, and model zoos. - **Composable by Design** -- Skills combine naturally. A PyTorch training project might use the PyTorch Lightning, Hydra Config, Weights & Biases, and Docker CV skills together. @@ -45,10 +45,10 @@ Claude Code will generate: ``` whet/ - skills/ # 25 domain-specific skill modules + skills/ # 32 domain-specific skill modules master-skill/ # Core conventions for all CV/ML code pytorch-lightning/ # PyTorch Lightning training patterns - pydantic-strict/ # Strict data validation with Pydantic + pydantic/ # Strict data validation with Pydantic ... agents/ # 6 agent persona definitions expert-coder/ # High-strictness coding agent @@ -76,7 +76,7 @@ Ready to dive in? Head to the [Installation](getting-started/installation.md) gu | Section | Description | |---------|-------------| | [Getting Started](getting-started/installation.md) | Install prerequisites, clone the repo, and configure Claude Code | -| [Skills Overview](skills/index.md) | Browse all 30 skills with categories and descriptions | +| [Skills Overview](skills/index.md) | Browse all 32 skills with categories and descriptions | | [Agents](agents/index.md) | Learn about the 6 agent personas and when to use each | | [Archetypes](archetypes/index.md) | Explore the 6 project templates | | [Guides](guides/creating-projects.md) | Step-by-step guides for common workflows | diff --git a/docs/skills/abstraction-patterns.md b/docs/skills/abstraction-patterns.md index f604f94..5bd3204 100644 --- a/docs/skills/abstraction-patterns.md +++ b/docs/skills/abstraction-patterns.md @@ -120,7 +120,7 @@ class FocalLoss(DetectionLoss): - **PyTorch Lightning** -- Abstract model interfaces for interchangeable LightningModules - **Hydra Config** -- Registry-based component instantiation from config -- **Pydantic Strict** -- Validated configuration for registered components +- **Pydantic** -- Validated configuration for registered components - **Testing** -- Test abstract interfaces with concrete test doubles ## Full Reference diff --git a/docs/skills/fastapi.md b/docs/skills/fastapi.md index 16061b7..ddc02e2 100644 --- a/docs/skills/fastapi.md +++ b/docs/skills/fastapi.md @@ -97,7 +97,7 @@ async def predict( ## Combines Well With -- **Pydantic Strict** -- Frozen BaseModel patterns for all API schemas +- **Pydantic** -- Frozen BaseModel patterns for all API schemas - **ONNX / TensorRT** -- Optimized model formats loaded in lifespan - **Docker CV** -- Production containerization for FastAPI services - **Loguru** -- Structured logging in middleware and exception handlers diff --git a/docs/skills/hydra-config.md b/docs/skills/hydra-config.md index 47e3956..fe37850 100644 --- a/docs/skills/hydra-config.md +++ b/docs/skills/hydra-config.md @@ -106,7 +106,7 @@ if __name__ == "__main__": - **PyTorch Lightning** -- Configure Trainer, model, and data through Hydra - **W&B / MLflow** -- Log resolved configs as experiment parameters -- **Pydantic Strict** -- Structured configs with validation +- **Pydantic** -- Structured configs with validation - **Testing** -- Test config composition and resolution ## Full Reference diff --git a/docs/skills/index.md b/docs/skills/index.md index c177e2e..36745ab 100644 --- a/docs/skills/index.md +++ b/docs/skills/index.md @@ -22,7 +22,7 @@ These skills define the foundational patterns for all projects. | Skill | Description | Key Libraries | |-------|-------------|---------------| | [Master Skill](master-skill.md) | Universal coding conventions for all CV/ML code | Python, typing | -| [Pydantic Strict](pydantic-strict.md) | Strict data validation and configuration models | Pydantic v2 | +| [Pydantic](pydantic.md) | Strict data validation and configuration models | Pydantic v2 | | [Code Quality](code-quality.md) | Linting, formatting, and type checking setup | ruff, mypy | | [Loguru](loguru.md) | Structured logging for all projects (mandatory convention) | loguru | | [Abstraction Patterns](abstraction-patterns.md) | Design patterns for ML codebases | ABC, Protocol | @@ -49,6 +49,8 @@ Skills specific to computer vision workflows. | [Matplotlib](matplotlib.md) | Visualization and plotting for CV results | matplotlib | | [ONNX](onnx.md) | Model export and optimization | onnx, onnxruntime, onnxslim | | [TensorRT](tensorrt.md) | GPU-optimized inference engine building | TensorRT, trtexec | +| [Model Evaluation](model-evaluation.md) | Metrics, mAP/IoU, eval sets, failure analysis | supervision, torchmetrics | +| [PydanticAI](pydantic-ai.md) | Type-safe LLM/VLM structured outputs, auto-labeling | pydantic-ai | | [Hugging Face](huggingface.md) | Pretrained models, fine-tuning, PEFT/LoRA | transformers, datasets, peft | ### Cloud & Deployment @@ -91,7 +93,7 @@ Skills for code review and architectural decisions. | Phase | Recommended Skills | |-------|-------------------| -| **Starting a new project** | Master Skill, Code Quality, Pixi, Pydantic Strict | +| **Starting a new project** | Master Skill, Code Quality, Pixi, Pydantic | | **Building models** | PyTorch Lightning, Hydra Config | | **Training & experiments** | W&B or MLflow, TensorBoard | | **Preparing for production** | Docker CV, ONNX, Testing, Pre-commit | diff --git a/docs/skills/loguru.md b/docs/skills/loguru.md index a0a68a8..43da15f 100644 --- a/docs/skills/loguru.md +++ b/docs/skills/loguru.md @@ -70,7 +70,7 @@ logging.basicConfig(handlers=[InterceptHandler()], level=0, force=True) - **Master Skill** -- loguru is a standard dependency in all generated projects - **PyTorch Lightning** -- InterceptHandler routes Lightning logs through loguru -- **Pydantic Strict** -- LogConfig model for type-safe logging configuration +- **Pydantic** -- LogConfig model for type-safe logging configuration - **Hydra Config** -- logging settings in `configs/logging.yaml` ## Full Reference diff --git a/docs/skills/model-evaluation.md b/docs/skills/model-evaluation.md new file mode 100644 index 0000000..35b93af --- /dev/null +++ b/docs/skills/model-evaluation.md @@ -0,0 +1,72 @@ +# Model Evaluation + +The model-evaluation skill covers how to measure CV/ML model quality correctly — task metrics, frozen eval sets, and failure analysis — the discipline that generic unit testing does not address. + +**Skill directory:** `skills/model-evaluation/` + +## Purpose + +Unit tests check that code runs; evaluation checks whether the *model* is good enough to ship on data it has never seen, and *where* it fails. Detection evaluation is built on **`supervision`** (`import supervision as sv`), the detection-native library that speaks the same `sv.Detections` object as RF-DETR, YOLO, Ultralytics, and Transformers outputs, with metrics aligned to `pycocotools`. It is the implementation counterpart to GSD's `eval-planner`. + +## When to Use + +- Detection metrics: mAP@[.50:.95], mAP@.50, per-class AP, per-size buckets +- Precision / Recall / F1 and confusion matrices +- Selecting a deployment confidence threshold on validation +- Per-slice / failure analysis to find where the model breaks +- Classification metrics (per-class F1, calibration/ECE) via torchmetrics +- Wiring evaluation into CI as a regression gate + +## Key Patterns + +### Detection mAP with supervision + +```python +import supervision as sv +from supervision.metrics import MeanAveragePrecision, MetricTarget + +dataset = sv.DetectionDataset.from_coco( + images_directory_path="data/test/images", + annotations_path="data/test/_annotations.coco.json", +) + +predictions, targets = [], [] +for _path, image, target in dataset: + predictions.append(predict(image)) # -> sv.Detections + targets.append(target) + +result = MeanAveragePrecision(metric_target=MetricTarget.BOXES).update( + predictions, targets +).compute() +print(result.map50_95, result.map50) +``` + +### Confusion matrix (at a real deployment threshold) + +```python +cm = sv.ConfusionMatrix.from_detections( + predictions=predictions, targets=targets, classes=dataset.classes, + conf_threshold=0.30, iou_threshold=0.50, +) +cm.plot(normalize=True, save_path="artifacts/confusion_matrix.png") +``` + +### Eval-as-CI gate + +Compare against a committed baseline and fail the build when quality drops outside the bootstrap confidence interval. + +## Gotchas + +- The top-level `sv.MeanAveragePrecision` is **deprecated** (removed in 0.31.0) and inconsistent with pycocotools — import from `supervision.metrics`. +- `MetricTarget.MASKS` is silently ignored by mAP in released versions; use `pycocotools` for instance-segmentation mAP. +- Do not pre-filter predictions at a deploy threshold before mAP — it needs the full score-ranked list (filter at ~0.001). +- `-1` is an "absent" sentinel, not a score. +- `len(predictions) != len(targets)` raises — append `sv.Detections.empty()` for empty images. + +## Anti-Patterns + +- Reporting mean mAP only, hiding a collapsed class or small-object failure +- Selecting thresholds or checkpoints on the test set (leakage) +- Splitting by frame instead of by scene/video/match (near-duplicate leakage) +- Hand-rolling mAP/IoU instead of using supervision or pycocotools +- Shipping without a slice/failure analysis or a frozen regression set diff --git a/docs/skills/onnx.md b/docs/skills/onnx.md index 9895618..60b7bef 100644 --- a/docs/skills/onnx.md +++ b/docs/skills/onnx.md @@ -113,7 +113,7 @@ onnx.save(slimmed, "model.onnx") - **PyTorch Lightning** -- Export from trained LightningModule checkpoints - **Docker CV** -- ONNX Runtime inference containers -- **Pydantic Strict** -- Validated request/response schemas around inference +- **Pydantic** -- Validated request/response schemas around inference - **Testing** -- Numerical equivalence tests between PyTorch and ONNX ## Full Reference diff --git a/docs/skills/pydantic-ai.md b/docs/skills/pydantic-ai.md new file mode 100644 index 0000000..02a553d --- /dev/null +++ b/docs/skills/pydantic-ai.md @@ -0,0 +1,32 @@ +# PydanticAI + +The pydantic-ai skill covers building type-safe LLM and VLM pipelines with PydanticAI — getting validated, structured results from models instead of parsing free text by hand. + +**Skill directory:** `skills/pydantic-ai/` + +## Purpose + +PydanticAI brings the "parse, don't validate" discipline to LLM/VLM calls: declare the result you want as a Pydantic model and the framework enforces it, retrying automatically when the model returns something off-schema. This skill teaches Claude Code to use it for the flagship CV use case — VLM-in-the-loop labeling, where an image goes to Gemini or a local Moondream/DINO model and comes back as a validated set of detections or attributes ready to feed a training pipeline. + +## When to Use + +- Any time the project calls an LLM or VLM and needs a typed, validated result +- Structured extraction / auto-labeling of images +- JSON outputs, tool / function calling, or an agent loop +- Replacing hand-written parsing of a model's free-text response + +## Key Patterns + +- `Agent` with an `output_type` Pydantic model +- Image inputs via `BinaryContent` / `ImageUrl` +- Typed dependency injection (`deps_type`) +- `output_validator` + `ModelRetry` for self-correcting, bounded retries +- Provider-agnostic model config (Gemini, Anthropic, OpenAI, local) +- `TestModel` + `ALLOW_MODEL_REQUESTS = False` for hermetic tests + +## Anti-Patterns + +- Prompting for JSON and `json.loads`-ing the reply — no schema enforcement, silent drift +- Putting the API client in a global — untestable, unswappable +- Unbounded retries — cap and fail loudly +- Trusting VLM confidences/boxes unchecked — validate ranges and geometry diff --git a/docs/skills/pydantic-strict.md b/docs/skills/pydantic.md similarity index 88% rename from docs/skills/pydantic-strict.md rename to docs/skills/pydantic.md index b4002df..4ff94cc 100644 --- a/docs/skills/pydantic-strict.md +++ b/docs/skills/pydantic.md @@ -1,8 +1,8 @@ -# Pydantic Strict +# Pydantic -The Pydantic Strict skill enforces rigorous data validation patterns using Pydantic v2, ensuring type safety and validation at runtime boundaries in CV/ML projects. +The Pydantic skill enforces rigorous data validation patterns using Pydantic v2, ensuring type safety and validation at runtime boundaries in CV/ML projects. -**Skill directory:** `skills/pydantic-strict/` +**Skill directory:** `skills/pydantic/` ## Purpose @@ -101,4 +101,4 @@ class PredictionResponse(BaseModel, strict=True): ## Full Reference -See [`skills/pydantic-strict/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pydantic-strict/SKILL.md) for patterns covering custom validators, serialization, and Pydantic settings for environment variable loading. +See [`skills/pydantic/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pydantic/SKILL.md) for patterns covering custom validators, serialization, and Pydantic settings for environment variable loading. diff --git a/mkdocs.yml b/mkdocs.yml index e633471..045ed40 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,13 +61,14 @@ nav: - Overview: skills/index.md - Master Skill: skills/master-skill.md - PyTorch Lightning: skills/pytorch-lightning.md - - Pydantic Strict: skills/pydantic-strict.md + - Pydantic: skills/pydantic.md - Code Quality: skills/code-quality.md - Loguru: skills/loguru.md - Pixi: skills/pixi.md - Docker CV: skills/docker-cv.md - Hydra Config: skills/hydra-config.md - Testing: skills/testing.md + - Model Evaluation: skills/model-evaluation.md - OpenCV: skills/opencv.md - Matplotlib: skills/matplotlib.md - PyPI: skills/pypi.md @@ -87,6 +88,7 @@ nav: - FastAPI: skills/fastapi.md - AWS SageMaker: skills/aws-sagemaker.md - Hugging Face: skills/huggingface.md + - PydanticAI: skills/pydantic-ai.md - Gradio: skills/gradio.md - Kubernetes: skills/kubernetes.md - Agents: diff --git a/skills/README.md b/skills/README.md index fe0e139..2e6df97 100644 --- a/skills/README.md +++ b/skills/README.md @@ -14,7 +14,7 @@ Each skill includes: | Skill | Purpose | |-------|---------| | `master-skill` | Meta-skill for project initialization | -| `pydantic-strict` | Pydantic V2 for configs and data structures | +| `pydantic` | Pydantic V2 for configs and data structures | | `code-quality` | Ruff + mypy enforcement | | `testing` | Pytest patterns and coverage | | `pre-commit` | Git hooks setup | diff --git a/skills/abstraction-patterns/README.md b/skills/abstraction-patterns/README.md index af69123..627370e 100644 --- a/skills/abstraction-patterns/README.md +++ b/skills/abstraction-patterns/README.md @@ -23,6 +23,6 @@ The core principle is simple: business logic depends on interfaces you own, neve ## Related Skills - **[Library Review](../library-review/)** -- evaluates libraries before adoption, determining whether and how they should be wrapped. -- **[Pydantic Strict](../pydantic-strict/)** -- configuration objects are passed to abstract interfaces, decoupling parameter definitions from implementations. +- **[Pydantic](../pydantic/)** -- configuration objects are passed to abstract interfaces, decoupling parameter definitions from implementations. - **[PyTorch Lightning](../pytorch-lightning/)** -- Lightning modules implement task-specific interfaces defined through abstraction patterns. - **[Code Quality](../code-quality/)** -- mypy strict mode verifies that implementations correctly satisfy Protocol and ABC contracts. diff --git a/skills/abstraction-patterns/SKILL.md b/skills/abstraction-patterns/SKILL.md index 89849b2..1e3cf7f 100644 --- a/skills/abstraction-patterns/SKILL.md +++ b/skills/abstraction-patterns/SKILL.md @@ -1,18 +1,19 @@ --- name: abstraction-patterns description: > - Teaches when and how to create well-designed abstractions in AI/CV Python projects. - Covers the Rule of Three, interface design, wrapper patterns, and anti-patterns - for reducing cognitive load without over-engineering. + Use this skill when deciding whether and how to introduce an abstraction, wrapper, + base class, or interface in an AI/CV Python codebase — refactoring repeated logic, + wrapping a third-party API, or judging whether code is over-engineered. Applies the + Rule of Three and interface-design principles to cut cognitive load without premature + generalization. Reach for it any time you'd otherwise copy-paste a third variation of + the same logic or spin up a class for a single function, even if the user doesn't say + "abstraction". Not for choosing which library to depend on (see library-review) or for + type/data validation (see pydantic). --- # Abstraction Patterns Skill -You are writing well-abstracted Python code for AI/CV projects. Follow these patterns exactly. - -## Core Philosophy - -Abstraction exists to reduce cognitive load, not to add layers. In AI/CV projects, the right abstractions make code readable, testable, and reusable. The wrong abstractions make code harder to debug and impossible to understand. Every abstraction must justify its existence by making at least three call sites simpler. +Well-abstracted Python for AI/CV projects. Abstraction exists to reduce cognitive load, not to add layers — every abstraction must justify itself by making at least three call sites simpler. ## The Rule: When to Abstract @@ -29,31 +30,7 @@ Do NOT abstract when: ## Pattern 1: VideoReader Abstraction -Video reading involves resource management (opening/closing file handles), frame iteration, and metadata access. This is a perfect candidate for abstraction. - -### The Problem Without Abstraction - -```python -# BAD: Raw OpenCV video reading scattered across codebase -import cv2 - -cap = cv2.VideoCapture("video.mp4") -if not cap.isOpened(): - raise RuntimeError("Failed to open video") - -fps = cap.get(cv2.CAP_PROP_FPS) -total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - -while True: - ret, frame = cap.read() - if not ret: - break - # process frame... - -cap.release() # Easy to forget! -``` - -### The Abstraction +Video reading involves resource management (opening/closing file handles), frame iteration, and metadata access — a perfect candidate for abstraction. Raw `cv2.VideoCapture` usage scattered across a codebase is easy to get wrong (forgetting `cap.release()`, missing `isOpened()` checks). Wrap it in a context manager: ```python """Video reader with proper resource management.""" @@ -70,16 +47,7 @@ import numpy as np @dataclass(frozen=True) class VideoMetadata: - """Metadata for a video file. - - Attributes: - path: Path to the video file. - fps: Frames per second. - total_frames: Total number of frames. - width: Frame width in pixels. - height: Frame height in pixels. - duration_seconds: Video duration in seconds. - """ + """Immutable metadata for a video file.""" path: Path fps: float @@ -89,22 +57,14 @@ class VideoMetadata: @property def duration_seconds(self) -> float: - """Video duration in seconds.""" return self.total_frames / self.fps if self.fps > 0 else 0.0 class VideoReader: - """Context-managed video reader using OpenCV. - - Provides safe resource management, frame iteration, and metadata access. - Always use as a context manager to ensure resources are released. + """Context-managed OpenCV video reader. - Args: - path: Path to the video file. - - Example: + Always use as a context manager so resources are released: with VideoReader("input.mp4") as reader: - print(f"FPS: {reader.metadata.fps}") for frame in reader: process(frame) """ @@ -116,19 +76,14 @@ class VideoReader: @property def metadata(self) -> VideoMetadata: - """Get video metadata. Must be called after entering context.""" if self._metadata is None: - msg = "VideoReader must be used as a context manager" - raise RuntimeError(msg) + raise RuntimeError("VideoReader must be used as a context manager") return self._metadata def __enter__(self) -> VideoReader: - """Open the video file.""" self._cap = cv2.VideoCapture(str(self._path)) if not self._cap.isOpened(): - msg = f"Failed to open video: {self._path}" - raise RuntimeError(msg) - + raise RuntimeError(f"Failed to open video: {self._path}") self._metadata = VideoMetadata( path=self._path, fps=self._cap.get(cv2.CAP_PROP_FPS), @@ -139,17 +94,13 @@ class VideoReader: return self def __exit__(self, *args: object) -> None: - """Release the video capture.""" if self._cap is not None: self._cap.release() self._cap = None def __iter__(self) -> Iterator[np.ndarray]: - """Iterate over all frames in the video.""" if self._cap is None: - msg = "VideoReader must be used as a context manager" - raise RuntimeError(msg) - + raise RuntimeError("VideoReader must be used as a context manager") while True: ret, frame = self._cap.read() if not ret: @@ -157,55 +108,31 @@ class VideoReader: yield frame def read_frame(self, frame_idx: int) -> np.ndarray: - """Read a specific frame by index. - - Args: - frame_idx: Zero-based frame index. - - Returns: - Frame as a numpy array in BGR format. - - Raises: - RuntimeError: If the frame cannot be read. - IndexError: If frame_idx is out of bounds. - """ + """Read a specific frame by zero-based index (BGR).""" if self._cap is None: - msg = "VideoReader must be used as a context manager" - raise RuntimeError(msg) - + raise RuntimeError("VideoReader must be used as a context manager") if frame_idx < 0 or frame_idx >= self.metadata.total_frames: - msg = f"Frame index {frame_idx} out of range [0, {self.metadata.total_frames})" - raise IndexError(msg) - + raise IndexError(f"Frame index {frame_idx} out of range [0, {self.metadata.total_frames})") self._cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) ret, frame = self._cap.read() if not ret: - msg = f"Failed to read frame {frame_idx}" - raise RuntimeError(msg) + raise RuntimeError(f"Failed to read frame {frame_idx}") return frame ``` -### Usage +Usage is now clean and leak-free: ```python -# Clean, safe, readable with VideoReader("input.mp4") as reader: - print(f"Video: {reader.metadata.fps} FPS, {reader.metadata.total_frames} frames") - + print(f"{reader.metadata.fps} FPS, {reader.metadata.total_frames} frames") for frame in reader: - detections = model.predict(frame) - visualize(frame, detections) - - # Or read specific frames - first_frame = reader.read_frame(0) - middle_frame = reader.read_frame(reader.metadata.total_frames // 2) + visualize(frame, model.predict(frame)) + middle = reader.read_frame(reader.metadata.total_frames // 2) ``` ## Pattern 2: Image Loading Abstraction -Image loading seems simple but involves format detection, color space conversion, validation, and error handling. Abstracting this prevents inconsistencies. - -### The Abstraction +Image loading seems simple but involves format detection, color space conversion, validation, and error handling. Abstracting it prevents inconsistencies. ```python """Robust image loading with validation.""" @@ -230,53 +157,33 @@ def load_image( color_space: ColorSpace = "rgb", max_size: int | None = None, ) -> np.ndarray: - """Load an image from disk with validation and optional resizing. + """Load an image with validation and optional longest-edge resize. - Args: - path: Path to the image file. - color_space: Target color space ('rgb', 'bgr', or 'gray'). - max_size: If provided, resize so the longest edge is at most this value. - - Returns: - Image as a numpy array in the requested color space. - Shape is (H, W, 3) for color or (H, W) for grayscale. - - Raises: - FileNotFoundError: If the image file does not exist. - ValueError: If the file extension is not supported. - RuntimeError: If the image cannot be decoded. + Returns (H, W, 3) for color or (H, W) for grayscale. Raises + FileNotFoundError, ValueError (unsupported extension), or RuntimeError + (decode failure). """ path = Path(path) - if not path.exists(): - msg = f"Image not found: {path}" - raise FileNotFoundError(msg) - + raise FileNotFoundError(f"Image not found: {path}") if path.suffix.lower() not in SUPPORTED_EXTENSIONS: - msg = f"Unsupported image format: {path.suffix}. Supported: {SUPPORTED_EXTENSIONS}" - raise ValueError(msg) + raise ValueError(f"Unsupported image format: {path.suffix}") - # Read image (OpenCV loads as BGR by default) - image = cv2.imread(str(path), cv2.IMREAD_COLOR) + image = cv2.imread(str(path), cv2.IMREAD_COLOR) # OpenCV loads BGR if image is None: - msg = f"Failed to decode image: {path}" - raise RuntimeError(msg) + raise RuntimeError(f"Failed to decode image: {path}") - # Convert color space if color_space == "rgb": image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) elif color_space == "gray": image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - # "bgr" requires no conversion + # "bgr" needs no conversion - # Optional resize if max_size is not None: h, w = image.shape[:2] scale = max_size / max(h, w) if scale < 1.0: - new_w = int(w * scale) - new_h = int(h * scale) - image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA) + image = cv2.resize(image, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA) return image @@ -287,25 +194,12 @@ def save_image( color_space: ColorSpace = "rgb", quality: int = 95, ) -> None: - """Save an image to disk. - - Args: - image: Image array to save. - path: Output file path. - color_space: Color space of the input image. - quality: JPEG quality (1-100). Only used for JPEG output. - - Raises: - ValueError: If the image array is invalid. - """ + """Save an image, creating parent dirs and picking encode params by suffix.""" path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) - if image.ndim not in (2, 3): - msg = f"Expected 2D or 3D array, got {image.ndim}D" - raise ValueError(msg) + raise ValueError(f"Expected 2D or 3D array, got {image.ndim}D") - # Convert to BGR for OpenCV if color_space == "rgb" and image.ndim == 3: image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) @@ -320,9 +214,7 @@ def save_image( ## Pattern 3: Metric Computation Abstraction -Metrics in CV projects often require accumulation over batches, thread safety, and reset semantics. Abstract this into a consistent interface. - -### The Abstraction +Metrics in CV projects require accumulation over batches and reset semantics. Abstract this into a consistent `update`/`compute`/`reset` interface. ```python """Metric computation with accumulation and reset.""" @@ -330,26 +222,19 @@ Metrics in CV projects often require accumulation over batches, thread safety, a from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any import numpy as np class Metric(ABC): - """Base class for accumulating metrics over batches. - - Subclasses must implement update() and compute(). - Call reset() between epochs. - """ + """Base class for metrics accumulated over batches; reset() between epochs.""" @abstractmethod def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - """Update metric state with new predictions and targets.""" ... @abstractmethod def compute(self) -> dict[str, float]: - """Compute final metric values from accumulated state.""" ... @abstractmethod @@ -359,49 +244,29 @@ class Metric(ABC): class AccuracyMetric(Metric): - """Top-1 accuracy metric with accumulation. - - Example: - metric = AccuracyMetric() - for batch in dataloader: - preds = model(batch.images) - metric.update(preds, batch.labels) - results = metric.compute() - print(f"Accuracy: {results['accuracy']:.4f}") - metric.reset() - """ + """Top-1 accuracy with accumulation.""" def __init__(self) -> None: self._correct: int = 0 self._total: int = 0 def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - """Update with batch predictions and targets.""" pred_classes = np.argmax(predictions, axis=1) self._correct += int(np.sum(pred_classes == targets)) self._total += len(targets) def compute(self) -> dict[str, float]: - """Compute accuracy from accumulated counts.""" if self._total == 0: return {"accuracy": 0.0} return {"accuracy": self._correct / self._total} def reset(self) -> None: - """Reset counters.""" self._correct = 0 self._total = 0 class IoUMetric(Metric): - """Intersection over Union metric for segmentation. - - Accumulates per-class IoU over batches and computes mean IoU. - - Args: - num_classes: Number of segmentation classes. - ignore_index: Class index to ignore in computation. - """ + """Per-class + mean IoU for segmentation; predictions/targets are (N, H, W) class indices.""" def __init__(self, num_classes: int, ignore_index: int = -1) -> None: self._num_classes = num_classes @@ -410,16 +275,8 @@ class IoUMetric(Metric): self._union = np.zeros(num_classes, dtype=np.int64) def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - """Update with batch predictions and targets. - - Args: - predictions: Predicted class indices, shape (N, H, W). - targets: Ground truth class indices, shape (N, H, W). - """ mask = targets != self._ignore_index - pred_masked = predictions[mask] - target_masked = targets[mask] - + pred_masked, target_masked = predictions[mask], targets[mask] for cls in range(self._num_classes): pred_cls = pred_masked == cls target_cls = target_masked == cls @@ -427,50 +284,31 @@ class IoUMetric(Metric): self._union[cls] += int(np.sum(pred_cls | target_cls)) def compute(self) -> dict[str, float]: - """Compute per-class and mean IoU.""" iou_per_class = np.zeros(self._num_classes) for cls in range(self._num_classes): if self._union[cls] > 0: iou_per_class[cls] = self._intersection[cls] / self._union[cls] - result: dict[str, float] = {"mean_iou": float(np.mean(iou_per_class))} for cls in range(self._num_classes): result[f"iou_class_{cls}"] = float(iou_per_class[cls]) return result def reset(self) -> None: - """Reset accumulation arrays.""" self._intersection[:] = 0 self._union[:] = 0 class MetricCollection: - """Collection of metrics computed together. - - Args: - metrics: Dictionary mapping metric names to Metric instances. - - Example: - metrics = MetricCollection({ - "accuracy": AccuracyMetric(), - "iou": IoUMetric(num_classes=21), - }) - for batch in dataloader: - metrics.update(preds, targets) - results = metrics.compute() - metrics.reset() - """ + """Runs several metrics together, prefixing each metric's keys with its name.""" def __init__(self, metrics: dict[str, Metric]) -> None: self._metrics = metrics def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - """Update all metrics.""" for metric in self._metrics.values(): metric.update(predictions, targets) def compute(self) -> dict[str, float]: - """Compute all metrics and merge results.""" results: dict[str, float] = {} for name, metric in self._metrics.items(): for key, value in metric.compute().items(): @@ -478,7 +316,6 @@ class MetricCollection: return results def reset(self) -> None: - """Reset all metrics.""" for metric in self._metrics.values(): metric.reset() ``` @@ -500,18 +337,7 @@ import torch.nn as nn class InferenceWrapper: - """Wrapper for running model inference with pre/post processing. - - Handles device management, input preprocessing, batched inference, - and output postprocessing in a single cohesive interface. - - Args: - model: The PyTorch model. - device: Device to run inference on. - input_size: Expected input size (height, width). - mean: Normalization mean per channel. - std: Normalization std per channel. - """ + """Runs inference with device management, preprocessing, batching, and postprocessing in one interface.""" def __init__( self, @@ -528,7 +354,7 @@ class InferenceWrapper: self._std = np.array(std, dtype=np.float32) def preprocess(self, image: np.ndarray) -> torch.Tensor: - """Preprocess a single image for model input.""" + """Resize, normalize, CHW, add batch dim, move to device.""" import cv2 resized = cv2.resize(image, (self._input_size[1], self._input_size[0])) @@ -538,32 +364,12 @@ class InferenceWrapper: @torch.no_grad() def predict(self, image: np.ndarray) -> np.ndarray: - """Run inference on a single image. - - Args: - image: Input image as RGB numpy array. - - Returns: - Model output as numpy array. - """ - input_tensor = self.preprocess(image) - output = self._model(input_tensor) - return output.cpu().numpy() + return self._model(self.preprocess(image)).cpu().numpy() @torch.no_grad() def predict_batch(self, images: list[np.ndarray]) -> np.ndarray: - """Run inference on a batch of images. - - Args: - images: List of RGB numpy arrays. - - Returns: - Batched model output as numpy array. - """ - tensors = [self.preprocess(img) for img in images] - batch = torch.cat(tensors, dim=0) - output = self._model(batch) - return output.cpu().numpy() + batch = torch.cat([self.preprocess(img) for img in images], dim=0) + return self._model(batch).cpu().numpy() @classmethod def from_checkpoint( @@ -572,7 +378,7 @@ class InferenceWrapper: model_class: type[nn.Module], **kwargs: object, ) -> InferenceWrapper: - """Load model from checkpoint and create wrapper.""" + """Load model from a checkpoint and wrap it.""" checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True) model = model_class(**checkpoint.get("hparams", {})) model.load_state_dict(checkpoint["state_dict"]) @@ -593,47 +399,23 @@ from my_project.io import VideoReader, load_image from my_project.metrics import AccuracyMetric, IoUMetric, MetricCollection -def test_accuracy_metric() -> None: - """Test accuracy metric accumulation.""" +def test_accuracy_metric_accumulates_across_batches() -> None: metric = AccuracyMetric() - - # Batch 1: 3/4 correct - preds = np.array([[0.9, 0.1], [0.2, 0.8], [0.7, 0.3], [0.4, 0.6]]) - targets = np.array([0, 1, 0, 0]) - metric.update(preds, targets) - - # Batch 2: 2/2 correct - preds = np.array([[0.8, 0.2], [0.3, 0.7]]) - targets = np.array([0, 1]) - metric.update(preds, targets) - - result = metric.compute() - assert result["accuracy"] == pytest.approx(5 / 6) + metric.update(np.array([[0.9, 0.1], [0.2, 0.8], [0.7, 0.3], [0.4, 0.6]]), np.array([0, 1, 0, 0])) # 3/4 + metric.update(np.array([[0.8, 0.2], [0.3, 0.7]]), np.array([0, 1])) # 2/2 + assert metric.compute()["accuracy"] == pytest.approx(5 / 6) def test_accuracy_metric_reset() -> None: - """Test that reset clears accumulated state.""" metric = AccuracyMetric() - preds = np.array([[0.9, 0.1]]) - targets = np.array([0]) - metric.update(preds, targets) + metric.update(np.array([[0.9, 0.1]]), np.array([0])) metric.reset() - result = metric.compute() - assert result["accuracy"] == 0.0 + assert metric.compute()["accuracy"] == 0.0 def test_load_image_not_found() -> None: - """Test that missing image raises FileNotFoundError.""" with pytest.raises(FileNotFoundError, match="Image not found"): load_image("/nonexistent/image.jpg") - - -def test_load_image_unsupported_format(tmp_path) -> None: - """Test that unsupported format raises ValueError.""" - fake_file = tmp_path / "image.bpg" - fake_file.touch() - with pytest.raises(ValueError, match="Unsupported image format"): - load_image(fake_file) ``` ## When NOT to Abstract diff --git a/skills/abstraction-patterns/skill.toml b/skills/abstraction-patterns/skill.toml index f9e97a9..3cf1891 100644 --- a/skills/abstraction-patterns/skill.toml +++ b/skills/abstraction-patterns/skill.toml @@ -5,7 +5,7 @@ category = "core" tags = ["design-patterns", "abc", "protocol", "architecture"] [dependencies] -requires = ["pydantic-strict"] +requires = ["pydantic"] recommends = ["master-skill"] [compatibility] diff --git a/skills/aws-sagemaker/SKILL.md b/skills/aws-sagemaker/SKILL.md index 4067854..d7f3420 100644 --- a/skills/aws-sagemaker/SKILL.md +++ b/skills/aws-sagemaker/SKILL.md @@ -1,65 +1,46 @@ --- name: aws-sagemaker description: > - AWS SageMaker patterns for ML training and deployment. Covers training jobs, - real-time endpoints, batch transform, processing jobs, model registry, - hyperparameter tuning, SageMaker Pipelines, and S3 data management. + Use this skill when training or deploying ML models on AWS SageMaker — launching + training jobs, standing up real-time endpoints or batch transform, running processing + jobs, registering models, tuning hyperparameters, building SageMaker Pipelines, or + wiring S3 data in and out. Reach for it any time the target is AWS-managed ML + infrastructure, even if the user just says "train this in the cloud" and means AWS. + Not for Google Cloud / Vertex AI training (see gcp) or self-managed Kubernetes + (see kubernetes). --- # AWS SageMaker Skill -You are building ML training and deployment pipelines on AWS SageMaker. Follow these patterns exactly. - -## Core Philosophy - -SageMaker provides managed infrastructure for training, tuning, and deploying ML models at scale. Every cloud training and deployment workflow targeting AWS uses SageMaker. Use the SageMaker Python SDK for all interactions — never call low-level boto3 APIs for SageMaker operations unless the SDK does not support a feature. +Managed training, tuning, and deployment on AWS SageMaker. Use the SageMaker Python SDK for all operations — fall back to boto3 only when the SDK lacks a feature. ## Project Structure -### Standard SageMaker Project Layout - ``` project/ ├── src/ -│ ├── training/ -│ │ ├── train.py # Training entry point -│ │ ├── model.py # Model definition -│ │ └── data.py # Data loading -│ ├── inference/ -│ │ ├── inference.py # model_fn, input_fn, predict_fn, output_fn -│ │ └── requirements.txt # Inference dependencies -│ └── processing/ -│ └── preprocess.py # Processing job script -├── pipelines/ -│ ├── training_pipeline.py # SageMaker Pipeline definition -│ └── config.py # Pipeline configuration -├── configs/ -│ ├── training.yaml # Hyperparameters -│ └── infrastructure.yaml # Instance types, counts -└── tests/ - ├── test_training_local.py # Local mode tests - └── test_inference.py # Endpoint tests +│ ├── training/{train.py, model.py, data.py} # training entry point + model/data +│ ├── inference/{inference.py, requirements.txt} # model_fn/input_fn/predict_fn/output_fn +│ └── processing/preprocess.py # processing job script +├── pipelines/{training_pipeline.py, config.py} # SageMaker Pipeline definitions +├── configs/{training.yaml, infrastructure.yaml} # hyperparameters, instance types +└── tests/{test_training_local.py, test_inference.py} ``` ## Training Jobs -### Configuring a Training Job with PyTorch Estimator +Configure a PyTorch estimator with frozen Pydantic configs. Distribution is enabled automatically for multi-instance jobs. ```python """SageMaker training job configuration.""" from __future__ import annotations -from pathlib import Path - -import sagemaker -from pydantic import BaseModel, Field +from pydantic import BaseModel from sagemaker.pytorch import PyTorch class TrainingConfig(BaseModel, frozen=True): - """Training job configuration.""" - role: str instance_type: str = "ml.g5.2xlarge" instance_count: int = 1 @@ -72,8 +53,6 @@ class TrainingConfig(BaseModel, frozen=True): class HyperParameters(BaseModel, frozen=True): - """Training hyperparameters passed to the training script.""" - epochs: int = 50 batch_size: int = 32 learning_rate: float = 1e-3 @@ -82,11 +61,7 @@ class HyperParameters(BaseModel, frozen=True): num_classes: int = 10 -def create_estimator( - config: TrainingConfig, - hyperparameters: HyperParameters, -) -> PyTorch: - """Create a SageMaker PyTorch estimator.""" +def create_estimator(config: TrainingConfig, hp: HyperParameters) -> PyTorch: return PyTorch( entry_point="train.py", source_dir="src/training", @@ -99,41 +74,29 @@ def create_estimator( base_job_name=config.base_job_name, max_run=config.max_run_seconds, volume_size=config.volume_size_gb, - hyperparameters=hyperparameters.model_dump(), - environment={ - "NCCL_DEBUG": "INFO", - "TORCH_DISTRIBUTED_DEBUG": "DETAIL", - }, - distribution={ - "torch_distributed": {"enabled": True} - } if config.instance_count > 1 else None, + hyperparameters=hp.model_dump(), + environment={"NCCL_DEBUG": "INFO", "TORCH_DISTRIBUTED_DEBUG": "DETAIL"}, + distribution={"torch_distributed": {"enabled": True}} + if config.instance_count > 1 + else None, ) def launch_training( - config: TrainingConfig, - hyperparameters: HyperParameters, - train_s3_uri: str, - val_s3_uri: str, + config: TrainingConfig, hp: HyperParameters, train_uri: str, val_uri: str ) -> str: - """Launch a SageMaker training job and return the job name.""" - estimator = create_estimator(config, hyperparameters) - - estimator.fit( - inputs={ - "train": train_s3_uri, - "validation": val_s3_uri, - }, - wait=False, - ) - + """Launch async and return the job name (wait=False for long jobs).""" + estimator = create_estimator(config, hp) + estimator.fit(inputs={"train": train_uri, "validation": val_uri}, wait=False) return estimator.latest_training_job.name ``` ### Training Script Entry Point +SageMaker injects channels and paths via `SM_*` environment variables. Read them as argparse defaults, never hardcode paths. + ```python -"""SageMaker training script entry point (src/training/train.py).""" +"""src/training/train.py""" from __future__ import annotations @@ -148,78 +111,53 @@ from loguru import logger def parse_args() -> argparse.Namespace: - """Parse SageMaker-injected arguments.""" - parser = argparse.ArgumentParser() - - # Hyperparameters - parser.add_argument("--epochs", type=int, default=50) - parser.add_argument("--batch-size", type=int, default=32) - parser.add_argument("--learning-rate", type=float, default=1e-3) - parser.add_argument("--model-name", type=str, default="resnet50") - parser.add_argument("--num-classes", type=int, default=10) - - # SageMaker environment variables - parser.add_argument("--model-dir", type=str, default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model")) - parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train")) - parser.add_argument("--validation", type=str, default=os.environ.get("SM_CHANNEL_VALIDATION", "/opt/ml/input/data/validation")) - parser.add_argument("--output-data-dir", type=str, default=os.environ.get("SM_OUTPUT_DATA_DIR", "/opt/ml/output/data")) - - return parser.parse_args() + p = argparse.ArgumentParser() + p.add_argument("--epochs", type=int, default=50) + p.add_argument("--batch-size", type=int, default=32) + p.add_argument("--learning-rate", type=float, default=1e-3) + p.add_argument("--model-name", type=str, default="resnet50") + # SageMaker-injected paths + p.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model")) + p.add_argument("--train", default=os.environ.get("SM_CHANNEL_TRAIN")) + p.add_argument("--validation", default=os.environ.get("SM_CHANNEL_VALIDATION")) + p.add_argument("--output-data-dir", default=os.environ.get("SM_OUTPUT_DATA_DIR")) + return p.parse_args() def train(args: argparse.Namespace) -> None: - """Main training function.""" - logger.info("Starting training with args: {}", vars(args)) - - # Distributed setup + logger.info("Starting training: {}", vars(args)) world_size = int(os.environ.get("SM_NUM_GPUS", 1)) local_rank = int(os.environ.get("LOCAL_RANK", 0)) - if world_size > 1: dist.init_process_group(backend="nccl") torch.cuda.set_device(local_rank) - logger.info("Distributed training: rank {} of {}", local_rank, world_size) device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") - model = build_model(args.model_name, args.num_classes).to(device) if world_size > 1: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank]) # ... training loop ... - # Save model artifacts - model_path = Path(args.model_dir) / "model.pth" - torch.save(model.state_dict(), model_path) - logger.info("Model saved to {}", model_path) - - # Save metrics + torch.save(model.state_dict(), Path(args.model_dir) / "model.pth") metrics = {"final_val_loss": 0.25, "final_val_acc": 0.92} - metrics_path = Path(args.output_data_dir) / "metrics.json" - metrics_path.write_text(json.dumps(metrics)) + (Path(args.output_data_dir) / "metrics.json").write_text(json.dumps(metrics)) if __name__ == "__main__": - args = parse_args() - train(args) + train(parse_args()) ``` ## Real-Time Endpoints -### Deploying a Model Endpoint +Deploy a `PyTorchModel` with a custom inference script: ```python -"""SageMaker endpoint deployment.""" - -from __future__ import annotations - -from pydantic import BaseModel, Field +from pydantic import BaseModel from sagemaker.pytorch import PyTorchModel class EndpointConfig(BaseModel, frozen=True): - """Endpoint deployment configuration.""" - role: str instance_type: str = "ml.g5.xlarge" instance_count: int = 1 @@ -230,7 +168,6 @@ class EndpointConfig(BaseModel, frozen=True): def deploy_endpoint(config: EndpointConfig) -> str: - """Deploy a real-time inference endpoint.""" model = PyTorchModel( model_data=config.model_data_s3, role=config.role, @@ -239,38 +176,32 @@ def deploy_endpoint(config: EndpointConfig) -> str: entry_point="inference.py", source_dir="src/inference", ) - predictor = model.deploy( initial_instance_count=config.instance_count, instance_type=config.instance_type, endpoint_name=config.endpoint_name, ) - return predictor.endpoint_name ``` -### Custom Inference Script +### Custom Inference Handlers -```python -"""Custom inference handlers (src/inference/inference.py). +SageMaker calls these in order: `model_fn` (once at load), then `input_fn → predict_fn → output_fn` per request. -SageMaker calls these functions in order: - input_fn → predict_fn → output_fn -""" +```python +"""src/inference/inference.py""" from __future__ import annotations import io import json -import numpy as np import torch from PIL import Image from torchvision import transforms def model_fn(model_dir: str) -> torch.nn.Module: - """Load the trained model from the model directory.""" model = build_model("resnet50", num_classes=10) model.load_state_dict(torch.load(f"{model_dir}/model.pth", map_location="cpu")) model.eval() @@ -278,40 +209,32 @@ def model_fn(model_dir: str) -> torch.nn.Module: def input_fn(request_body: bytes, content_type: str) -> torch.Tensor: - """Deserialize input data to a tensor.""" if content_type == "application/x-image": image = Image.open(io.BytesIO(request_body)).convert("RGB") - transform = transforms.Compose([ + tfm = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ]) - return transform(image).unsqueeze(0) - elif content_type == "application/json": - data = json.loads(request_body) - return torch.tensor(data["instances"]) - else: - raise ValueError(f"Unsupported content type: {content_type}") + return tfm(image).unsqueeze(0) + if content_type == "application/json": + return torch.tensor(json.loads(request_body)["instances"]) + raise ValueError(f"Unsupported content type: {content_type}") def predict_fn(input_data: torch.Tensor, model: torch.nn.Module) -> dict: - """Run inference on the input tensor.""" device = next(model.parameters()).device - input_data = input_data.to(device) - with torch.no_grad(): - outputs = model(input_data) - probabilities = torch.softmax(outputs, dim=1) - predictions = torch.argmax(probabilities, dim=1) - + outputs = model(input_data.to(device)) + probs = torch.softmax(outputs, dim=1) + preds = torch.argmax(probs, dim=1) return { - "predictions": predictions.cpu().numpy().tolist(), - "probabilities": probabilities.cpu().numpy().tolist(), + "predictions": preds.cpu().numpy().tolist(), + "probabilities": probs.cpu().numpy().tolist(), } def output_fn(prediction: dict, accept: str) -> str: - """Serialize prediction output.""" if accept == "application/json": return json.dumps(prediction) raise ValueError(f"Unsupported accept type: {accept}") @@ -319,13 +242,9 @@ def output_fn(prediction: dict, accept: str) -> str: ## Hyperparameter Tuning -### Automatic Model Tuning +Bayesian search over parameter ranges. Metrics are parsed from training logs via regex `metric_definitions`. ```python -"""SageMaker hyperparameter tuning job.""" - -from __future__ import annotations - from sagemaker.tuner import ( CategoricalParameter, ContinuousParameter, @@ -335,18 +254,15 @@ from sagemaker.tuner import ( def create_tuner(estimator: PyTorch) -> HyperparameterTuner: - """Create a hyperparameter tuning job.""" - hyperparameter_ranges = { - "learning-rate": ContinuousParameter(1e-5, 1e-2, scaling_type="Logarithmic"), - "batch-size": CategoricalParameter([16, 32, 64, 128]), - "weight-decay": ContinuousParameter(1e-6, 1e-2, scaling_type="Logarithmic"), - "epochs": IntegerParameter(10, 100), - } - return HyperparameterTuner( estimator=estimator, objective_metric_name="validation:accuracy", - hyperparameter_ranges=hyperparameter_ranges, + objective_type="Maximize", + hyperparameter_ranges={ + "learning-rate": ContinuousParameter(1e-5, 1e-2, scaling_type="Logarithmic"), + "batch-size": CategoricalParameter([16, 32, 64, 128]), + "epochs": IntegerParameter(10, 100), + }, metric_definitions=[ {"Name": "validation:accuracy", "Regex": r"val_acc=(\S+)"}, {"Name": "validation:loss", "Regex": r"val_loss=(\S+)"}, @@ -354,19 +270,14 @@ def create_tuner(estimator: PyTorch) -> HyperparameterTuner: max_jobs=20, max_parallel_jobs=4, strategy="Bayesian", - objective_type="Maximize", ) ``` ## SageMaker Pipelines -### End-to-End Training Pipeline +End-to-end preprocess → train → conditional-register. Steps pass data via `.properties` references; registration only runs if accuracy clears a threshold. ```python -"""SageMaker Pipeline for training and registration.""" - -from __future__ import annotations - import sagemaker from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor from sagemaker.pytorch import PyTorch @@ -379,18 +290,11 @@ from sagemaker.workflow.pipeline import Pipeline from sagemaker.workflow.steps import ProcessingStep, TrainingStep -def create_pipeline( - role: str, - pipeline_name: str = "cv-training-pipeline", -) -> Pipeline: - """Create a SageMaker Pipeline.""" +def create_pipeline(role: str, pipeline_name: str = "cv-training-pipeline") -> Pipeline: session = sagemaker.Session() - - # Pipeline parameters input_data = ParameterString(name="InputData") accuracy_threshold = ParameterFloat(name="AccuracyThreshold", default_value=0.90) - # Step 1: Data preprocessing processor = ScriptProcessor( role=role, image_uri=session.sagemaker_client.describe_image("pytorch-training")["ImageUri"], @@ -398,8 +302,7 @@ def create_pipeline( instance_count=1, command=["python3"], ) - - preprocess_step = ProcessingStep( + preprocess = ProcessingStep( name="PreprocessData", processor=processor, inputs=[ProcessingInput(source=input_data, destination="/opt/ml/processing/input")], @@ -410,7 +313,6 @@ def create_pipeline( code="src/processing/preprocess.py", ) - # Step 2: Training estimator = PyTorch( entry_point="train.py", source_dir="src/training", @@ -420,60 +322,43 @@ def create_pipeline( framework_version="2.1.0", py_version="py310", ) - - training_step = TrainingStep( + outs = preprocess.properties.ProcessingOutputConfig.Outputs + train_step = TrainingStep( name="TrainModel", estimator=estimator, - inputs={ - "train": preprocess_step.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri, - "validation": preprocess_step.properties.ProcessingOutputConfig.Outputs["val"].S3Output.S3Uri, - }, - ) - - # Step 3: Conditional model registration - accuracy_condition = ConditionGreaterThanOrEqualTo( - left=JsonGet( - step_name=training_step.name, - property_file="metrics", - json_path="val_accuracy", - ), - right=accuracy_threshold, + inputs={"train": outs["train"].S3Output.S3Uri, "validation": outs["val"].S3Output.S3Uri}, ) - - register_step = ModelStep( + register = ModelStep( name="RegisterModel", step_args=estimator.register( - content_types=["application/json", "application/x-image"], + content_types=["application/json"], response_types=["application/json"], model_package_group_name="cv-models", approval_status="PendingManualApproval", ), ) - - condition_step = ConditionStep( + condition = ConditionStep( name="CheckAccuracy", - conditions=[accuracy_condition], - if_steps=[register_step], + conditions=[ConditionGreaterThanOrEqualTo( + left=JsonGet(step_name=train_step.name, property_file="metrics", json_path="val_accuracy"), + right=accuracy_threshold, + )], + if_steps=[register], else_steps=[], ) - return Pipeline( name=pipeline_name, parameters=[input_data, accuracy_threshold], - steps=[preprocess_step, training_step, condition_step], + steps=[preprocess, train_step, condition], sagemaker_session=session, ) ``` ## S3 Data Management -### Data Upload and Download Patterns +Use the SageMaker session for uploads (auto-resolves the default bucket); use boto3 to pull artifacts back. ```python -"""S3 data management for SageMaker workflows.""" - -from __future__ import annotations - from pathlib import Path import boto3 @@ -481,103 +366,58 @@ import sagemaker from loguru import logger -def upload_dataset( - local_path: Path, - bucket: str, - prefix: str = "datasets", -) -> str: - """Upload a local dataset to S3 and return the S3 URI.""" - session = sagemaker.Session() - s3_uri = session.upload_data( - path=str(local_path), - bucket=bucket, - key_prefix=prefix, +def upload_dataset(local_path: Path, bucket: str, prefix: str = "datasets") -> str: + s3_uri = sagemaker.Session().upload_data( + path=str(local_path), bucket=bucket, key_prefix=prefix ) logger.info("Uploaded {} to {}", local_path, s3_uri) return s3_uri -def download_model_artifacts( - model_data_s3: str, - local_dir: Path, -) -> Path: - """Download model artifacts from S3.""" +def download_model_artifacts(model_data_s3: str, local_dir: Path) -> Path: local_dir.mkdir(parents=True, exist_ok=True) - - session = boto3.Session() - s3 = session.resource("s3") - - # Parse S3 URI - parts = model_data_s3.replace("s3://", "").split("/", 1) - bucket_name, key = parts[0], parts[1] - + bucket, key = model_data_s3.replace("s3://", "").split("/", 1) local_path = local_dir / Path(key).name - s3.Bucket(bucket_name).download_file(key, str(local_path)) - + boto3.resource("s3").Bucket(bucket).download_file(key, str(local_path)) logger.info("Downloaded {} to {}", model_data_s3, local_path) return local_path ``` ## Batch Transform -### Offline Batch Inference +Offline inference over an S3 dataset — reuses the same `PyTorchModel`/inference script as endpoints: ```python -"""SageMaker batch transform for offline inference.""" - -from __future__ import annotations - from sagemaker.pytorch import PyTorchModel def run_batch_transform( - model_data_s3: str, - input_s3_uri: str, - output_s3_uri: str, - role: str, + model_data_s3: str, input_s3_uri: str, output_s3_uri: str, role: str, instance_type: str = "ml.g5.xlarge", ) -> None: - """Run batch transform on a dataset.""" model = PyTorchModel( - model_data=model_data_s3, - role=role, - framework_version="2.1.0", - py_version="py310", - entry_point="inference.py", - source_dir="src/inference", + model_data=model_data_s3, role=role, + framework_version="2.1.0", py_version="py310", + entry_point="inference.py", source_dir="src/inference", ) - transformer = model.transformer( - instance_count=1, - instance_type=instance_type, - output_path=output_s3_uri, - strategy="MultiRecord", - max_payload=6, - ) - - transformer.transform( - data=input_s3_uri, - content_type="application/json", - split_type="Line", + instance_count=1, instance_type=instance_type, + output_path=output_s3_uri, strategy="MultiRecord", max_payload=6, ) + transformer.transform(data=input_s3_uri, content_type="application/json", split_type="Line") ``` ## Local Mode Testing -### Test Training Locally Before Submitting to SageMaker +Test the training script with `instance_type="local"` and `file://` inputs before submitting cloud jobs: ```python -"""Local mode testing for SageMaker training scripts.""" - -from __future__ import annotations - import pytest from sagemaker.pytorch import PyTorch @pytest.fixture def local_estimator() -> PyTorch: - """Create a local mode estimator for testing.""" return PyTorch( entry_point="train.py", source_dir="src/training", @@ -586,20 +426,13 @@ def local_estimator() -> PyTorch: instance_count=1, framework_version="2.1.0", py_version="py310", - hyperparameters={ - "epochs": 1, - "batch-size": 4, - "model-name": "resnet18", - }, + hyperparameters={"epochs": 1, "batch-size": 4, "model-name": "resnet18"}, ) def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: - """Verify training script runs in local mode.""" - # Create minimal test data create_test_dataset(tmp_path / "train") create_test_dataset(tmp_path / "val") - local_estimator.fit({ "train": f"file://{tmp_path / 'train'}", "validation": f"file://{tmp_path / 'val'}", @@ -608,17 +441,15 @@ def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: ## Anti-Patterns -- **Never hardcode S3 paths** — use SageMaker session defaults and `ParameterString` in pipelines. -- **Never use `instance_type="ml.p3.16xlarge"` for simple models** — right-size instances. Start with `ml.g5.xlarge` and scale up. -- **Never skip local mode testing** — always test with `instance_type="local"` before submitting cloud jobs. -- **Never put credentials in training scripts** — SageMaker injects the IAM role automatically. -- **Never download the full dataset inside the training script** — use SageMaker input channels (`SM_CHANNEL_*`). -- **Never forget to call `wait=False` for long training jobs** — use async job submission and poll status separately. +- **Never hardcode S3 paths** — use session defaults and `ParameterString` in pipelines. +- **Never oversize instances** — start at `ml.g5.xlarge` and scale up; don't reach for `ml.p3.16xlarge` for simple models. +- **Never skip local mode** — validate with `instance_type="local"` first. +- **Never put credentials in training scripts** — SageMaker injects the IAM role. +- **Never download the full dataset inside the script** — use `SM_CHANNEL_*` input channels. +- **Never block on long jobs** — submit with `wait=False` and poll status. ## Integration with Other Skills -- **PyTorch Lightning** — Training scripts use LightningModule inside SageMaker training jobs. -- **Hydra Config** — Hyperparameters serialized and passed to SageMaker as flat key-value pairs. -- **W&B / MLflow** — Experiment tracking inside SageMaker training containers. -- **Docker CV** — Custom training containers when the built-in SageMaker images are insufficient. -- **DVC** — Data versioning with S3 remote storage that SageMaker can access. +- **PyTorch Lightning** — LightningModule inside training jobs; **Hydra Config** — hyperparameters flattened to key-value pairs. +- **W&B / MLflow** — experiment tracking in containers; **Docker CV** — custom containers when built-in images fall short. +- **DVC** — data versioning with an S3 remote SageMaker can access. diff --git a/skills/aws-sagemaker/skill.toml b/skills/aws-sagemaker/skill.toml index df3c52e..d782d12 100644 --- a/skills/aws-sagemaker/skill.toml +++ b/skills/aws-sagemaker/skill.toml @@ -5,7 +5,7 @@ category = "cloud" tags = ["aws", "sagemaker", "training", "deployment", "cloud", "mlops"] [dependencies] -requires = ["pydantic-strict", "loguru"] +requires = ["pydantic", "loguru"] recommends = ["pytorch-lightning", "docker-cv", "wandb", "dvc"] [compatibility] diff --git a/skills/code-quality/README.md b/skills/code-quality/README.md index 5b3608c..eafa102 100644 --- a/skills/code-quality/README.md +++ b/skills/code-quality/README.md @@ -22,7 +22,7 @@ Ruff handles three concerns in a single tool: Black-compatible formatting, isort ## Related Skills -- **[Pydantic Strict](../pydantic-strict/)** -- mypy's Pydantic plugin validates model definitions statically, complementing runtime checks. +- **[Pydantic](../pydantic/)** -- mypy's Pydantic plugin validates model definitions statically, complementing runtime checks. - **[Pixi](../pixi/)** -- defines task commands (`pixi run lint`, `pixi run typecheck`) that invoke Ruff and mypy with the correct flags. - **[GitHub Actions](../github-actions/)** -- runs code quality checks as a required CI step before merge. - **[VS Code](../vscode/)** -- configures editor extensions for Ruff and mypy to surface issues inline during development. diff --git a/skills/code-quality/SKILL.md b/skills/code-quality/SKILL.md index 8803316..f69db66 100644 --- a/skills/code-quality/SKILL.md +++ b/skills/code-quality/SKILL.md @@ -1,9 +1,12 @@ --- name: code-quality description: > - Enforces code quality standards for AI/CV Python projects using Ruff linting, - MyPy strict type checking, and automated formatting. Covers editor integration, - pre-commit hooks, and CI pipeline enforcement. + Use this skill when setting up or fixing linting, type checking, and formatting for an + AI/CV Python project — configuring Ruff rules, MyPy strict mode, resolving lint or type + errors, and defining the quality bar. Reach for it any time you'd otherwise hand-tune + pyproject linting config or silence a type error, even if the user just says "clean up + the code" or "make it pass checks". This skill owns the standards themselves; the git + commit-hook wiring lives in pre-commit and editor integration in vscode. --- # Code Quality Skill @@ -94,20 +97,9 @@ docstring-code-format = true ### Running Ruff ```bash -# Check for lint violations -pixi run ruff check . - -# Fix auto-fixable violations -pixi run ruff check . --fix - -# Format code -pixi run ruff format . - -# Check formatting without modifying -pixi run ruff format . --check - -# Show what would change -pixi run ruff format . --diff +ruff check . # lint +ruff check . --fix # lint + auto-fix +ruff format . # format (add --check or --diff for CI) ``` ## Mypy Configuration @@ -188,27 +180,12 @@ def process_image(image, target_size, normalize=True): #### Use modern type syntax (Python 3.11+) ```python -# CORRECT: Modern syntax -def get_labels() -> list[str]: - ... - -def get_config() -> dict[str, int]: - ... - -def maybe_transform(image: np.ndarray) -> np.ndarray | None: - ... - -# WRONG: Legacy typing module -from typing import Dict, List, Optional, Union +# CORRECT: built-in generics and PEP 604 unions +def get_labels() -> list[str]: ... +def get_config() -> dict[str, int]: ... +def maybe_transform(image: np.ndarray) -> np.ndarray | None: ... -def get_labels() -> List[str]: - ... - -def get_config() -> Dict[str, int]: - ... - -def maybe_transform(image: np.ndarray) -> Optional[np.ndarray]: - ... +# WRONG: legacy typing module — List[str], Dict[str, int], Optional[np.ndarray] ``` #### Use `from __future__ import annotations` in every file @@ -290,14 +267,8 @@ DetectionList: TypeAlias = list[tuple[BoundingBox, float, int]] ### Running Mypy ```bash -# Type check source code -pixi run mypy src/ --strict - -# Type check with error details -pixi run mypy src/ --strict --show-error-context - -# Generate HTML report -pixi run mypy src/ --strict --html-report mypy-report/ +mypy src/ --strict # type check +mypy src/ --strict --html-report report/ # optional HTML report ``` ## Pre-commit Integration @@ -341,14 +312,14 @@ repos: ```bash # Install pre-commit hooks -pixi run pre-commit install +pre-commit install # Run on all files (useful for first-time setup) -pixi run pre-commit run --all-files +pre-commit run --all-files # Run a specific hook -pixi run pre-commit run ruff --all-files -pixi run pre-commit run mypy --all-files +pre-commit run ruff --all-files +pre-commit run mypy --all-files ``` ## CI Enforcement @@ -368,41 +339,16 @@ on: branches: [main] jobs: - lint: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - - - name: Run ruff check - run: pixi run ruff check . --output-format=github - - - name: Run ruff format check - run: pixi run ruff format . --check - - typecheck: + quality: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + - uses: prefix-dev/setup-pixi@v0.8.1 - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - - - name: Run mypy - run: pixi run mypy src/ --strict - - test: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - - - name: Run tests - run: pixi run pytest tests/ -v --cov=src --cov-report=xml --cov-fail-under=80 + - run: pixi run ruff check . --output-format=github + - run: pixi run ruff format . --check + - run: pixi run mypy src/ --strict + - run: pixi run pytest tests/ -v --cov=src --cov-report=xml --cov-fail-under=80 - name: Upload coverage uses: codecov/codecov-action@v4 @@ -414,28 +360,14 @@ jobs: ### Logging Instead of Print -```python -from __future__ import annotations - -import logging +The `T20` rule bans `print()` in source. Use a logger with lazy `%`-style args +(the project convention is loguru — see the loguru skill): -logger = logging.getLogger(__name__) - - -def train_epoch(epoch: int, num_batches: int) -> float: - """Train for one epoch.""" - logger.info("Starting epoch %d with %d batches", epoch, num_batches) - - total_loss = 0.0 - for batch_idx in range(num_batches): - loss = _process_batch(batch_idx) - total_loss += loss - if batch_idx % 100 == 0: - logger.debug("Batch %d/%d, loss=%.4f", batch_idx, num_batches, loss) +```python +from loguru import logger - avg_loss = total_loss / num_batches - logger.info("Epoch %d complete, avg_loss=%.4f", epoch, avg_loss) - return avg_loss +logger.info("Starting epoch {} with {} batches", epoch, num_batches) +logger.debug("Batch {}/{}, loss={:.4f}", batch_idx, num_batches, loss) ``` ### Path Handling with pathlib @@ -484,10 +416,10 @@ raise ValueError("Invalid image shape") Before every commit, verify: -- [ ] `pixi run ruff check .` passes with zero violations -- [ ] `pixi run ruff format . --check` reports no changes needed -- [ ] `pixi run mypy src/ --strict` passes with zero errors -- [ ] `pixi run pytest tests/ -v --cov-fail-under=80` passes +- [ ] `ruff check .` passes with zero violations +- [ ] `ruff format . --check` reports no changes needed +- [ ] `mypy src/ --strict` passes with zero errors +- [ ] `pytest tests/ -v --cov-fail-under=80` passes - [ ] No `print()` statements in source code (use `logging`) - [ ] No `os.path` usage (use `pathlib.Path`) - [ ] No `typing.Dict`, `typing.List`, `typing.Optional` (use built-in generics) diff --git a/skills/docker-cv/SKILL.md b/skills/docker-cv/SKILL.md index 696b2ee..38ca561 100644 --- a/skills/docker-cv/SKILL.md +++ b/skills/docker-cv/SKILL.md @@ -1,9 +1,13 @@ --- name: docker-cv description: > - Build optimized Docker images for computer vision and deep learning workloads. - Covers CUDA support, multi-stage builds, layer caching, security best practices, - and GPU-accelerated container deployment. + Use this skill when writing or optimizing a Dockerfile for computer vision or deep + learning — CUDA/GPU base images, multi-stage builds, layer caching, slim inference + images, non-root security, and shrinking bloated CV images. Reach for it any time + you'd otherwise hand-write a GPU Dockerfile or debug a container that won't see the + GPU, even if the user just says "containerize this model". For deploying the resulting + containers to a cluster see kubernetes; for cloud image registries see gcp and + aws-sagemaker. --- # Docker CV Skill diff --git a/skills/dvc/SKILL.md b/skills/dvc/SKILL.md index 13b647c..ea5a729 100644 --- a/skills/dvc/SKILL.md +++ b/skills/dvc/SKILL.md @@ -1,9 +1,12 @@ --- name: dvc description: > - Data Version Control (DVC) for versioning large datasets, models, and ML pipeline - artifacts. Covers DVC initialization, remote storage configuration (S3, GCS, Azure), - pipeline definitions, and integration with Git workflows. + Use this skill when versioning large datasets, model weights, or pipeline artifacts + with DVC — running dvc init/add/push/pull, configuring S3/GCS/Azure remotes, defining + reproducible dvc.yaml pipelines, and keeping big binaries out of Git. Reach for it any + time data or checkpoints are too large to commit to Git and need tracking or sharing, + even if the user doesn't say "DVC". For experiment metrics, run comparison, and model + registry see mlflow or wandb. --- # Data Version Control (DVC) for ML Projects diff --git a/skills/fastapi/README.md b/skills/fastapi/README.md index 2289053..afbf11c 100644 --- a/skills/fastapi/README.md +++ b/skills/fastapi/README.md @@ -25,7 +25,7 @@ When you need to serve ML models over HTTP — whether for batch predictions, re ## Related Skills -- **[Pydantic Strict](../pydantic-strict/)** — frozen BaseModel patterns used for all API schemas. +- **[Pydantic](../pydantic/)** — frozen BaseModel patterns used for all API schemas. - **[ONNX](../onnx/)** / **[TensorRT](../tensorrt/)** — optimized model formats loaded in the lifespan handler. - **[Docker CV](../docker-cv/)** — production containerization for FastAPI ML services. - **[Loguru](../loguru/)** — structured logging in middleware and exception handlers. diff --git a/skills/fastapi/SKILL.md b/skills/fastapi/SKILL.md index e964f8f..28c8442 100644 --- a/skills/fastapi/SKILL.md +++ b/skills/fastapi/SKILL.md @@ -1,28 +1,21 @@ --- name: fastapi description: > - FastAPI patterns for building ML model serving APIs. Covers async endpoints, - Pydantic request/response models, dependency injection, middleware, CORS, + Use this skill when building an HTTP API to serve an ML model — async prediction + endpoints, Pydantic request/response schemas, dependency injection, middleware, CORS, background tasks, WebSocket streaming, health checks, and structured error handling. + Reach for it any time you'd otherwise hand-roll a model-serving web service or expose + inference over REST, even if the user just says "put this model behind an API". For a + quick interactive demo UI instead of a production JSON API, see gradio. --- # FastAPI Skill -You are building FastAPI applications for serving ML models and CV pipelines. Follow these patterns exactly. +Build FastAPI applications for serving ML models and CV pipelines. Use Pydantic models for all request/response schemas — never accept raw dicts. Use an application factory for testable configuration and clean startup/shutdown lifecycle. -## Core Philosophy - -FastAPI provides automatic OpenAPI documentation, request validation via Pydantic, and async-first design. Every model serving endpoint in this framework uses FastAPI. Use Pydantic models for all request and response schemas — never accept raw dicts from API consumers. - -## Application Structure - -### Standard Application Factory - -Use an application factory pattern to configure the FastAPI app. This enables testing with different configurations and clean startup/shutdown lifecycle management. +## Application Factory ```python -"""FastAPI application factory for ML model serving.""" - from __future__ import annotations from contextlib import asynccontextmanager @@ -36,8 +29,6 @@ from pydantic import BaseModel, Field class AppConfig(BaseModel, frozen=True): - """Application configuration.""" - title: str = "ML Model API" version: str = "1.0.0" cors_origins: list[str] = Field(default_factory=lambda: ["*"]) @@ -48,18 +39,14 @@ class AppConfig(BaseModel, frozen=True): class ModelRegistry: """Holds loaded models for the application lifetime.""" - def __init__(self) -> None: self.models: dict[str, Any] = {} async def load(self, config: AppConfig) -> None: logger.info("Loading model from {}", config.model_path) - # Load ONNX, TensorRT, or PyTorch model here self.models["default"] = await _load_model(config.model_path) - logger.info("Model loaded successfully") async def shutdown(self) -> None: - logger.info("Releasing model resources") self.models.clear() @@ -92,7 +79,6 @@ def create_app(config: AppConfig | None = None) -> FastAPI: allow_methods=["*"], allow_headers=["*"], ) - from .routes import prediction, health app.include_router(health.router, tags=["health"]) app.include_router(prediction.router, prefix="/api/v1", tags=["prediction"]) @@ -102,13 +88,9 @@ def create_app(config: AppConfig | None = None) -> FastAPI: ## Request and Response Models -### Pydantic Schemas for ML Endpoints - Define explicit Pydantic models for every endpoint. Never use `dict` or `Any` in API signatures. ```python -"""Pydantic schemas for prediction endpoints.""" - from __future__ import annotations import base64 @@ -120,9 +102,7 @@ class PredictionRequest(BaseModel, frozen=True): """Single image prediction request.""" image_b64: str = Field(..., description="Base64-encoded image bytes") - confidence_threshold: float = Field( - default=0.5, ge=0.0, le=1.0, description="Minimum confidence for detections" - ) + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) max_detections: int = Field(default=100, ge=1, le=1000) @field_validator("image_b64") @@ -136,64 +116,36 @@ class PredictionRequest(BaseModel, frozen=True): class Detection(BaseModel, frozen=True): - """Single object detection result.""" - label: str confidence: float = Field(ge=0.0, le=1.0) bbox: list[float] = Field(min_length=4, max_length=4, description="[x1, y1, x2, y2]") class PredictionResponse(BaseModel, frozen=True): - """Prediction response with detections and metadata.""" - detections: list[Detection] inference_time_ms: float model_version: str -class BatchPredictionRequest(BaseModel, frozen=True): - """Batch prediction request.""" - - images: list[PredictionRequest] = Field(max_length=32) - - -class BatchPredictionResponse(BaseModel, frozen=True): - """Batch prediction response.""" - - results: list[PredictionResponse] - total_inference_time_ms: float - - class ErrorResponse(BaseModel, frozen=True): - """Structured error response.""" - error: str detail: str | None = None request_id: str | None = None ``` -## Endpoint Patterns +## Prediction Endpoint with Dependency Injection -### Prediction Endpoint with Dependency Injection +Inject the model and preprocessor via `Depends` — never load them inside the handler. For batch endpoints, accept a `list[PredictionRequest]` (cap with `Field(max_length=...)`) and loop this same logic. ```python -"""Prediction routes.""" - from __future__ import annotations import time from typing import Annotated -import numpy as np -from fastapi import APIRouter, Depends, HTTPException +from fastapi import APIRouter, Depends -from .schemas import ( - PredictionRequest, - PredictionResponse, - BatchPredictionRequest, - BatchPredictionResponse, - Detection, -) +from .schemas import PredictionRequest, PredictionResponse, Detection from .dependencies import get_model, get_preprocessor router = APIRouter() @@ -224,35 +176,11 @@ async def predict( inference_time_ms=round(elapsed_ms, 2), model_version=model.version, ) - - -@router.post("/predict/batch", response_model=BatchPredictionResponse) -async def predict_batch( - request: BatchPredictionRequest, - model: Annotated[Model, Depends(get_model)], - preprocessor: Annotated[Preprocessor, Depends(get_preprocessor)], -) -> BatchPredictionResponse: - """Run inference on a batch of images.""" - start = time.perf_counter() - - results: list[PredictionResponse] = [] - for item in request.images: - result = await predict(item, model, preprocessor) - results.append(result) - - total_ms = (time.perf_counter() - start) * 1000 - - return BatchPredictionResponse( - results=results, - total_inference_time_ms=round(total_ms, 2), - ) ``` ### Health Check Endpoints ```python -"""Health check routes.""" - from __future__ import annotations from fastapi import APIRouter @@ -269,7 +197,7 @@ class HealthResponse(BaseModel, frozen=True): @router.get("/health", response_model=HealthResponse) async def health_check() -> HealthResponse: - """Liveness and readiness probe.""" + """Liveness probe.""" from .app import model_registry return HealthResponse( @@ -281,7 +209,7 @@ async def health_check() -> HealthResponse: @router.get("/ready") async def readiness() -> dict[str, bool]: - """Kubernetes readiness probe.""" + """Kubernetes readiness probe — 503 until the model is loaded.""" from .app import model_registry if not model_registry.models: @@ -291,11 +219,7 @@ async def readiness() -> dict[str, bool]: ## Dependency Injection -### Model and Preprocessor Dependencies - ```python -"""FastAPI dependencies for ML serving.""" - from __future__ import annotations from functools import lru_cache @@ -333,14 +257,12 @@ async def get_preprocessor( ### Request ID and Logging Middleware ```python -"""Custom middleware for ML API.""" - from __future__ import annotations import time import uuid -from fastapi import FastAPI, Request, Response +from fastapi import Request, Response from loguru import logger from starlette.middleware.base import BaseHTTPMiddleware @@ -357,14 +279,9 @@ class RequestLoggingMiddleware(BaseHTTPMiddleware): elapsed = (time.perf_counter() - start) * 1000 logger.info( - "{method} {path} → {status} ({elapsed:.1f}ms) [{rid}]", - method=request.method, - path=request.url.path, - status=response.status_code, - elapsed=elapsed, - rid=request_id, + "{} {} → {} ({:.1f}ms) [{}]", + request.method, request.url.path, response.status_code, elapsed, request_id, ) - response.headers["X-Request-ID"] = request_id return response ``` @@ -372,8 +289,6 @@ class RequestLoggingMiddleware(BaseHTTPMiddleware): ### Structured Exception Handlers ```python -"""Exception handlers for consistent error responses.""" - from __future__ import annotations from fastapi import FastAPI, Request @@ -382,7 +297,6 @@ from loguru import logger async def model_error_handler(request: Request, exc: ModelInferenceError) -> JSONResponse: - """Handle model inference failures.""" logger.error("Inference error: {}", exc) return JSONResponse( status_code=500, @@ -394,28 +308,13 @@ async def model_error_handler(request: Request, exc: ModelInferenceError) -> JSO ) -async def validation_error_handler(request: Request, exc: ValueError) -> JSONResponse: - """Handle input validation errors with clear messages.""" - return JSONResponse( - status_code=422, - content={"error": "validation_error", "detail": str(exc)}, - ) - - def register_exception_handlers(app: FastAPI) -> None: - """Register all exception handlers on the app.""" app.add_exception_handler(ModelInferenceError, model_error_handler) ``` -## WebSocket Streaming - -### Real-Time Video Inference +## WebSocket Streaming (Real-Time Video Inference) ```python -"""WebSocket endpoint for streaming inference.""" - -from __future__ import annotations - import base64 from fastapi import APIRouter, WebSocket, WebSocketDisconnect @@ -449,31 +348,18 @@ async def stream_inference(websocket: WebSocket) -> None: logger.info("WebSocket client disconnected") ``` -## Background Tasks +## Background Tasks (Async Post-Processing) -### Async Post-Processing +Use `BackgroundTasks` to persist results or log after the response is sent, keeping request latency low. ```python -"""Background task patterns for FastAPI ML APIs.""" - -from __future__ import annotations - -from fastapi import APIRouter, BackgroundTasks -from loguru import logger - -router = APIRouter() +from fastapi import BackgroundTasks -async def save_prediction_to_db( - request_id: str, - detections: list[Detection], -) -> None: - """Save prediction results asynchronously after response.""" - logger.info("Saving {} detections for request {}", len(detections), request_id) - await db.predictions.insert_one({ - "request_id": request_id, - "detections": [d.model_dump() for d in detections], - }) +async def save_prediction_to_db(request_id: str, detections: list[Detection]) -> None: + await db.predictions.insert_one( + {"request_id": request_id, "detections": [d.model_dump() for d in detections]} + ) @router.post("/predict") @@ -481,25 +367,14 @@ async def predict_with_logging( request: PredictionRequest, background_tasks: BackgroundTasks, ) -> PredictionResponse: - """Predict and log results in background.""" result = await run_prediction(request) - - background_tasks.add_task( - save_prediction_to_db, - request_id=request.state.request_id, - detections=result.detections, - ) - + background_tasks.add_task(save_prediction_to_db, request.state.request_id, result.detections) return result ``` -## Testing FastAPI Applications - -### Async Test Client +## Testing (Async Test Client) ```python -"""Tests for prediction API.""" - from __future__ import annotations import base64 @@ -552,44 +427,27 @@ async def test_predict_rejects_invalid_base64(client: AsyncClient) -> None: ## Docker Deployment -### Production Dockerfile for FastAPI ML API +Production Dockerfile plus a Uvicorn runner for programmatic startup. ```dockerfile FROM python:3.11-slim AS base WORKDIR /app -RUN pip install --no-cache-dir uv +RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" -COPY pyproject.toml uv.lock ./ -RUN uv sync --frozen --no-dev +COPY pixi.toml pixi.lock ./ +RUN pixi install COPY src/ ./src/ EXPOSE 8000 -CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] +CMD ["pixi", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] ``` -### Uvicorn Configuration - -```python -"""Uvicorn runner with production settings.""" - -import uvicorn - -if __name__ == "__main__": - uvicorn.run( - "app.main:create_app", - factory=True, - host="0.0.0.0", - port=8000, - workers=4, - log_level="info", - access_log=True, - limit_concurrency=100, - timeout_keep_alive=30, - ) -``` +For programmatic startup with the factory, use `uvicorn.run("app.main:create_app", factory=True, host="0.0.0.0", port=8000, workers=4, limit_concurrency=100, timeout_keep_alive=30)`. ## Anti-Patterns @@ -602,8 +460,4 @@ if __name__ == "__main__": ## Integration with Other Skills -- **Pydantic Strict** — All request/response models follow frozen BaseModel patterns. -- **Docker CV** — Production Dockerfiles with multi-stage builds for FastAPI + model serving. -- **ONNX / TensorRT** — Load optimized models in the lifespan handler. -- **Loguru** — Structured logging in middleware and exception handlers. -- **Testing** — Async test client with httpx for full endpoint coverage. +Pydantic (frozen models), Docker CV (multi-stage serving images), ONNX/TensorRT (load optimized models in the lifespan handler), Loguru (middleware/handler logging), Testing (httpx async client). diff --git a/skills/fastapi/skill.toml b/skills/fastapi/skill.toml index 1f6fc6f..28b8b9d 100644 --- a/skills/fastapi/skill.toml +++ b/skills/fastapi/skill.toml @@ -5,7 +5,7 @@ category = "infra" tags = ["api", "serving", "fastapi", "rest", "async"] [dependencies] -requires = ["pydantic-strict", "loguru"] +requires = ["pydantic", "loguru"] recommends = ["docker-cv", "onnx", "testing"] [compatibility] diff --git a/skills/gcp/SKILL.md b/skills/gcp/SKILL.md index 811ec5d..0f7eaee 100644 --- a/skills/gcp/SKILL.md +++ b/skills/gcp/SKILL.md @@ -1,9 +1,12 @@ --- name: gcp description: > - Google Cloud Platform services for CV/ML projects. Covers Artifact Registry for - Docker images, Cloud Storage for datasets, Vertex AI for training jobs, and - gcloud CLI patterns for infrastructure management. + Use this skill when working with Google Cloud infrastructure for CV/ML — pushing + images to Artifact Registry, storing datasets and checkpoints in Cloud Storage (gsutil, + gcsfuse, the Python client), and provisioning with the gcloud CLI. Reach for it any + time the project touches GCP buckets, registries, or gcloud commands, even if the user + doesn't name the specific service — including submitting Vertex AI custom training jobs + and retrieving their artifacts. For the AWS equivalent see aws-sagemaker. --- # GCP Skill @@ -12,7 +15,7 @@ Google Cloud Platform services for CV/ML projects: Artifact Registry, Cloud Stor ## Artifact Registry -Use Artifact Registry for Docker images and Python packages. It replaces the deprecated Container Registry. +Use Artifact Registry (replaces the deprecated Container Registry) for Docker images and Python packages. ### Create a Docker Repository @@ -46,27 +49,14 @@ docker pull us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 ### Python Package Repository ```bash -# Create a Python repository gcloud artifacts repositories create ml-packages \ - --repository-format=python \ - --location=us-central1 + --repository-format=python --location=us-central1 -# Configure pip to pull from Artifact Registry -gcloud artifacts print-settings python \ - --repository=ml-packages \ - --location=us-central1 +# Print pip/uv index settings for the repo +gcloud artifacts print-settings python --repository=ml-packages --location=us-central1 ``` -```python -# pixi.toml — add Artifact Registry as extra index -# [project] -# name = "my-cv-project" -# -# [tool.pixi.pypi-options] -# extra-index-urls = [ -# "https://us-central1-python.pkg.dev/my-project/ml-packages/simple/" -# ] -``` +Add the printed index to `pyproject.toml` under `[tool.uv]` as an `extra-index-url` (e.g. `https://us-central1-python.pkg.dev/my-project/ml-packages/simple/`). ## Cloud Storage @@ -215,20 +205,29 @@ model = job.run( ### Prebuilt Training Containers +Skip building a custom image: pass a Google prebuilt container (e.g. `us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-3:latest`) as `container_uri` to `from_local_script`, plus `requirements=["torchvision", "albumentations", ...]` for extra deps. + +### Retrieving Artifacts After Training + +**The training VM is ephemeral — anything not written to GCS is gone when the job ends.** +Write checkpoints and exports to the GCS path Vertex provides (`AIP_MODEL_DIR`), then pull +them down explicitly once the job finishes. Do not assume the job "left them somewhere". + ```python -# Use Google's prebuilt PyTorch containers instead of custom images -PYTORCH_GPU_CONTAINER = "us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-3:latest" - -job = aiplatform.CustomJob.from_local_script( - display_name="detection-train", - script_path="src/train.py", - container_uri=PYTORCH_GPU_CONTAINER, - requirements=["torchvision", "albumentations", "pycocotools"], - args=["--epochs=100", "--batch-size=32"], - machine_type="n1-standard-8", - accelerator_type="NVIDIA_TESLA_T4", - accelerator_count=1, -) +import os + +# Inside the training container: write to the GCS path Vertex provides. +# Falls back to a local dir so the same script runs off-cloud unchanged. +model_dir = os.environ.get("AIP_MODEL_DIR", "outputs/model") +trainer.save_checkpoint(f"{model_dir}/best.ckpt") +``` + +```bash +# After the job completes: sync every artifact to the local machine. +gcloud storage rsync -r "gs://my-bucket/jobs/${JOB_ID}/model" ./artifacts/ + +# Verify before deleting anything remote. +ls -lh ./artifacts/ ``` ## Docker Image Management @@ -260,61 +259,37 @@ docker push "${FULL_URI}" ### Multi-Stage for Training and Inference ```dockerfile -# ============================================================================== -# Base stage — shared between training and inference -# ============================================================================== FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 AS base - -ENV DEBIAN_FRONTEND=noninteractive -ENV PYTHONDONTWRITEBYTECODE=1 -ENV PYTHONUNBUFFERED=1 - +ENV DEBIAN_FRONTEND=noninteractive PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 RUN apt-get update && apt-get install -y --no-install-recommends \ - libgl1-mesa-glx libglib2.0-0 curl \ - && rm -rf /var/lib/apt/lists/* - -# ============================================================================== -# Training stage — full environment with dev tools -# ============================================================================== -FROM base AS training - + libgl1-mesa-glx libglib2.0-0 curl && rm -rf /var/lib/apt/lists/* RUN curl -fsSL https://pixi.sh/install.sh | bash ENV PATH="/root/.pixi/bin:${PATH}" +# Training — full environment with dev tools +FROM base AS training WORKDIR /app COPY pixi.toml pixi.lock ./ -RUN pixi install --frozen - -COPY pyproject.toml ./ +RUN pixi install COPY src/ src/ COPY configs/ configs/ - -RUN pixi run pip install -e ".[dev]" RUN useradd -m -u 1000 trainer USER trainer - ENTRYPOINT ["pixi", "run", "python", "-m"] CMD ["my_project.train"] -# ============================================================================== -# Inference stage — minimal runtime -# ============================================================================== +# Inference — minimal runtime FROM base AS inference - WORKDIR /app -COPY requirements-inference.txt ./ -RUN pip install --no-cache-dir -r requirements-inference.txt - +COPY pixi.toml pixi.lock ./ +RUN pixi install COPY src/ src/ - RUN useradd -m -u 1000 appuser USER appuser - HEALTHCHECK --interval=30s --timeout=10s --retries=3 \ CMD curl -f http://localhost:8000/health || exit 1 - EXPOSE 8000 -CMD ["uvicorn", "src.serve:app", "--host", "0.0.0.0", "--port", "8000"] +CMD ["pixi", "run", "uvicorn", "src.serve:app", "--host", "0.0.0.0", "--port", "8000"] ``` ### GitHub Actions: Build and Push to Artifact Registry @@ -371,20 +346,12 @@ jobs: gcloud iam service-accounts create ml-trainer \ --display-name="ML Training Service Account" -# Grant required roles +# Grant required roles (least privilege) SA_EMAIL="ml-trainer@my-project.iam.gserviceaccount.com" - -gcloud projects add-iam-policy-binding my-project \ - --member="serviceAccount:${SA_EMAIL}" \ - --role="roles/aiplatform.user" - -gcloud projects add-iam-policy-binding my-project \ - --member="serviceAccount:${SA_EMAIL}" \ - --role="roles/storage.objectAdmin" - -gcloud projects add-iam-policy-binding my-project \ - --member="serviceAccount:${SA_EMAIL}" \ - --role="roles/artifactregistry.reader" +for ROLE in aiplatform.user storage.objectAdmin artifactregistry.reader; do + gcloud projects add-iam-policy-binding my-project \ + --member="serviceAccount:${SA_EMAIL}" --role="roles/${ROLE}" +done ``` ### Workload Identity Federation for CI @@ -411,32 +378,23 @@ gcloud iam service-accounts add-iam-policy-binding "${SA_EMAIL}" \ ### Docker Authentication ```bash -# ✅ Configure Docker to authenticate with Artifact Registry -gcloud auth configure-docker us-central1-docker.pkg.dev - -# ✅ For CI: use credential helper with service account key -# (prefer Workload Identity Federation when possible) -cat key.json | docker login -u _json_key --password-stdin \ - https://us-central1-docker.pkg.dev - -# ❌ Do not use gcloud auth print-access-token for long-running processes -# Tokens expire after 1 hour +# Local: gcloud auth configure-docker us-central1-docker.pkg.dev +# CI (prefer WIF over keys): cat key.json | docker login -u _json_key \ +# --password-stdin https://us-central1-docker.pkg.dev ``` ## Pydantic Configuration -Define typed configuration models for GCP project settings and job specifications. +Typed models for GCP project settings and job specs. ```python from pydantic import BaseModel, Field class GCPConfig(BaseModel, frozen=True): - """GCP project configuration.""" - project_id: str = Field(description="GCP project ID") - region: str = Field(default="us-central1", description="Default region") - zone: str = Field(default="us-central1-a", description="Default zone") + region: str = Field(default="us-central1") + zone: str = Field(default="us-central1-a") class ArtifactRegistryConfig(BaseModel, frozen=True): @@ -481,7 +439,7 @@ class VertexJobConfig(BaseModel, frozen=True): class GCPProjectConfig(BaseModel, frozen=True): - """Complete GCP configuration for an ML project.""" + """Complete GCP configuration — composed from a Hydra-compatible configs/gcp.yaml.""" gcp: GCPConfig artifact_registry: ArtifactRegistryConfig @@ -489,106 +447,31 @@ class GCPProjectConfig(BaseModel, frozen=True): vertex_job: VertexJobConfig ``` -```yaml -# configs/gcp.yaml — Hydra-compatible configuration -gcp: - project_id: my-cv-project - region: us-central1 - zone: us-central1-a - -artifact_registry: - repository: ml-images - location: us-central1 - -storage: - bucket_name: my-cv-project-ml - datasets_prefix: datasets/ - checkpoints_prefix: checkpoints/ - outputs_prefix: outputs/ - -vertex_job: - display_name: resnet50-train - machine_type: n1-standard-8 - accelerator_type: NVIDIA_TESLA_T4 - accelerator_count: 1 - staging_bucket: my-cv-project-ml-staging - boot_disk_size_gb: 100 -``` +## Project Dependencies -## Integration with pixi - -Define pixi tasks for common GCP operations to ensure consistency across the team. - -```toml -# pixi.toml — GCP task definitions -[project] -name = "my-cv-project" -channels = ["conda-forge"] -platforms = ["linux-64", "osx-arm64"] - -[dependencies] -python = ">=3.11" -google-cloud-storage = ">=2.14" -google-cloud-aiplatform = ">=1.40" - -[feature.dev.dependencies] -google-cloud-artifact-registry = ">=1.11" - -[tasks] -# Authentication -gcp-auth = "gcloud auth application-default login" -gcp-docker-auth = "gcloud auth configure-docker us-central1-docker.pkg.dev" - -# Cloud Storage -gcs-upload-data = "gsutil -m cp -r ./data/ gs://my-ml-bucket/datasets/" -gcs-download-checkpoint = "gsutil cp gs://my-ml-bucket/checkpoints/latest.pt ./checkpoints/" -gcs-sync-outputs = "gsutil -m rsync -r ./outputs/ gs://my-ml-bucket/runs/" - -# Docker — build and push -docker-build-train = """docker build \ - --tag us-central1-docker.pkg.dev/my-project/ml-images/training:latest \ - --target training .""" -docker-build-inference = """docker build \ - --tag us-central1-docker.pkg.dev/my-project/ml-images/inference:latest \ - --target inference .""" -docker-push-train = "docker push us-central1-docker.pkg.dev/my-project/ml-images/training:latest" -docker-push-inference = "docker push us-central1-docker.pkg.dev/my-project/ml-images/inference:latest" - -# Vertex AI -vertex-submit = """python -c " -from src.gcp import submit_training_job -submit_training_job( - project='my-project', - location='us-central1', - display_name='training-run', - container_uri='us-central1-docker.pkg.dev/my-project/ml-images/training:latest', - args=['--config=configs/train.yaml'], -)" -""" -vertex-list-jobs = "gcloud ai custom-jobs list --region=us-central1 --limit=10" -vertex-logs = "gcloud ai custom-jobs stream-logs --region=us-central1" -``` +Add the GCP client libraries with `pixi add google-cloud-storage google-cloud-aiplatform` (and `google-cloud-artifact-registry` as a dev dependency). Wrap the recurring `gcloud`/`gsutil` commands above in a `justfile` for team consistency — e.g. `just gcs-sync-outputs`, `just docker-push-train`, `just vertex-list-jobs` (`gcloud ai custom-jobs list --region=us-central1`). ## Best Practices -1. **Use Artifact Registry, not Container Registry** -- Container Registry is deprecated; Artifact Registry supports Docker, Python, and npm packages in one service. -2. **Pin image tags for Vertex AI jobs** -- never use `:latest` in production training jobs; use semantic version tags or Git SHAs. -3. **Use Workload Identity Federation** -- avoid long-lived service account keys; use OIDC tokens from GitHub Actions or GKE workloads. -4. **Store large datasets in Cloud Storage, not in Docker images** -- mount buckets with gcsfuse or download at job start. -5. **Set `staging_bucket` for Vertex AI** -- Vertex AI needs a GCS bucket for staging scripts and intermediate artifacts. -6. **Use regional resources** -- keep Artifact Registry, Cloud Storage, and Vertex AI jobs in the same region to minimize egress costs and latency. -7. **Configure lifecycle rules on GCS buckets** -- auto-delete old checkpoints and temporary outputs to control storage costs. -8. **Use prebuilt Vertex AI containers when possible** -- Google's PyTorch/TF containers have optimized CUDA and NCCL setups. -9. **Tag images with both version and `latest`** -- version tags for reproducibility, `latest` for development convenience. -10. **Grant least-privilege IAM roles** -- `roles/aiplatform.user` for submitting jobs, `roles/storage.objectViewer` for read-only data access. - -## Anti-Patterns to Avoid - -- ❌ Using Container Registry (`gcr.io/`) for new projects -- use Artifact Registry (`pkg.dev/`) instead. -- ❌ Baking credentials or service account keys into Docker images -- pass via environment variables or Workload Identity. -- ❌ Running Vertex AI jobs with the default Compute Engine service account -- create dedicated service accounts with minimal permissions. -- ❌ Storing training datasets inside Docker images -- images become massive and slow to pull; mount from GCS instead. -- ❌ Using `gcloud auth print-access-token` in scripts -- tokens expire after 1 hour; use `gcloud auth application-default login` or service account impersonation. -- ❌ Submitting Vertex AI jobs without a staging bucket -- the job will fail or use an auto-created bucket you cannot control. -- ❌ Using multi-region GCS buckets for training data accessed from a single region -- pay extra egress with no benefit; use regional buckets. -- ❌ Hardcoding project IDs and regions -- use Pydantic config models or environment variables for portability. +1. **Artifact Registry, not Container Registry** — the latter is deprecated. +2. **Pin image tags for Vertex AI jobs** — semantic versions or Git SHAs, never `:latest` in production. +3. **Use Workload Identity Federation** — OIDC tokens over long-lived service account keys. +4. **Keep datasets in Cloud Storage, not images** — mount via gcsfuse or download at job start. +5. **Set `staging_bucket`** — Vertex AI needs one for scripts and intermediate artifacts. +6. **Write outputs to `AIP_MODEL_DIR` and pull them down when the job finishes** — the training VM is ephemeral; un-synced artifacts are lost. +7. **Keep resources regional and co-located** to minimize egress cost and latency. +8. **Configure GCS lifecycle rules** to auto-delete stale checkpoints/outputs. +9. **Prefer prebuilt Vertex AI containers** — optimized CUDA/NCCL. +10. **Tag with both version and `latest`** — reproducibility plus dev convenience. +11. **Grant least-privilege IAM** — `roles/aiplatform.user` for jobs, `roles/storage.objectViewer` for read-only data. + +## Anti-Patterns + +- ❌ Using Container Registry (`gcr.io/`) for new projects — use `pkg.dev/`. +- ❌ Baking credentials/SA keys into images — use env vars or Workload Identity. +- ❌ Running Vertex jobs as the default Compute Engine SA — use a dedicated minimal-permission SA. +- ❌ Storing datasets inside images — huge, slow to pull; mount from GCS. +- ❌ `gcloud auth print-access-token` in scripts — tokens expire hourly; use ADC or SA impersonation. +- ❌ Submitting Vertex jobs without a staging bucket. +- ❌ Multi-region buckets for single-region training data — extra egress, no benefit. +- ❌ Hardcoding project IDs/regions — use Pydantic config or env vars. diff --git a/skills/github-actions/SKILL.md b/skills/github-actions/SKILL.md index 5b0771c..f4d8b0c 100644 --- a/skills/github-actions/SKILL.md +++ b/skills/github-actions/SKILL.md @@ -1,61 +1,63 @@ --- name: github-actions description: > - CI/CD workflow patterns for ML/CV projects using GitHub Actions. Covers tiered - pipelines (lint, test, build, deploy), GPU runner support, dependency caching, - matrix builds, and AI agent integration. + Use this skill when creating or fixing CI/CD workflows for an ML/CV repo on GitHub + Actions — tiered lint/test/build/deploy pipelines, GPU runners, dependency caching, + matrix builds, Docker build-and-push, releases, and AI-agent integration. Reach for it + any time you'd otherwise hand-write a .github/workflows YAML or debug a failing + pipeline, even if the user just says "set up CI" or "why is the build red". For hooks + that run locally before commit see pre-commit; for repo settings and branch protection + see github-repo-setup. --- # GitHub Actions Skill -CI/CD workflow patterns for ML/CV projects with tiered pipelines, GPU runner support, caching, and AI agent integration. +CI/CD workflow patterns for ML/CV projects: tiered pipelines, GPU runners, caching, and AI agent integration. -## Workflow Structure +## Standard pixi Setup -Organize workflows into tiers based on speed and trigger frequency. +Every job shares the same setup steps. Later examples abbreviate this as `# ...pixi setup...`. -### Tier 1: Fast — Lint and Format (Every Push) +```yaml +- uses: actions/checkout@v4 +- uses: prefix-dev/setup-pixi@v0.8.1 + with: + cache: true # caches on pixi.lock hash +- run: pixi install +``` + +## Workflow Tiers + +Organize workflows by speed and trigger frequency. + +### Tier 1: Lint and Format (every push) ```yaml # .github/workflows/lint.yml name: Lint & Format - on: [push, pull_request] - jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true - - - name: Check formatting - run: pixi run ruff format --check . - - - name: Lint - run: pixi run ruff check . - - - name: Type check - run: pixi run mypy src/ + # ...pixi setup... + - run: pixi run ruff format --check . + - run: pixi run ruff check . + - run: pixi run mypy src/ ``` -### Tier 2: Medium — Tests (Pull Requests) +### Tier 2: Tests (pull requests) + +Adds a Python matrix and coverage upload. ```yaml # .github/workflows/test.yml name: Test Suite - on: pull_request: branches: [main] push: branches: [main] - jobs: test: runs-on: ubuntu-latest @@ -63,23 +65,8 @@ jobs: matrix: python-version: ["3.11", "3.12"] steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true - - - name: Run tests - run: | - pixi run pytest \ - --cov=src \ - --cov-report=xml \ - --cov-report=term \ - --cov-fail-under=80 \ - -v - + # ...pixi setup... + - run: pixi run pytest --cov=src --cov-report=xml --cov-fail-under=80 -v - name: Upload coverage if: always() uses: codecov/codecov-action@v4 @@ -88,42 +75,31 @@ jobs: fail_ci_if_error: false ``` -### Tier 3: Heavy — Training Validation (Merge to Main) +### Tier 3: Training Validation (merge to main, GPU runner) + +Runs on a self-hosted GPU runner with a timeout and artifact upload. ```yaml # .github/workflows/train-validation.yml name: Training Validation - on: push: branches: [main] workflow_dispatch: inputs: epochs: - description: "Number of epochs" - required: false default: "2" - jobs: smoke-test: - runs-on: self-hosted # GPU runner + runs-on: [self-hosted, gpu, linux] timeout-minutes: 60 steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true - + # ...pixi setup... - name: Run smoke training run: | pixi run python -m my_project.train \ trainer.max_epochs=${{ github.event.inputs.epochs || '2' }} \ - trainer.fast_dev_run=false \ data.batch_size=4 - - name: Upload training artifacts if: always() uses: actions/upload-artifact@v4 @@ -137,7 +113,7 @@ jobs: ## Matrix Testing -### Python Version + OS Matrix +OS + Python matrix (use `include`/`exclude` to prune combinations): ```yaml jobs: @@ -153,14 +129,13 @@ jobs: runs-on: ${{ matrix.os }} ``` -### CPU/GPU Matrix +CPU/GPU matrix — pair a runner label with a device flag via `include`: ```yaml jobs: test: strategy: matrix: - runner: [ubuntu-latest, self-hosted-gpu] include: - runner: ubuntu-latest device: cpu @@ -168,35 +143,12 @@ jobs: device: cuda runs-on: ${{ matrix.runner }} steps: - - name: Run tests - run: pixi run pytest -v --device=${{ matrix.device }} + - run: pixi run pytest -v --device=${{ matrix.device }} ``` -## Caching Strategies +## Caching -### Pixi Environment Cache - -```yaml -- name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true # Caches based on pixi.lock hash -``` - -### pip Cache (Fallback) - -```yaml -- name: Cache pip - uses: actions/cache@v4 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }} - restore-keys: | - ${{ runner.os }}-pip- -``` - -### Model Weights Cache +`setup-pixi` with `cache: true` handles the environment. Add caches for pip fallback and model weights keyed on the relevant file hash: ```yaml - name: Cache pretrained models @@ -208,77 +160,40 @@ jobs: ${{ runner.os }}-torch-hub- ``` -## GPU Runner Configuration +The same pattern works for `~/.cache/pip` keyed on `requirements*.txt`. + +## GPU Runners -### Self-Hosted Runner Setup +Label self-hosted runners by capability (`self-hosted`, `gpu`, `linux`, or a GPU type like `a100`), then target them and verify the device: ```yaml -# Label self-hosted runners with GPU capabilities jobs: train: runs-on: [self-hosted, gpu, linux] steps: - name: Verify GPU run: nvidia-smi - - name: Set CUDA device run: echo "CUDA_VISIBLE_DEVICES=0" >> $GITHUB_ENV ``` -### Runner Labels - -| Label | Description | -|-------|-------------| -| `self-hosted` | Any self-hosted runner | -| `gpu` | Runner with NVIDIA GPU | -| `linux` | Linux OS | -| `a100` | Specific GPU type | +## Artifacts Across Jobs -## Artifact Management - -### Upload Model Checkpoints - -```yaml -- name: Upload checkpoint - uses: actions/upload-artifact@v4 - with: - name: model-checkpoint-${{ github.sha }} - path: checkpoints/best.ckpt - retention-days: 30 -``` - -### Upload Evaluation Reports - -```yaml -- name: Generate evaluation report - run: pixi run python -m my_project.evaluate --output=report.json - -- name: Upload report - uses: actions/upload-artifact@v4 - with: - name: evaluation-report - path: report.json -``` - -### Download Artifacts Across Jobs +Upload in one job (set `retention-days`), download in a dependent job via `needs`: ```yaml jobs: train: - outputs: - artifact-name: model-${{ github.sha }} steps: - - name: Upload - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v4 with: name: model-${{ github.sha }} path: checkpoints/ - + retention-days: 30 evaluate: needs: train steps: - - name: Download model - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v4 with: name: model-${{ github.sha }} path: checkpoints/ @@ -286,146 +201,76 @@ jobs: ## Reusable Workflows -### Reusable Lint Workflow +Define with `workflow_call`, call with `uses:` and pass `inputs`: ```yaml # .github/workflows/reusable-lint.yml -name: Reusable Lint - on: workflow_call: inputs: python-version: type: string default: "3.11" - jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true - - run: pixi run lint - - run: pixi run typecheck + # ...pixi setup... + - run: pixi run ruff check . + - run: pixi run mypy src/ ``` -### Calling Reusable Workflows - ```yaml -# .github/workflows/ci.yml -name: CI -on: [push, pull_request] - +# .github/workflows/ci.yml — caller jobs: lint: uses: ./.github/workflows/reusable-lint.yml with: python-version: "3.11" - test: needs: lint uses: ./.github/workflows/reusable-test.yml ``` -## Secret Management +## Secrets and Environments -### Setting Secrets +Pass repository secrets via `env`; gate production jobs behind a protected `environment` that requires approval: ```yaml -# Use GitHub repository secrets for API keys -steps: - - name: Train with W&B logging - env: - WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }} - AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} - AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} - run: pixi run python -m my_project.train -``` - -### Environment Protection - -```yaml -# Use environments for production deployments jobs: deploy: runs-on: ubuntu-latest - environment: production # Requires approval + environment: production # requires manual approval steps: - - name: Deploy model + - name: Train + deploy env: - DEPLOY_KEY: ${{ secrets.DEPLOY_KEY }} + WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }} + AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} run: pixi run deploy ``` ## Agent Integration -### Code Review Agent +Run quality-gate agents on PRs. Code review and test-engineer agents share the same shape — the review agent runs format/lint/mypy plus security (`ruff check --select S`); the test agent runs coverage and flags skipped tests: ```yaml # .github/workflows/code-review.yml name: Code Review - on: pull_request: types: [opened, synchronize] - jobs: review: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - - - name: Run ruff format check - run: pixi run ruff format --check . - - - name: Run ruff lint - run: pixi run ruff check . - - - name: Run mypy - run: pixi run mypy src/ - + # ...pixi setup... + - run: pixi run ruff format --check . + - run: pixi run ruff check . + - run: pixi run mypy src/ - name: Security checks run: pixi run ruff check --select S . -``` - -### Test Engineer Agent - -```yaml -# .github/workflows/test-engineer.yml -name: Test Engineer - -on: - pull_request: - types: [opened, synchronize] - -jobs: - test: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - - - name: Run tests with coverage - run: | - pixi run pytest \ - --cov=src \ - --cov-report=term \ - --cov-report=xml \ - --cov-fail-under=80 - - - name: Check for skipped tests + - name: Warn on skipped tests run: | if pixi run pytest --collect-only -q 2>&1 | grep -q "skipped"; then echo "::warning::Skipped tests found. Fix or remove them." @@ -434,33 +279,28 @@ jobs: ## Docker Build and Push +Build a multi-stage image (`target: inference`) and push to GHCR with GHA layer caching: + ```yaml # .github/workflows/docker.yml name: Build Docker Image - on: push: branches: [main] tags: ["v*"] - jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 - + - uses: docker/setup-buildx-action@v3 - name: Login to GHCR uses: docker/login-action@v3 with: registry: ghcr.io username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - - - name: Build and push - uses: docker/build-push-action@v5 + - uses: docker/build-push-action@v5 with: context: . push: true @@ -474,68 +314,43 @@ jobs: ## Semantic Release -Automate version bumps, changelog generation, and publishing with Python Semantic Release. The workflow runs after checks pass, analyzes commit messages since the last release, and determines the next version automatically. - -### Release Workflow +Automate version bumps, changelogs, and publishing. The release job runs after checks pass, analyzes commits since the last tag, and publishes only when a new version is cut. ```yaml # .github/workflows/release.yml name: Release - on: push: branches: [main, develop] - jobs: check: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - cache: true - - - name: Lint - run: pixi run lint - - - name: Type check - run: pixi run typecheck - - - name: Test - run: pixi run test - + # ...pixi setup... + - run: pixi run ruff check . + - run: pixi run mypy src/ + - run: pixi run pytest release: - name: Semantic Release runs-on: ubuntu-latest concurrency: release environment: pypi needs: [check] - if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/develop') + if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/develop' permissions: - id-token: write # Required for OIDC / Trusted Publishing - contents: write # Required for creating releases/tags - + id-token: write # OIDC / trusted publishing + contents: write # create releases/tags steps: - - name: Checkout code - uses: actions/checkout@v4 + - uses: actions/checkout@v4 with: - fetch-depth: 0 # Full history for commit analysis - + fetch-depth: 0 # full history for commit analysis - name: Python Semantic Release id: release uses: python-semantic-release/python-semantic-release@v9.15.1 with: github_token: ${{ secrets.GITHUB_TOKEN }} - - name: Publish to PyPI if: steps.release.outputs.released == 'true' uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_TOKEN }} - - name: Publish to GitHub Releases if: steps.release.outputs.released == 'true' uses: python-semantic-release/upload-to-gh-release@v9.15.1 @@ -544,23 +359,15 @@ jobs: tag: ${{ steps.release.outputs.tag }} ``` -### Key Details - -- **`fetch-depth: 0`** -- semantic release needs the full git history to analyze commits since the last tag -- **`concurrency: release`** -- prevents parallel release jobs from conflicting -- **`environment: pypi`** -- use a GitHub environment with deployment protection rules -- **`needs: [check]`** -- releases only run after lint, typecheck, and tests pass -- **Conditional publish** -- `steps.release.outputs.released == 'true'` only publishes when semantic release actually creates a new version -- **Trusted publishing** -- use `pypa/gh-action-pypi-publish` with OIDC (`id-token: write`) instead of long-lived API tokens when possible - -### Branch Strategy +Key details: -| Branch | Release Type | Version Example | -|--------|-------------|-----------------| -| `main` | Production release | `1.2.0` | -| `develop` | Prerelease | `1.3.0-dev.1` | +- `fetch-depth: 0` — semantic release needs full git history to analyze commits since the last tag. +- `concurrency: release` — prevents parallel release jobs from conflicting. +- `environment: pypi` — attach deployment protection rules. +- Conditional publish (`released == 'true'`) — only publishes when a new version is actually created. +- Prefer trusted publishing (`id-token: write` + OIDC) over long-lived API tokens. -Configure in `pyproject.toml` (see PyPI skill for full config): +Branch strategy — `main` cuts production releases (`1.2.0`); `develop` cuts prereleases (`1.3.0-dev.1`). Configure in `pyproject.toml` (see PyPI skill): ```toml [tool.semantic_release.branches.main] @@ -574,59 +381,35 @@ prerelease_token = "dev" ## CI Verification (Required Before Task Completion) -A PR is **not done** until CI is green. Always verify CI after pushing: +A PR is **not done** until CI is green. Always verify after pushing: ```bash -# Check CI status on a PR -gh pr checks - -# If a check fails, read the logs -gh run view --log-failed - -# Fix the issue, commit, push, and re-check +gh pr checks # check status +gh run view --log-failed # read failing logs ``` -### Common CI Failures and Fixes +Workflow: run local checks (`ruff format && ruff check && pytest`), push, run `gh pr checks `, fix any failure locally, push, repeat until all checks are green. + +### Common CI Failures | Failure | Cause | Fix | |---------|-------|-----| -| `ruff format --check` fails | Pre-commit ruff version differs from pixi ruff | Update `.pre-commit-config.yaml` rev to match `pixi run ruff -- --version` | -| `ruff check` fails | New lint violations | Run `pixi run lint` locally and fix | -| `mypy` fails | Type errors | Run `pixi run typecheck` locally and fix | -| `pytest` fails | Test failures | Run `pixi run test` locally and fix | -| Merge conflicts | Branch diverged from base | Merge/rebase base branch, resolve conflicts, re-run checks | - -### Workflow - -1. Run local checks: `pixi run format && pixi run lint && pixi run test` -2. Push and create PR -3. Run `gh pr checks ` -- wait for all checks to report -4. If any check fails: read logs, fix locally, push, repeat from step 3 -5. Task is complete only when all checks are green - -### Tool Version Alignment - -Keep formatter/linter versions synchronized across all environments: - -| Tool | Where | Must Match | -|------|-------|------------| -| ruff | `.pre-commit-config.yaml` rev | `pixi run ruff -- --version` | -| ruff | CI (`pixi run format-check`) | pixi.lock | -| mypy | CI (`pixi run typecheck`) | pixi.lock | +| `ruff format --check` fails | Pre-commit ruff version differs from project ruff | Sync `.pre-commit-config.yaml` rev to `pixi run ruff --version` | +| `ruff check` fails | New lint violations | Run `pixi run ruff check .` locally and fix | +| `mypy` fails | Type errors | Run `pixi run mypy src/` locally and fix | +| `pytest` fails | Test failures | Run `pixi run pytest` locally and fix | +| Merge conflicts | Branch diverged from base | Merge/rebase base, resolve, re-run | -If pre-commit hooks reformat files that CI then rejects, the versions are out of sync. Update `.pre-commit-config.yaml` to match the pixi-managed version. +Keep formatter/linter versions synchronized: the `.pre-commit-config.yaml` ruff rev must match `pixi run ruff --version`. If pre-commit reformats files that CI then rejects, the versions are out of sync. ## Best Practices -1. **Use pixi in CI** -- keep CI commands identical to local development -2. **Cache aggressively** -- pixi environments, pip cache, model weights -3. **Fail fast on lint** -- run lint before tests to get quick feedback -4. **Pin action versions** -- use `@v4` not `@main` for stability -5. **Set timeouts** -- prevent runaway training jobs from consuming runner time -6. **Use artifacts wisely** -- set retention days, don't upload huge datasets -7. **Protect secrets** -- use GitHub Secrets, never hardcode credentials -8. **Use environments** -- require approval for production deployments -9. **Matrix strategically** -- test Python versions and OS combinations that matter -10. **Document runner requirements** -- label self-hosted runners clearly -11. **Verify CI after every push** -- never consider a task done until all checks pass -12. **Fix CI immediately** -- a broken CI blocks the entire team; treat failures as highest priority +1. **Use pixi in CI** — keep CI commands identical to local development. +2. **Cache aggressively** — pixi environments, model weights. +3. **Fail fast on lint** — run lint before tests for quick feedback. +4. **Pin action versions** — use `@v4`, not `@main`. +5. **Set timeouts** — prevent runaway training jobs. +6. **Use artifacts wisely** — set retention days; don't upload huge datasets. +7. **Protect secrets** — GitHub Secrets only, never hardcode. +8. **Gate production** — require approval via environments. +9. **Verify CI after every push** — never call a task done until checks are green; fix broken CI immediately. diff --git a/skills/github-repo-setup/README.md b/skills/github-repo-setup/README.md index 0c9cf30..ca765a7 100644 --- a/skills/github-repo-setup/README.md +++ b/skills/github-repo-setup/README.md @@ -22,6 +22,7 @@ Standardizes GitHub repository initialization and configuration for CV/ML projec - Squash merge as default strategy - Full setup automation script - Pydantic configuration model for repo settings +- Follow gitflow workflow ## Related Skills diff --git a/skills/github-repo-setup/SKILL.md b/skills/github-repo-setup/SKILL.md index e698775..b14cd8b 100644 --- a/skills/github-repo-setup/SKILL.md +++ b/skills/github-repo-setup/SKILL.md @@ -1,9 +1,12 @@ --- name: github-repo-setup description: > - Best practices for initializing and configuring GitHub repositories for CV/ML projects. - Covers repository creation, branch protection rules, PR and issue templates, - CODEOWNERS, merge strategies, and gh CLI automation. + Use this skill when initializing or configuring a GitHub repository for a CV/ML project + — creating the repo, branch protection rules, PR and issue templates, CODEOWNERS, merge + strategy, and gh CLI automation. Reach for it any time you'd otherwise click through + GitHub settings by hand or ask "how should I set up this repo", even if the user just + says "create the repo" or "protect main". For the CI/CD workflows that run on those + branches see github-actions. --- # GitHub Repository Setup Skill diff --git a/skills/gradio/README.md b/skills/gradio/README.md index 7a9e239..489cc63 100644 --- a/skills/gradio/README.md +++ b/skills/gradio/README.md @@ -34,5 +34,5 @@ When you need to quickly build an interactive demo for a trained model -- whethe - **[PyTorch Lightning](../pytorch-lightning/)** -- load Lightning checkpoints and serve trained models through Gradio. - **[Testing](../testing/)** -- test Gradio components with the Gradio test client and pytest fixtures. - **[ONNX](../onnx/)** -- serve optimized ONNX models for low-latency Gradio inference. -- **[Pydantic Strict](../pydantic-strict/)** -- frozen BaseModel patterns for all Gradio configuration objects. +- **[Pydantic](../pydantic/)** -- frozen BaseModel patterns for all Gradio configuration objects. - **[Loguru](../loguru/)** -- structured logging in model loading, prediction callbacks, and flagging. diff --git a/skills/gradio/SKILL.md b/skills/gradio/SKILL.md index 9c2328f..e24a5a4 100644 --- a/skills/gradio/SKILL.md +++ b/skills/gradio/SKILL.md @@ -1,28 +1,27 @@ --- name: gradio description: > - Gradio patterns for building interactive ML demos and model serving UIs. - Covers gr.Interface, gr.Blocks layouts, image/video/text inputs and outputs, - model serving with gr.load, custom components, flagging and feedback collection, - deployment to Hugging Face Spaces, and integration with PyTorch/ONNX models. + Use this skill when building an interactive demo or lightweight UI to try an ML model — + gr.Interface and gr.Blocks layouts, image/video/text inputs and outputs, loading models + with gr.load, custom components, flagging and feedback collection, and deploying to + Hugging Face Spaces. Reach for it any time you'd otherwise hand-build a front-end so + people can play with a model, even if the user just says "make a UI to test the model" + or "put up a demo". For a production JSON serving API instead of a demo, see fastapi. --- # Gradio Skill -You are building Gradio applications for interactive ML demos and model serving UIs. Follow these patterns exactly. +Build Gradio 4.x demos with typed interfaces, Pydantic-validated configs, and +Loguru logging. Use `gr.Interface` for a single prediction function; use +`gr.Blocks` for multi-step workflows, comparisons, tabs, and conditional +visibility. Never expose raw model internals to the UI layer. -## Core Philosophy +## gr.Interface — Single-Function Demos -Gradio provides the fastest path from a trained model to an interactive web demo. Every demo in this framework uses Gradio 4.x with typed interfaces, Pydantic-validated configurations, and Loguru-based logging. Use `gr.Blocks` for complex layouts and `gr.Interface` for simple single-function demos. Never expose raw model internals to the UI layer. - -## Interface Creation - -### Simple Interface with gr.Interface - -Use `gr.Interface` when wrapping a single prediction function with clearly defined inputs and outputs. This is the fastest way to prototype a demo. +Fastest way to wrap one prediction function with defined inputs/outputs. ```python -"""Simple image classification demo with gr.Interface.""" +"""Image classification demo with gr.Interface.""" from __future__ import annotations @@ -34,8 +33,6 @@ from pydantic import BaseModel, Field class ClassificationConfig(BaseModel, frozen=True): - """Configuration for classification demo.""" - model_path: str = "models/classifier.onnx" labels_path: str = "models/labels.txt" top_k: int = Field(default=5, ge=1, le=20) @@ -46,7 +43,7 @@ def classify_image( image: Image.Image, config: ClassificationConfig = ClassificationConfig(), ) -> dict[str, float]: - """Run classification on a single image and return top-k predictions.""" + """Return top-k predictions for a single image.""" logger.info("Received image of size {}", image.size) preprocessed = preprocess(image, config.image_size) predictions = run_model(preprocessed, config.model_path) @@ -54,7 +51,7 @@ def classify_image( top_indices = np.argsort(predictions)[-config.top_k :][::-1] results = {labels[i]: float(predictions[i]) for i in top_indices} - logger.info("Top prediction: {} ({:.3f})", list(results.keys())[0], list(results.values())[0]) + logger.info("Top prediction: {} ({:.3f})", *next(iter(results.items()))) return results @@ -63,33 +60,28 @@ demo = gr.Interface( inputs=gr.Image(type="pil", label="Upload Image"), outputs=gr.Label(num_top_classes=5, label="Predictions"), title="Image Classifier", - description="Upload an image to classify it using the trained model.", + description="Upload an image to classify it.", examples=[["examples/cat.jpg"], ["examples/dog.jpg"]], cache_examples=True, ) ``` -### Complex Layouts with gr.Blocks +## gr.Blocks — Complex Layouts -Use `gr.Blocks` for multi-step workflows, side-by-side comparisons, tabs, and conditional visibility. This is the standard for production demos. +Standard for production demos. Combines tabs, rows/columns, buttons, streaming, +and batch inputs. The pattern below shows upload, webcam streaming, and batch tabs. ```python """Object detection demo with gr.Blocks layout.""" from __future__ import annotations -from pathlib import Path - import gradio as gr -import numpy as np from loguru import logger -from PIL import Image from pydantic import BaseModel, Field class DetectionConfig(BaseModel, frozen=True): - """Detection demo configuration.""" - model_path: str = "models/detector.onnx" confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) nms_threshold: float = Field(default=0.45, ge=0.0, le=1.0) @@ -98,52 +90,39 @@ class DetectionConfig(BaseModel, frozen=True): def build_detection_demo(config: DetectionConfig | None = None) -> gr.Blocks: - """Build the detection demo application.""" config = config or DetectionConfig() logger.info("Building detection demo with model: {}", config.model_path) with gr.Blocks(title="Object Detection", theme=gr.themes.Soft()) as demo: gr.Markdown("# Object Detection Demo") - gr.Markdown("Upload an image or use the webcam to detect objects in real time.") with gr.Tab("Image Upload"): with gr.Row(): with gr.Column(scale=1): input_image = gr.Image(type="pil", label="Input Image") confidence_slider = gr.Slider( - minimum=0.0, - maximum=1.0, - value=config.confidence_threshold, - step=0.05, - label="Confidence Threshold", + 0.0, 1.0, value=config.confidence_threshold, + step=0.05, label="Confidence Threshold", ) detect_btn = gr.Button("Detect Objects", variant="primary") - with gr.Column(scale=1): output_image = gr.Image(type="pil", label="Detections") output_json = gr.JSON(label="Detection Results") - detect_btn.click( fn=run_detection, inputs=[input_image, confidence_slider], outputs=[output_image, output_json], ) - with gr.Tab("Webcam"): + with gr.Tab("Webcam"): # .stream() for real-time per-frame processing webcam_input = gr.Image(sources=["webcam"], type="pil", label="Webcam Feed") webcam_output = gr.Image(type="pil", label="Live Detections") webcam_input.stream( - fn=run_detection_realtime, - inputs=[webcam_input], - outputs=[webcam_output], + fn=run_detection_realtime, inputs=[webcam_input], outputs=[webcam_output], ) - with gr.Tab("Batch Processing"): - batch_input = gr.File( - file_count="multiple", - file_types=["image"], - label="Upload Multiple Images", - ) + with gr.Tab("Batch Processing"): # gr.File(multiple) + gr.Gallery output + batch_input = gr.File(file_count="multiple", file_types=["image"], label="Images") batch_output = gr.Gallery(label="Results", columns=3) batch_btn = gr.Button("Process Batch", variant="primary") batch_btn.click( @@ -155,21 +134,13 @@ def build_detection_demo(config: DetectionConfig | None = None) -> gr.Blocks: return demo ``` -## Image, Video, and Text Inputs and Outputs +## Multi-Modal Inputs and Outputs -### Multi-Modal Input/Output Patterns +Mix `gr.Image`, `gr.Video`, `gr.Textbox`, `gr.Gallery`, and `gr.JSON`. Wire events +with `.change()` (fire on input change) or `.click()` (fire on button). ```python -"""Multi-modal Gradio interface patterns.""" - -from __future__ import annotations - -import gradio as gr -from loguru import logger - - def build_multimodal_demo() -> gr.Blocks: - """Build a demo with image, video, and text I/O.""" with gr.Blocks() as demo: gr.Markdown("# Multi-Modal Analysis") @@ -178,67 +149,48 @@ def build_multimodal_demo() -> gr.Blocks: caption_output = gr.Textbox(label="Generated Caption", lines=3) img_input.change(fn=generate_caption, inputs=[img_input], outputs=[caption_output]) - with gr.Tab("Video Analysis"): + with gr.Tab("Video Analysis"): # one input -> multiple outputs video_input = gr.Video(label="Upload Video") video_output = gr.Video(label="Annotated Video") frame_gallery = gr.Gallery(label="Key Frames", columns=4) analysis_json = gr.JSON(label="Analysis Results") - - analyze_btn = gr.Button("Analyze Video", variant="primary") - analyze_btn.click( + gr.Button("Analyze Video", variant="primary").click( fn=analyze_video, inputs=[video_input], outputs=[video_output, frame_gallery, analysis_json], ) - with gr.Tab("Visual Question Answering"): + with gr.Tab("Visual Question Answering"): # multiple inputs -> one output with gr.Row(): vqa_image = gr.Image(type="pil", label="Image") with gr.Column(): vqa_question = gr.Textbox(label="Question", placeholder="What is in this image?") vqa_answer = gr.Textbox(label="Answer", interactive=False) - vqa_btn = gr.Button("Ask", variant="primary") - - vqa_btn.click( - fn=answer_question, - inputs=[vqa_image, vqa_question], - outputs=[vqa_answer], - ) + gr.Button("Ask", variant="primary").click( + fn=answer_question, inputs=[vqa_image, vqa_question], outputs=[vqa_answer], + ) return demo ``` -## Model Serving with gr.load - -### Loading Models from Hugging Face Hub +## Model Serving -Use `gr.load` to quickly create a demo from a Hugging Face model without writing inference code. +### From the Hugging Face Hub with gr.load ```python -"""Serve Hugging Face models directly with gr.load.""" - -from __future__ import annotations - -import gradio as gr -from loguru import logger - -# Load a model directly from the Hugging Face Hub -logger.info("Loading model from Hugging Face Hub") demo = gr.load( name="facebook/detr-resnet-50", src="models", title="DETR Object Detection", - description="Detect objects using DETR (DEtection TRansformer).", -) - -# Load a Hugging Face Space as a component -image_gen = gr.load( - name="stabilityai/stable-diffusion-2", - src="models", + description="Detect objects using DETR.", ) ``` -### Serving Custom PyTorch and ONNX Models +### Custom ONNX / PyTorch Model Server + +Wrap the model in a class loaded once at startup; reference it via closure in the +callback. Same pattern applies to a PyTorch checkpoint (swap `ort.InferenceSession` +for `torch.load(...).eval()` and preprocess with `torchvision.transforms`). ```python """Serve a custom ONNX model through Gradio.""" @@ -256,8 +208,6 @@ from pydantic import BaseModel, Field class ONNXModelConfig(BaseModel, frozen=True): - """ONNX model serving configuration.""" - model_path: Path = Path("models/model.onnx") input_size: tuple[int, int] = (640, 640) providers: list[str] = Field( @@ -266,99 +216,96 @@ class ONNXModelConfig(BaseModel, frozen=True): class ModelServer: - """Wraps an ONNX model for Gradio serving.""" + """Wraps an ONNX model for Gradio serving (loaded once at startup).""" def __init__(self, config: ONNXModelConfig) -> None: self.config = config logger.info("Loading ONNX model from {}", config.model_path) - self.session = ort.InferenceSession( - str(config.model_path), - providers=config.providers, - ) + self.session = ort.InferenceSession(str(config.model_path), providers=config.providers) self.input_name = self.session.get_inputs()[0].name - logger.info("Model loaded successfully with providers: {}", config.providers) def predict(self, image: Image.Image) -> tuple[Image.Image, dict]: - """Run inference and return annotated image and results dict.""" - logger.debug("Preprocessing image of size {}", image.size) + """Run inference, return annotated image and results dict.""" input_array = self._preprocess(image) - outputs = self.session.run(None, {self.input_name: input_array}) detections = self._postprocess(outputs, image.size) - annotated = self._draw_detections(image, detections) - results = {"num_detections": len(detections), "detections": detections} logger.info("Found {} detections", len(detections)) - return annotated, results + return annotated, {"num_detections": len(detections), "detections": detections} def _preprocess(self, image: Image.Image) -> np.ndarray: - """Resize and normalize input image.""" resized = image.resize(self.config.input_size) array = np.array(resized, dtype=np.float32) / 255.0 return np.transpose(array, (2, 0, 1))[np.newaxis, ...] def _postprocess(self, outputs: list, original_size: tuple[int, int]) -> list[dict]: - """Convert raw model outputs to detection dicts.""" - # Implementation depends on model architecture - ... + ... # depends on model architecture def _draw_detections(self, image: Image.Image, detections: list[dict]) -> Image.Image: - """Draw bounding boxes on the image.""" - # Implementation uses PIL.ImageDraw - ... + ... # uses PIL.ImageDraw def create_onnx_demo(config: ONNXModelConfig | None = None) -> gr.Blocks: - """Create a Gradio demo serving an ONNX model.""" - config = config or ONNXModelConfig() - server = ModelServer(config) - + server = ModelServer(config or ONNXModelConfig()) with gr.Blocks(title="ONNX Model Demo") as demo: gr.Markdown("# Custom ONNX Model Demo") with gr.Row(): input_img = gr.Image(type="pil", label="Input") output_img = gr.Image(type="pil", label="Output") results_json = gr.JSON(label="Results") - run_btn = gr.Button("Run Inference", variant="primary") - - run_btn.click( - fn=server.predict, - inputs=[input_img], - outputs=[output_img, results_json], + gr.Button("Run Inference", variant="primary").click( + fn=server.predict, inputs=[input_img], outputs=[output_img, results_json], ) - return demo ``` -## Custom Components and Layouts - -### Reusable Component Patterns +### PyTorch Model with @torch.inference_mode ```python -"""Reusable Gradio component patterns.""" +from torchvision import transforms -from __future__ import annotations -import gradio as gr -from loguru import logger +def create_pytorch_demo(config: TorchModelConfig) -> gr.Blocks: + model = torch.load(config.checkpoint_path, map_location=config.device) + model.eval() + transform = transforms.Compose([ + transforms.Resize(config.input_size), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + @torch.inference_mode() + def predict(image: Image.Image) -> dict[str, float]: + tensor = transform(image).unsqueeze(0).to(config.device) + probs = torch.nn.functional.softmax(model(tensor)[0], dim=0) + top5_prob, top5_idx = torch.topk(probs, 5) + return { + (config.class_names[idx] if config.class_names else f"class_{idx}"): float(prob) + for prob, idx in zip(top5_prob, top5_idx) + } -def create_model_selector( - models: dict[str, str], - default: str | None = None, -) -> gr.Dropdown: - """Create a model selection dropdown with display names.""" + with gr.Blocks(title="PyTorch Model Demo") as demo: + with gr.Row(): + input_img = gr.Image(type="pil", label="Upload Image") + output_label = gr.Label(num_top_classes=5, label="Predictions") + gr.Button("Classify", variant="primary").click( + fn=predict, inputs=[input_img], outputs=[output_label], + ) + return demo +``` + +## Reusable Components and Comparison Layouts + +Factor repeated controls into helper functions returning components. Reuse them to +build side-by-side comparison layouts. + +```python +def create_model_selector(models: dict[str, str], default: str | None = None) -> gr.Dropdown: choices = list(models.keys()) - return gr.Dropdown( - choices=choices, - value=default or choices[0], - label="Select Model", - info="Choose a model for inference", - ) + return gr.Dropdown(choices=choices, value=default or choices[0], label="Select Model") def create_preprocessing_controls() -> tuple[gr.Slider, gr.Slider, gr.Checkbox]: - """Create standard image preprocessing controls.""" brightness = gr.Slider(-1.0, 1.0, value=0.0, step=0.1, label="Brightness") contrast = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Contrast") grayscale = gr.Checkbox(value=False, label="Convert to Grayscale") @@ -366,63 +313,45 @@ def create_preprocessing_controls() -> tuple[gr.Slider, gr.Slider, gr.Checkbox]: def build_comparison_layout() -> gr.Blocks: - """Build a side-by-side model comparison layout.""" with gr.Blocks() as demo: - gr.Markdown("# Model Comparison") input_image = gr.Image(type="pil", label="Input Image") - with gr.Row(): with gr.Column(): gr.Markdown("### Model A") - model_a_selector = create_model_selector( - {"YOLOv8-n": "yolov8n", "YOLOv8-s": "yolov8s"}, - default="YOLOv8-n", - ) + model_a = create_model_selector({"YOLOv8-n": "yolov8n", "YOLOv8-s": "yolov8s"}) output_a = gr.Image(type="pil", label="Model A Output") metrics_a = gr.JSON(label="Model A Metrics") - with gr.Column(): gr.Markdown("### Model B") - model_b_selector = create_model_selector( - {"YOLOv8-m": "yolov8m", "YOLOv8-l": "yolov8l"}, - default="YOLOv8-m", - ) + model_b = create_model_selector({"YOLOv8-m": "yolov8m", "YOLOv8-l": "yolov8l"}) output_b = gr.Image(type="pil", label="Model B Output") metrics_b = gr.JSON(label="Model B Metrics") - - compare_btn = gr.Button("Compare Models", variant="primary") - compare_btn.click( + gr.Button("Compare Models", variant="primary").click( fn=compare_models, - inputs=[input_image, model_a_selector, model_b_selector], + inputs=[input_image, model_a, model_b], outputs=[output_a, metrics_a, output_b, metrics_b], ) - return demo ``` ## Flagging and Feedback Collection -### Custom Flagging Callback - -Use flagging to collect user feedback on model predictions for dataset improvement and active learning. +Subclass `gr.FlaggingCallback` to persist structured feedback for active learning. ```python """Custom flagging callback for ML feedback collection.""" from __future__ import annotations -import json from datetime import datetime, timezone from pathlib import Path import gradio as gr from loguru import logger -from pydantic import BaseModel, Field +from pydantic import BaseModel class FlaggedSample(BaseModel, frozen=True): - """A flagged sample with metadata.""" - timestamp: str flag_reason: str input_hash: str @@ -431,25 +360,15 @@ class FlaggedSample(BaseModel, frozen=True): class MLFlaggingCallback(gr.FlaggingCallback): - """Custom flagging callback that saves structured feedback.""" - def __init__(self, output_dir: str = "flagged_data") -> None: self.output_dir = Path(output_dir) self.output_dir.mkdir(parents=True, exist_ok=True) - logger.info("Flagging callback initialized, saving to {}", self.output_dir) def setup(self, components: list, flagging_dir: str | Path) -> None: - """Set up the flagging directory.""" self.flagging_dir = Path(flagging_dir) self.flagging_dir.mkdir(parents=True, exist_ok=True) - def flag( - self, - flag_data: list, - flag_option: str = "incorrect", - username: str | None = None, - ) -> int: - """Save a flagged sample with structured metadata.""" + def flag(self, flag_data: list, flag_option: str = "incorrect", username: str | None = None) -> int: sample = FlaggedSample( timestamp=datetime.now(tz=timezone.utc).isoformat(), flag_reason=flag_option, @@ -457,13 +376,11 @@ class MLFlaggingCallback(gr.FlaggingCallback): prediction=str(flag_data[1]) if len(flag_data) > 1 else "", user_correction=str(flag_data[2]) if len(flag_data) > 2 else None, ) - output_path = self.output_dir / f"flag_{sample.timestamp}.json" - output_path.write_text(sample.model_dump_json(indent=2)) - logger.info("Flagged sample saved: {} (reason: {})", output_path, flag_option) + (self.output_dir / f"flag_{sample.timestamp}.json").write_text(sample.model_dump_json(indent=2)) + logger.info("Flagged sample saved (reason: {})", flag_option) return 1 -# Usage in a demo demo = gr.Interface( fn=classify_image, inputs=gr.Image(type="pil"), @@ -475,43 +392,22 @@ demo = gr.Interface( ## Deployment -### Sharing and Hugging Face Spaces +Launch with production settings; optionally add auth or mount inside FastAPI. +Never use `share=True` in production — host on Spaces, Docker, or behind a proxy. ```python -"""Deployment patterns for Gradio demos.""" - -from __future__ import annotations - -import gradio as gr -from loguru import logger - - def launch_demo(demo: gr.Blocks, share: bool = False) -> None: - """Launch the Gradio demo with production settings.""" - logger.info("Launching Gradio demo (share={})", share) demo.launch( server_name="0.0.0.0", server_port=7860, share=share, show_error=True, max_threads=10, - auth=None, # Set ("user", "pass") for basic auth + auth=None, # or [("admin", "secure_password")] for basic auth ) -def launch_with_auth(demo: gr.Blocks) -> None: - """Launch with authentication for internal deployments.""" - logger.info("Launching Gradio demo with authentication") - demo.launch( - server_name="0.0.0.0", - server_port=7860, - auth=[("admin", "secure_password")], - auth_message="Enter credentials to access the demo.", - ) - - -def mount_on_fastapi(demo: gr.Blocks) -> None: - """Mount Gradio inside a FastAPI application.""" +def mount_on_fastapi(demo: gr.Blocks): from fastapi import FastAPI app = FastAPI(title="ML Demo API") @@ -520,143 +416,52 @@ def mount_on_fastapi(demo: gr.Blocks) -> None: async def health() -> dict[str, str]: return {"status": "healthy"} - app = gr.mount_gradio_app(app, demo, path="/demo") - logger.info("Gradio demo mounted at /demo") + return gr.mount_gradio_app(app, demo, path="/demo") ``` -### Hugging Face Spaces Dockerfile +### Hugging Face Spaces + +`Dockerfile` and `app.py` entry point: ```dockerfile FROM python:3.11-slim - WORKDIR /app - -RUN pip install --no-cache-dir uv -COPY pyproject.toml uv.lock ./ -RUN uv sync --frozen --no-dev - +RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" +COPY pixi.toml pixi.lock ./ +RUN pixi install COPY src/ ./src/ COPY models/ ./models/ - EXPOSE 7860 - -CMD ["uv", "run", "python", "-m", "src.app", "--server-name", "0.0.0.0", "--server-port", "7860"] +CMD ["pixi", "run", "python", "-m", "src.app", "--server-name", "0.0.0.0", "--server-port", "7860"] ``` -### Hugging Face Spaces app.py entry point - ```python -"""Hugging Face Spaces entry point.""" - -from __future__ import annotations +"""app.py — Hugging Face Spaces entry point.""" -import gradio as gr -from loguru import logger - -from src.demo import build_detection_demo from src.config import DetectionConfig +from src.demo import build_detection_demo - -logger.info("Starting Hugging Face Spaces demo") -config = DetectionConfig() -demo = build_detection_demo(config) +demo = build_detection_demo(DetectionConfig()) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860) ``` -## Integration with PyTorch Models - -### PyTorch Lightning Model in Gradio - -```python -"""Serve a PyTorch Lightning model through Gradio.""" - -from __future__ import annotations - -from pathlib import Path - -import gradio as gr -import torch -from loguru import logger -from PIL import Image -from pydantic import BaseModel, Field -from torchvision import transforms - - -class TorchModelConfig(BaseModel, frozen=True): - """PyTorch model serving configuration.""" - - checkpoint_path: Path = Path("checkpoints/best.ckpt") - device: str = "cuda" if torch.cuda.is_available() else "cpu" - input_size: tuple[int, int] = (224, 224) - class_names: list[str] = Field(default_factory=list) - - -def load_pytorch_model(config: TorchModelConfig) -> torch.nn.Module: - """Load a PyTorch model from checkpoint.""" - logger.info("Loading PyTorch model from {}", config.checkpoint_path) - model = torch.load(config.checkpoint_path, map_location=config.device) - model.eval() - logger.info("Model loaded on device: {}", config.device) - return model - - -def create_pytorch_demo(config: TorchModelConfig | None = None) -> gr.Blocks: - """Create a Gradio demo for a PyTorch model.""" - config = config or TorchModelConfig() - model = load_pytorch_model(config) - - transform = transforms.Compose([ - transforms.Resize(config.input_size), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ]) - - @torch.inference_mode() - def predict(image: Image.Image) -> dict[str, float]: - """Run inference on a single image.""" - tensor = transform(image).unsqueeze(0).to(config.device) - output = model(tensor) - probabilities = torch.nn.functional.softmax(output[0], dim=0) - - top5_prob, top5_idx = torch.topk(probabilities, 5) - results = {} - for prob, idx in zip(top5_prob, top5_idx): - label = config.class_names[idx] if config.class_names else f"class_{idx}" - results[label] = float(prob) - - logger.info("Top prediction: {} ({:.3f})", list(results.keys())[0], list(results.values())[0]) - return results - - with gr.Blocks(title="PyTorch Model Demo") as demo: - gr.Markdown("# PyTorch Model Demo") - with gr.Row(): - input_img = gr.Image(type="pil", label="Upload Image") - output_label = gr.Label(num_top_classes=5, label="Predictions") - predict_btn = gr.Button("Classify", variant="primary") - predict_btn.click(fn=predict, inputs=[input_img], outputs=[output_label]) - - return demo -``` - ## Anti-Patterns -- **Never load models inside Gradio callback functions** -- load models once at startup and reference them via closure or a server class. Reloading on every request causes massive latency. -- **Never use `gr.Interface` for complex multi-step workflows** -- use `gr.Blocks` with explicit layout control for anything beyond a single input-output function. -- **Never expose raw tensors or numpy arrays to Gradio outputs** -- always convert to PIL Images, JSON-serializable dicts, or strings before returning. -- **Never hardcode file paths in Gradio demos** -- use Pydantic config models to make paths configurable for different environments. -- **Never skip input validation** -- validate image sizes, file types, and parameter ranges before feeding data to the model. -- **Never use `share=True` in production deployments** -- use proper hosting (Hugging Face Spaces, Docker, or a reverse proxy) instead of Gradio's temporary share links. -- **Never block the main thread with long-running inference** -- use `gr.Progress` for progress tracking and consider async patterns for heavy workloads. -- **Never ignore logging** -- use Loguru for structured logging in all prediction functions, model loading, and error handling. +- **Never load models inside callbacks** -- load once at startup and reference via closure or a server class. Reloading per request adds massive latency. +- **Never use `gr.Interface` for multi-step workflows** -- use `gr.Blocks` for explicit layout control. +- **Never expose raw tensors/numpy to outputs** -- convert to PIL Images, JSON-serializable dicts, or strings first. +- **Never hardcode file paths** -- use Pydantic config models so paths are environment-configurable. +- **Never skip input validation** -- check image sizes, file types, and parameter ranges before inference. +- **Never use `share=True` in production** -- use proper hosting. +- **Never block the main thread** on long inference -- use `gr.Progress` and consider async patterns. +- **Never skip logging** -- use Loguru in prediction functions, model loading, and error handling. ## Integration with Other Skills -- **Pydantic Strict** -- All configuration models follow frozen BaseModel patterns with validators. -- **Loguru** -- Structured logging in model loading, prediction functions, and flagging callbacks. -- **Hugging Face** -- Load pretrained models from the Hub and deploy demos to Hugging Face Spaces. -- **FastAPI** -- Mount Gradio demos inside FastAPI applications for combined API + demo serving. -- **ONNX** -- Serve optimized ONNX models through Gradio for low-latency inference. -- **PyTorch Lightning** -- Load Lightning checkpoints and serve trained models through Gradio. -- **Testing** -- Test Gradio components with the Gradio test client and pytest. +Pydantic (frozen config models), Loguru (structured logging), Hugging Face +(load Hub models, deploy to Spaces), FastAPI (mount demos), ONNX (low-latency +serving), PyTorch Lightning (load checkpoints), Testing (Gradio test client + pytest). diff --git a/skills/gradio/skill.toml b/skills/gradio/skill.toml index 8889988..f9d8c3b 100644 --- a/skills/gradio/skill.toml +++ b/skills/gradio/skill.toml @@ -5,7 +5,7 @@ category = "infra" tags = ["demo", "ui", "gradio", "prototyping", "serving"] [dependencies] -requires = ["pydantic-strict", "loguru"] +requires = ["pydantic", "loguru"] recommends = ["huggingface", "fastapi"] [compatibility] diff --git a/skills/huggingface/SKILL.md b/skills/huggingface/SKILL.md index ab96597..802e047 100644 --- a/skills/huggingface/SKILL.md +++ b/skills/huggingface/SKILL.md @@ -1,18 +1,17 @@ --- name: huggingface description: > - Hugging Face ecosystem patterns for NLP and vision. Covers Transformers - models, datasets library, tokenizers, pipelines, fine-tuning with Trainer, - PEFT/LoRA adapters, model hub publishing, and inference optimization. + Use this skill when working with the Hugging Face ecosystem for NLP or vision — loading + Transformers models with AutoModel, the datasets library, tokenizers, task pipelines, + fine-tuning with Trainer, PEFT/LoRA adapters, publishing to the Model Hub, and + quantization/inference optimization. Reach for it any time a pretrained transformer, an + HF dataset, or the Hub is involved, even if the user doesn't say "Hugging Face". For the + training-loop framework around these models see pytorch-lightning. --- # Hugging Face Skill -You are working with the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Follow these patterns exactly. - -## Core Philosophy - -Hugging Face provides a unified API for loading, fine-tuning, and deploying pretrained models. Use `AutoModel` and `AutoTokenizer` classes for model loading — never hardcode model class names unless absolutely necessary. Use the `datasets` library for all data loading and preprocessing. Use the `Trainer` API for fine-tuning unless you need custom training loops. +Patterns for the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Use `Auto*` classes for loading (never hardcode model class names), the `datasets` library for data, and the `Trainer` API for fine-tuning unless you need a custom loop. ## Model Loading @@ -72,13 +71,8 @@ def load_vision_model(config: ModelConfig) -> tuple: def load_text_model(config: ModelConfig) -> tuple: - """Load a text model and tokenizer.""" - tokenizer = AutoTokenizer.from_pretrained( - config.model_name, - revision=config.revision, - cache_dir=config.cache_dir, - ) - + """Load a text model and tokenizer (same shape as the vision path).""" + tokenizer = AutoTokenizer.from_pretrained(config.model_name, revision=config.revision) model = AutoModelForSequenceClassification.from_pretrained( config.model_name, revision=config.revision, @@ -86,7 +80,6 @@ def load_text_model(config: ModelConfig) -> tuple: device_map=config.device_map, cache_dir=config.cache_dir, ) - return model, tokenizer ``` @@ -211,22 +204,9 @@ class FinetuneConfig(BaseModel, frozen=True): def create_training_args(config: FinetuneConfig) -> TrainingArguments: - """Create TrainingArguments from config.""" + """Map config fields directly onto TrainingArguments, plus fixed extras.""" return TrainingArguments( - output_dir=config.output_dir, - num_train_epochs=config.num_train_epochs, - per_device_train_batch_size=config.per_device_train_batch_size, - per_device_eval_batch_size=config.per_device_eval_batch_size, - learning_rate=config.learning_rate, - weight_decay=config.weight_decay, - warmup_ratio=config.warmup_ratio, - fp16=config.fp16, - eval_strategy=config.eval_strategy, - save_strategy=config.save_strategy, - load_best_model_at_end=config.load_best_model_at_end, - metric_for_best_model=config.metric_for_best_model, - push_to_hub=config.push_to_hub, - hub_model_id=config.hub_model_id, + **config.model_dump(), logging_steps=50, dataloader_num_workers=4, dataloader_pin_memory=True, @@ -365,47 +345,21 @@ def create_image_classifier( device: int = 0, ) -> pipeline: """Create an image classification pipeline.""" - return pipeline( - "image-classification", - model=model_name, - device=device, - ) + return pipeline("image-classification", model=model_name, device=device) -def create_object_detector( - model_name: str = "facebook/detr-resnet-50", - device: int = 0, - threshold: float = 0.5, -) -> pipeline: +# Same pattern for other tasks — just change the task string and default model: +# "object-detection" (facebook/detr-resnet-50, add threshold=0.5) +# "zero-shot-image-classification" (openai/clip-vit-base-patch32) +def create_object_detector(model_name: str = "facebook/detr-resnet-50", device: int = 0) -> pipeline: """Create an object detection pipeline.""" - return pipeline( - "object-detection", - model=model_name, - device=device, - threshold=threshold, - ) + return pipeline("object-detection", model=model_name, device=device, threshold=0.5) -def create_zero_shot_classifier( - model_name: str = "openai/clip-vit-base-patch32", - device: int = 0, -) -> pipeline: - """Create a zero-shot image classification pipeline.""" - return pipeline( - "zero-shot-image-classification", - model=model_name, - device=device, - ) - - -# Usage +# Usage: classifier(...) -> [{"label": "cat", "score": 0.97}, ...] +# detector(...) -> [{"label": "person", "score": 0.99, "box": {...}}, ...] classifier = create_image_classifier() results = classifier("path/to/image.jpg") -# [{"label": "cat", "score": 0.97}, {"label": "dog", "score": 0.02}] - -detector = create_object_detector() -results = detector("path/to/image.jpg") -# [{"label": "person", "score": 0.99, "box": {"xmin": 10, "ymin": 20, "xmax": 200, "ymax": 400}}] ``` ## Model Hub Publishing @@ -445,30 +399,23 @@ def push_model_to_hub( def create_model_card( - repo_id: str, - model_name: str, - dataset_name: str, - metrics: dict[str, float], - language: str = "en", + repo_id: str, model_name: str, dataset_name: str, metrics: dict[str, float] ) -> ModelCard: - """Create and push a model card.""" + """Create and push a model card (always include license, dataset, metrics).""" card_data = ModelCardData( - language=language, + language="en", license="apache-2.0", model_name=model_name, datasets=[dataset_name], metrics=list(metrics.keys()), ) - card = ModelCard.from_template( card_data, model_id=repo_id, model_description=f"Fine-tuned {model_name} on {dataset_name}.", training_metrics=metrics, ) - card.push_to_hub(repo_id) - logger.info("Model card pushed to: {}", repo_id) return card ``` @@ -522,9 +469,4 @@ def load_quantized_model( ## Integration with Other Skills -- **PyTorch Lightning** — Use Lightning Trainer for custom training loops with HF models. -- **W&B** — Pass `report_to=["wandb"]` in TrainingArguments for experiment tracking. -- **ONNX** — Export HF models via `optimum` for optimized inference. -- **AWS SageMaker** — Deploy HF models on SageMaker with the HF DLC (Deep Learning Container). -- **FastAPI** — Serve HF pipelines behind async API endpoints. -- **DVC** — Version datasets downloaded from Hugging Face Hub. +Wrap HF models in a Lightning Trainer for custom loops; set `report_to=["wandb"]` for tracking; export via `optimum`/ONNX for inference; deploy on SageMaker (HF DLC) or behind FastAPI; version Hub datasets with DVC. diff --git a/skills/huggingface/skill.toml b/skills/huggingface/skill.toml index bcf14fb..466b4de 100644 --- a/skills/huggingface/skill.toml +++ b/skills/huggingface/skill.toml @@ -5,7 +5,7 @@ category = "cv-ml" tags = ["transformers", "datasets", "fine-tuning", "pretrained", "nlp", "vision"] [dependencies] -requires = ["pydantic-strict", "loguru"] +requires = ["pydantic", "loguru"] recommends = ["pytorch-lightning", "wandb", "onnx", "fastapi"] [compatibility] diff --git a/skills/hydra-config/README.md b/skills/hydra-config/README.md index 377b02a..a981b7d 100644 --- a/skills/hydra-config/README.md +++ b/skills/hydra-config/README.md @@ -42,5 +42,5 @@ python train.py --multirun training.learning_rate=1e-3,1e-4,1e-5 ## See Also - `SKILL.md` in this directory for full documentation and code examples -- `pydantic-strict` skill for validation patterns +- `pydantic` skill for validation patterns - `pytorch-lightning` skill for training loop integration diff --git a/skills/hydra-config/SKILL.md b/skills/hydra-config/SKILL.md index f31063a..939bfc6 100644 --- a/skills/hydra-config/SKILL.md +++ b/skills/hydra-config/SKILL.md @@ -1,9 +1,12 @@ --- name: hydra-config description: > - Manage complex hierarchical configurations for ML experiments using Hydra. - Covers structured configs, config composition, command-line overrides, multi-run - sweeps, config groups, and Pydantic validation integration. + Use this skill when managing complex, hierarchical experiment configuration with Hydra + — structured/dataclass configs, config groups and composition, command-line overrides, + multi-run sweeps, and validating the composed config with Pydantic. Reach for it any + time an experiment has many swappable config pieces (model/data/augmentation) and you'd + otherwise juggle argparse flags, even if the user doesn't say "Hydra". For plain data + or settings validation without config composition, see pydantic. --- # Hydra Configuration Skill diff --git a/skills/hydra-config/skill.toml b/skills/hydra-config/skill.toml index a157fa8..3a90051 100644 --- a/skills/hydra-config/skill.toml +++ b/skills/hydra-config/skill.toml @@ -5,7 +5,7 @@ category = "cv-ml" tags = ["configuration", "hydra", "omegaconf", "experiment-management"] [dependencies] -requires = ["pydantic-strict"] +requires = ["pydantic"] recommends = ["pytorch-lightning"] [compatibility] diff --git a/skills/kubernetes/SKILL.md b/skills/kubernetes/SKILL.md index fcf521f..3a2e608 100644 --- a/skills/kubernetes/SKILL.md +++ b/skills/kubernetes/SKILL.md @@ -1,15 +1,18 @@ --- name: kubernetes description: > - Kubernetes deployment patterns for ML inference and training services. Covers - GPU resource scheduling, Helm charts, Kustomize overlays, health probes, - autoscaling, persistent volumes for model storage, and namespace organization - for dev/staging/prod environments. + Use this skill when deploying ML inference or training services to Kubernetes — + Deployments, GPU scheduling, Services and Ingress, HPA autoscaling, ConfigMaps and + Secrets, persistent volumes for model storage, health probes, Helm charts, and + Kustomize overlays for dev/staging/prod. Reach for it any time you'd otherwise hand-write + k8s manifests or a Helm chart for a model server, even if the user just says "deploy the + model to the cluster" or "scale up the endpoint". For building the container image the + cluster runs, see docker-cv. --- # Kubernetes Skill -Kubernetes deployment patterns for ML inference and training services with GPU scheduling, Helm charts, autoscaling, and environment-specific overlays. +Deployment patterns for ML inference and training services: GPU scheduling, Helm charts, autoscaling, and environment-specific overlays. ## Deployment Manifests for ML Services @@ -44,99 +47,60 @@ spec: - containerPort: 8000 name: http resources: - requests: - cpu: "2" - memory: "4Gi" - nvidia.com/gpu: "1" - limits: - cpu: "4" - memory: "8Gi" - nvidia.com/gpu: "1" + requests: { cpu: "2", memory: "4Gi", nvidia.com/gpu: "1" } + limits: { cpu: "4", memory: "8Gi", nvidia.com/gpu: "1" } envFrom: - - configMapRef: - name: model-config - - secretRef: - name: model-secrets + - configMapRef: { name: model-config } + - secretRef: { name: model-secrets } volumeMounts: - - name: model-storage - mountPath: /models - readOnly: true + - { name: model-storage, mountPath: /models, readOnly: true } livenessProbe: - httpGet: - path: /health - port: http + httpGet: { path: /health, port: http } initialDelaySeconds: 30 periodSeconds: 15 - timeoutSeconds: 5 failureThreshold: 3 readinessProbe: - httpGet: - path: /health/ready - port: http + httpGet: { path: /health/ready, port: http } initialDelaySeconds: 60 periodSeconds: 10 - timeoutSeconds: 5 failureThreshold: 5 - startupProbe: - httpGet: - path: /health - port: http + startupProbe: # allows ~5 min for model load + httpGet: { path: /health, port: http } initialDelaySeconds: 10 periodSeconds: 10 failureThreshold: 30 volumes: - name: model-storage - persistentVolumeClaim: - claimName: model-pvc + persistentVolumeClaim: { claimName: model-pvc } tolerations: - - key: nvidia.com/gpu - operator: Exists - effect: NoSchedule + - { key: nvidia.com/gpu, operator: Exists, effect: NoSchedule } nodeSelector: cloud.google.com/gke-accelerator: nvidia-tesla-t4 ``` ## GPU Resource Requests -Always set both `requests` and `limits` for `nvidia.com/gpu`. Kubernetes GPU scheduling requires exact counts -- fractional GPUs are not natively supported. +Always set both `requests` and `limits` for `nvidia.com/gpu`, and they must be equal — GPU scheduling requires exact counts (fractional GPUs are not natively supported). Multi-GPU pods just raise the count alongside CPU/memory: ```yaml -# GPU resource patterns -resources: - requests: - nvidia.com/gpu: "1" # Request exactly 1 GPU - limits: - nvidia.com/gpu: "1" # Must equal requests for GPUs - -# Multi-GPU training pod resources: - requests: - cpu: "8" - memory: "32Gi" - nvidia.com/gpu: "4" - limits: - cpu: "16" - memory: "64Gi" - nvidia.com/gpu: "4" + requests: { cpu: "8", memory: "32Gi", nvidia.com/gpu: "4" } + limits: { cpu: "16", memory: "64Gi", nvidia.com/gpu: "4" } ``` ### NVIDIA Device Plugin -The NVIDIA device plugin must be deployed in the cluster to enable GPU scheduling. +GPU scheduling requires the NVIDIA device plugin deployed in-cluster: ```bash -# Install the NVIDIA device plugin via Helm -helm repo add nvdp https://nvidia.github.io/k8s-device-plugin -helm repo update - +helm repo add nvdp https://nvidia.github.io/k8s-device-plugin && helm repo update helm install nvidia-device-plugin nvdp/nvidia-device-plugin \ - --namespace kube-system \ - --set runtimeClassName=nvidia + --namespace kube-system --set runtimeClassName=nvidia ``` ## Service and Ingress Configuration -### Service +A `ClusterIP` Service exposes the pods internally: ```yaml # k8s/base/service.yaml @@ -144,21 +108,18 @@ apiVersion: v1 kind: Service metadata: name: model-server - labels: - app: model-server spec: type: ClusterIP ports: - - port: 80 - targetPort: http - protocol: TCP - name: http + - { port: 80, targetPort: http, protocol: TCP, name: http } selector: app: model-server ``` ### Ingress +TLS-terminated Ingress (nginx + cert-manager); raise `proxy-body-size` for large image uploads: + ```yaml # k8s/base/ingress.yaml apiVersion: networking.k8s.io/v1 @@ -172,8 +133,7 @@ metadata: spec: ingressClassName: nginx tls: - - hosts: - - api.ml.example.com + - hosts: [api.ml.example.com] secretName: model-server-tls rules: - host: api.ml.example.com @@ -184,8 +144,7 @@ spec: backend: service: name: model-server - port: - name: http + port: { name: http } ``` ## Horizontal Pod Autoscaler @@ -205,33 +164,21 @@ spec: name: model-server minReplicas: 2 maxReplicas: 10 - behavior: + behavior: # scale up fast (60s window), down slow (300s window) to avoid flapping scaleUp: stabilizationWindowSeconds: 60 - policies: - - type: Pods - value: 2 - periodSeconds: 60 + policies: [{ type: Pods, value: 2, periodSeconds: 60 }] scaleDown: stabilizationWindowSeconds: 300 - policies: - - type: Pods - value: 1 - periodSeconds: 120 - metrics: + policies: [{ type: Pods, value: 1, periodSeconds: 120 }] + metrics: # add a matching memory Resource metric (averageUtilization: 80) as needed - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - - type: Resource - resource: - name: memory - target: - type: Utilization - averageUtilization: 80 - - type: Pods + - type: Pods # custom metric — scale on request rate, not just CPU pods: metric: name: inference_requests_per_second @@ -252,7 +199,6 @@ metadata: name: model-config data: MODEL_NAME: "resnet50" - MODEL_VERSION: "v1.2.0" MODEL_PATH: "/models/resnet50_v1.2.0.onnx" BATCH_SIZE: "8" NUM_WORKERS: "4" @@ -262,25 +208,12 @@ data: ### Secret -```yaml -# k8s/base/secret.yaml -apiVersion: v1 -kind: Secret -metadata: - name: model-secrets -type: Opaque -stringData: - WANDB_API_KEY: "" # Populated via Kustomize or sealed-secrets - S3_ACCESS_KEY: "" - S3_SECRET_KEY: "" -``` +Never commit plaintext secrets to a `Secret` manifest — create them at deploy time (or use sealed-secrets): ```bash -# Create secrets from command line (never commit plaintext secrets) kubectl create secret generic model-secrets \ --from-literal=WANDB_API_KEY="${WANDB_API_KEY}" \ --from-literal=S3_ACCESS_KEY="${S3_ACCESS_KEY}" \ - --from-literal=S3_SECRET_KEY="${S3_SECRET_KEY}" \ --namespace=ml-inference ``` @@ -295,8 +228,7 @@ kind: PersistentVolumeClaim metadata: name: model-pvc spec: - accessModes: - - ReadOnlyMany + accessModes: [ReadOnlyMany] # many pods mount read-only; use ReadWriteOnce for checkpoints storageClassName: standard resources: requests: @@ -306,56 +238,26 @@ spec: ### Init Container to Download Models ```yaml -# Use an init container to pull model weights before the main container starts spec: initContainers: - name: model-downloader image: google/cloud-sdk:slim - command: - - gsutil - - cp - - gs://my-ml-bucket/models/resnet50_v1.2.0.onnx - - /models/resnet50_v1.2.0.onnx + command: ["gsutil", "cp", "gs://my-ml-bucket/models/resnet50.onnx", "/models/resnet50.onnx"] volumeMounts: - - name: model-storage - mountPath: /models + - { name: model-storage, mountPath: /models } containers: - name: model-server image: registry.example.com/ml-images/inference:v1.2.0 volumeMounts: - - name: model-storage - mountPath: /models - readOnly: true + - { name: model-storage, mountPath: /models, readOnly: true } volumes: - name: model-storage - emptyDir: - sizeLimit: 50Gi + emptyDir: { sizeLimit: 50Gi } ``` ## Helm Chart Patterns -Organize Kubernetes manifests into a Helm chart for templated, reusable deployments. - -### Chart Structure - -``` -helm/model-server/ -├── Chart.yaml -├── values.yaml -├── values-dev.yaml -├── values-staging.yaml -├── values-prod.yaml -└── templates/ - ├── _helpers.tpl - ├── deployment.yaml - ├── service.yaml - ├── ingress.yaml - ├── hpa.yaml - ├── configmap.yaml - ├── secret.yaml - ├── pvc.yaml - └── NOTES.txt -``` +Organize manifests into a Helm chart (`helm/model-server/`) with `Chart.yaml`, a base `values.yaml` plus per-env `values-{dev,staging,prod}.yaml`, and a `templates/` dir holding templated copies of each manifest (deployment, service, ingress, hpa, configmap, secret, pvc) plus `_helpers.tpl` and `NOTES.txt`. ### values.yaml @@ -375,14 +277,8 @@ model: storageSizeGi: 50 resources: - requests: - cpu: "2" - memory: "4Gi" - nvidia.com/gpu: "1" - limits: - cpu: "4" - memory: "8Gi" - nvidia.com/gpu: "1" + requests: { cpu: "2", memory: "4Gi", nvidia.com/gpu: "1" } + limits: { cpu: "4", memory: "8Gi", nvidia.com/gpu: "1" } autoscaling: enabled: true @@ -395,106 +291,50 @@ ingress: host: api.ml.example.com tls: true -probes: - liveness: - path: /health - initialDelaySeconds: 30 - periodSeconds: 15 - readiness: - path: /health/ready - initialDelaySeconds: 60 - periodSeconds: 10 - startup: - path: /health - initialDelaySeconds: 10 - failureThreshold: 30 +# probes: liveness/readiness/startup paths + timings (see Deployment manifest) nodeSelector: cloud.google.com/gke-accelerator: nvidia-tesla-t4 tolerations: - - key: nvidia.com/gpu - operator: Exists - effect: NoSchedule + - { key: nvidia.com/gpu, operator: Exists, effect: NoSchedule } ``` ### Helm Deployment Commands ```bash -# Install the chart helm install model-server ./helm/model-server \ - --namespace ml-inference \ - --create-namespace \ + --namespace ml-inference --create-namespace \ --values helm/model-server/values-prod.yaml -# Upgrade with new image tag -helm upgrade model-server ./helm/model-server \ - --namespace ml-inference \ - --set image.tag=v1.3.0 - -# Rollback to previous release +helm upgrade model-server ./helm/model-server --set image.tag=v1.3.0 helm rollback model-server 1 --namespace ml-inference - -# Dry-run to preview rendered templates helm template model-server ./helm/model-server \ - --values helm/model-server/values-staging.yaml \ - --debug + --values helm/model-server/values-staging.yaml --debug # preview ``` ## Health Checks (Liveness, Readiness, and Startup Probes) -ML containers need long startup times for model loading. Use all three probe types. - -```yaml -# Startup probe: allow up to 5 minutes for model loading (10s * 30 attempts) -startupProbe: - httpGet: - path: /health - port: 8000 - initialDelaySeconds: 10 - periodSeconds: 10 - failureThreshold: 30 - -# Liveness probe: restart if unresponsive -livenessProbe: - httpGet: - path: /health - port: 8000 - initialDelaySeconds: 0 - periodSeconds: 15 - timeoutSeconds: 5 - failureThreshold: 3 - -# Readiness probe: remove from service if not ready -readinessProbe: - httpGet: - path: /health/ready - port: 8000 - initialDelaySeconds: 0 - periodSeconds: 10 - timeoutSeconds: 5 - failureThreshold: 5 -``` +ML containers need long startup times for model loading, so use all three probe types (see the Deployment manifest above for the full YAML): a `startupProbe` (`failureThreshold: 30`, `periodSeconds: 10` allows ~5 min to load), a `livenessProbe` to restart hung processes, and a `readinessProbe` on `/health/ready` to gate traffic until the model is loaded. ### FastAPI Health Endpoints +`/health` (liveness) returns alive as soon as the process is up; `/health/ready` (readiness) must return 503 until the model is loaded so traffic is not routed early: + ```python from fastapi import FastAPI, Response, status app = FastAPI() - model_loaded = False @app.get("/health") def health() -> dict[str, str]: - """Liveness probe -- is the process alive?""" return {"status": "alive"} @app.get("/health/ready") def readiness(response: Response) -> dict[str, str]: - """Readiness probe -- is the model loaded and ready for inference?""" if not model_loaded: response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE return {"status": "not_ready", "reason": "model loading"} @@ -503,17 +343,7 @@ def readiness(response: Response) -> dict[str, str]: ## Namespace Organization -Separate environments and workload types using namespaces with resource quotas. - -```bash -# Create namespaces for each environment -kubectl create namespace ml-dev -kubectl create namespace ml-staging -kubectl create namespace ml-prod - -# Create namespace for training jobs -kubectl create namespace ml-training -``` +Separate environments and workload types using namespaces (`ml-dev`, `ml-staging`, `ml-prod`, `ml-training`) via `kubectl create namespace `, each with a resource quota. ### Resource Quotas per Namespace @@ -535,36 +365,7 @@ spec: ## Kustomize Overlays for Dev/Staging/Prod -Use Kustomize to manage environment-specific variations without duplicating manifests. - -### Directory Structure - -``` -k8s/ -├── base/ -│ ├── kustomization.yaml -│ ├── deployment.yaml -│ ├── service.yaml -│ ├── ingress.yaml -│ ├── hpa.yaml -│ ├── configmap.yaml -│ └── pvc.yaml -└── overlays/ - ├── dev/ - │ ├── kustomization.yaml - │ └── patches/ - │ ├── deployment-patch.yaml - │ └── hpa-patch.yaml - ├── staging/ - │ ├── kustomization.yaml - │ └── patches/ - │ └── deployment-patch.yaml - └── prod/ - ├── kustomization.yaml - └── patches/ - ├── deployment-patch.yaml - └── hpa-patch.yaml -``` +Use Kustomize to manage environment-specific variations without duplicating manifests. Layout: `k8s/base/` holds the shared manifests plus a `kustomization.yaml`; `k8s/overlays/{dev,staging,prod}/` each hold a `kustomization.yaml` and a `patches/` directory. ### Base Kustomization @@ -572,19 +373,15 @@ k8s/ # k8s/base/kustomization.yaml apiVersion: kustomize.config.k8s.io/v1beta1 kind: Kustomization -resources: - - deployment.yaml - - service.yaml - - ingress.yaml - - hpa.yaml - - configmap.yaml - - pvc.yaml +resources: [deployment.yaml, service.yaml, ingress.yaml, hpa.yaml, configmap.yaml, pvc.yaml] commonLabels: app.kubernetes.io/name: model-server app.kubernetes.io/managed-by: kustomize ``` -### Dev Overlay +### Overlay Example + +Each overlay references `../../base`, sets a `namespace`, pins the image `newTag`, and applies patches. Only the patch values differ per environment. ```yaml # k8s/overlays/dev/kustomization.yaml @@ -614,81 +411,15 @@ spec: containers: - name: model-server resources: - requests: - cpu: "1" - memory: "2Gi" - nvidia.com/gpu: "1" - limits: - cpu: "2" - memory: "4Gi" - nvidia.com/gpu: "1" -``` - -```yaml -# k8s/overlays/dev/patches/hpa-patch.yaml -apiVersion: autoscaling/v2 -kind: HorizontalPodAutoscaler -metadata: - name: model-server -spec: - minReplicas: 1 - maxReplicas: 2 -``` - -### Prod Overlay - -```yaml -# k8s/overlays/prod/kustomization.yaml -apiVersion: kustomize.config.k8s.io/v1beta1 -kind: Kustomization -namespace: ml-prod -resources: - - ../../base -patches: - - path: patches/deployment-patch.yaml - - path: patches/hpa-patch.yaml -images: - - name: registry.example.com/ml-images/inference - newTag: v1.2.0 -``` - -```yaml -# k8s/overlays/prod/patches/deployment-patch.yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: model-server -spec: - replicas: 3 - template: - spec: - containers: - - name: model-server - resources: - requests: - cpu: "4" - memory: "8Gi" - nvidia.com/gpu: "1" - limits: - cpu: "8" - memory: "16Gi" - nvidia.com/gpu: "1" + requests: { cpu: "1", memory: "2Gi", nvidia.com/gpu: "1" } + limits: { cpu: "2", memory: "4Gi", nvidia.com/gpu: "1" } ``` -### Apply Overlays +The `hpa-patch.yaml` in the same overlay just overrides `minReplicas`/`maxReplicas` (e.g. `1`/`2` for dev). Staging and prod use the same structure with scaled-up values: prod pins a real version tag (e.g. `newTag: v1.2.0`), sets `namespace: ml-prod`, `replicas: 3`, and larger CPU/memory requests (e.g. `cpu: "4"`, `memory: "8Gi"`) with `minReplicas` raised accordingly. ```bash -# Apply dev overlay -kubectl apply -k k8s/overlays/dev/ - -# Apply staging overlay -kubectl apply -k k8s/overlays/staging/ - -# Apply prod overlay -kubectl apply -k k8s/overlays/prod/ - -# Preview rendered manifests without applying -kubectl kustomize k8s/overlays/prod/ +kubectl apply -k k8s/overlays/dev/ # or staging/ , prod/ +kubectl kustomize k8s/overlays/prod/ # preview rendered manifests without applying ``` ## Training Jobs with Kubernetes @@ -712,35 +443,20 @@ spec: - name: trainer image: registry.example.com/ml-images/training:v1.2.0 command: ["python", "-m", "my_project.train"] - args: - - "--config=configs/train.yaml" - - "--epochs=100" - - "--batch-size=32" + args: ["--config=configs/train.yaml", "--epochs=100"] resources: - requests: - cpu: "8" - memory: "32Gi" - nvidia.com/gpu: "4" - limits: - cpu: "16" - memory: "64Gi" - nvidia.com/gpu: "4" + requests: { cpu: "8", memory: "32Gi", nvidia.com/gpu: "4" } + limits: { cpu: "16", memory: "64Gi", nvidia.com/gpu: "4" } volumeMounts: - - name: data - mountPath: /data - readOnly: true - - name: checkpoints - mountPath: /checkpoints + - { name: data, mountPath: /data, readOnly: true } + - { name: checkpoints, mountPath: /checkpoints } envFrom: - - secretRef: - name: training-secrets + - secretRef: { name: training-secrets } volumes: - name: data - persistentVolumeClaim: - claimName: training-data-pvc + persistentVolumeClaim: { claimName: training-data-pvc } - name: checkpoints - persistentVolumeClaim: - claimName: checkpoints-pvc + persistentVolumeClaim: { claimName: checkpoints-pvc } tolerations: - key: nvidia.com/gpu operator: Exists @@ -764,13 +480,4 @@ spec: ## Best Practices -1. **Always set resource requests and limits** -- GPU, CPU, and memory must all be specified for predictable scheduling. -2. **Use startup probes** -- ML containers need extended initialization time; startup probes prevent premature restarts. -3. **Separate namespaces by environment** -- dev, staging, and prod with resource quotas per namespace. -4. **Use Kustomize or Helm for environment management** -- never duplicate YAML across environments. -5. **Pin image tags** -- use semantic versions or Git SHAs, never `latest` in staging or prod. -6. **Run as non-root** -- set `securityContext.runAsNonRoot: true` on all pods. -7. **Use PVCs for model storage** -- decouple model weights from container images. -8. **Set pod disruption budgets** -- ensure minimum availability during cluster maintenance. -9. **Configure HPA with appropriate metrics** -- scale on request rate or GPU utilization, not just CPU. -10. **Use init containers for model downloads** -- pull weights from object storage before the main container starts. +The positive form of the anti-patterns above: always set GPU/CPU/memory requests and limits; use all three probe types; separate environments by namespace with resource quotas; manage env differences with Kustomize/Helm (never duplicated YAML); pin image tags; run as non-root; store model weights on PVCs (not in images); set pod disruption budgets; scale HPA on request rate or GPU utilization; and pull weights via init containers. diff --git a/skills/library-review/README.md b/skills/library-review/README.md index 70c4f6d..58a371b 100644 --- a/skills/library-review/README.md +++ b/skills/library-review/README.md @@ -23,6 +23,6 @@ The evaluation framework scores libraries across multiple dimensions: maintenanc ## Related Skills - **[Abstraction Patterns](../abstraction-patterns/)** -- implements the wrapping strategy determined by the library review process. -- **[Pydantic Strict](../pydantic-strict/)** -- typing compatibility with Pydantic models is a key evaluation criterion for new libraries. +- **[Pydantic](../pydantic/)** -- typing compatibility with Pydantic models is a key evaluation criterion for new libraries. - **[Code Quality](../code-quality/)** -- mypy compatibility and type stub availability factor heavily into the evaluation score. - **[Pixi](../pixi/)** -- accepted libraries are added to `pixi.toml` with pinned versions and documented in the lockfile. diff --git a/skills/library-review/SKILL.md b/skills/library-review/SKILL.md index 8b4dee7..15ede7d 100644 --- a/skills/library-review/SKILL.md +++ b/skills/library-review/SKILL.md @@ -1,30 +1,17 @@ --- name: library-review description: > - Structured framework for evaluating third-party Python libraries before adoption. - Covers technology radar classification (Adopt/Trial/Assess/Hold), dependency risk - assessment, API wrapping strategies, and security review checklists. + Use this skill when deciding whether to adopt a third-party Python library — evaluating + maintenance, community, docs, type-hint support, license compatibility, security + history, and performance, then classifying it Adopt/Trial/Assess/Hold. Reach for it any + time you'd otherwise add a dependency on gut feel or wonder "is this package safe and + maintained enough to depend on?", even if the user doesn't say "library review". For + designing wrappers around a library once adopted, see abstraction-patterns. --- # Library Review and Evaluation Framework -## Overview - -Adopting a new third-party library is a long-term commitment. Every dependency added to a project increases the attack surface, maintenance burden, and potential for breaking changes. This skill provides a structured framework for evaluating new libraries before adopting them, ensuring that every dependency earns its place in the project. The framework applies a technology radar approach (Adopt / Trial / Assess / Hold) combined with a wrapping strategy that isolates third-party APIs behind project-owned interfaces. - -## Why Evaluate Libraries - -The cost of a bad dependency is far higher than the cost of evaluating it upfront: - -- **Security vulnerabilities** in dependencies can compromise the entire project. -- **Abandoned libraries** stop receiving bug fixes and compatibility updates. -- **Breaking changes** in major version updates can require significant refactoring. -- **License conflicts** can create legal issues for commercial projects. -- **Performance problems** may only become apparent under production load. -- **Poor type support** undermines the project's static analysis guarantees. -- **Transitive dependencies** bring in additional risk with every library added. - -A structured evaluation process prevents these problems before they occur. +Every dependency is a long-term commitment carrying security, maintenance, breaking-change, license, and transitive-dependency risk. Evaluate new libraries before adoption using a technology radar (Adopt / Trial / Assess / Hold) plus a wrapping strategy that isolates third-party APIs behind project-owned interfaces. ## Evaluation Criteria Checklist @@ -187,7 +174,7 @@ Verify the library works with your existing tools and dependencies. | Does it conflict with existing dependencies? | Run `pip check` after install | | Does it support your OS/platform? | Check CI matrix and platform wheels | | Does it integrate with PyTorch/Lightning? | Check for official integrations | -| Can it be installed with pixi/conda? | Check conda-forge availability | +| Can it be installed with uv/conda? | Check PyPI and conda-forge availability | ```bash # Check for dependency conflicts @@ -255,27 +242,17 @@ Always wrap third-party APIs behind project-owned interfaces. This isolates the ### Why Wrap -```python -# BAD: Direct usage scattered throughout codebase -# If the library changes its API, every file must be updated -import some_library -result = some_library.process(image, mode="fast", threshold=0.5) -``` +Direct calls scattered across files (`some_library.process(...)`) mean every file +changes when the library's API changes. Wrapped behind a Protocol, only the wrapper +updates: ```python -# GOOD: Wrapped behind a project interface -# Only the wrapper needs updating if the library changes - -# src/processing/interface.py from typing import Protocol class ImageProcessor(Protocol): """Interface for image processing.""" def process(self, image: np.ndarray, threshold: float = 0.5) -> np.ndarray: ... -# src/processing/some_library_processor.py -import some_library - class SomeLibraryProcessor: """Image processor using some_library.""" @@ -335,30 +312,8 @@ class WandbTracker(ExperimentTracker): wandb.finish() -class MLflowTracker(ExperimentTracker): - """MLflow implementation of experiment tracker.""" - - def __init__(self, experiment_name: str, config: dict[str, Any]) -> None: - import mlflow - mlflow.set_experiment(experiment_name) - self.run = mlflow.start_run() - mlflow.log_params(config) - - def log_metric(self, name: str, value: float, step: int) -> None: - import mlflow - mlflow.log_metric(name, value, step=step) - - def log_params(self, params: dict[str, Any]) -> None: - import mlflow - mlflow.log_params(params) - - def log_artifact(self, path: str) -> None: - import mlflow - mlflow.log_artifact(path) - - def finish(self) -> None: - import mlflow - mlflow.end_run() +# An MLflowTracker implements the same four methods against mlflow.log_metric / +# log_params / log_artifact / end_run — swapping the backend is a wrapper-only change. class NullTracker(ExperimentTracker): @@ -419,30 +374,13 @@ Use this template when proposing a new dependency: ### Alternatives Considered | Library | Pros | Cons | |---------|------|------| -| [Alternative 1] | ... | ... | -| [Alternative 2] | ... | ... | ### Evaluation Checklist -- [ ] Maintenance: Last commit within 3 months -- [ ] Maintenance: Last release within 6 months -- [ ] Community: > 100 GitHub stars -- [ ] Community: > 5 contributors -- [ ] Documentation: API reference complete -- [ ] Documentation: Tutorials/examples available -- [ ] Types: py.typed marker or stubs available -- [ ] Types: Passes MyPy strict mode -- [ ] License: Compatible with project license -- [ ] Security: No known CVEs -- [ ] Security: pip-audit clean -- [ ] Performance: Benchmarked against alternatives -- [ ] Integration: No dependency conflicts -- [ ] Integration: Available on conda-forge +Score each criterion above (maintenance, community, docs, types, license, +security, performance, integration) green/yellow/red. ### Decision -- [ ] Adopt -- [ ] Trial -- [ ] Assess -- [ ] Hold +- [ ] Adopt - [ ] Trial - [ ] Assess - [ ] Hold ### Wrapping Plan [How will this library be wrapped behind a project interface?] @@ -510,6 +448,3 @@ pipdeptree --warn silence | grep -E "^\w" 9. **Track the Technology Radar**: Maintain a team document classifying all dependencies. 10. **Plan for migration**: Assume every library will eventually need to be replaced. -## Summary - -Library evaluation is a discipline that pays dividends over the lifetime of a project. By applying a structured checklist, using a decision framework, wrapping third-party APIs, and maintaining regular review cycles, teams avoid the common pitfalls of dependency management. The investment of an hour evaluating a library before adoption can save weeks of emergency migration when a dependency is abandoned, compromised, or introduces breaking changes. diff --git a/skills/loguru/README.md b/skills/loguru/README.md index 889c7ce..dfcd41e 100644 --- a/skills/loguru/README.md +++ b/skills/loguru/README.md @@ -27,5 +27,5 @@ This is a mandatory project convention. Every project generated by the master sk - **[Master Skill](../master-skill/)** -- project initialization includes loguru as a standard dependency and generates `setup_logging()`. - **[PyTorch Lightning](../pytorch-lightning/)** -- `InterceptHandler` routes Lightning's stdlib logging through loguru. -- **[Pydantic Strict](../pydantic-strict/)** -- `LogConfig` model validates logging configuration with frozen Pydantic models. +- **[Pydantic](../pydantic/)** -- `LogConfig` model validates logging configuration with frozen Pydantic models. - **[Hydra Config](../hydra-config/)** -- logging configuration lives in `configs/logging.yaml` and is composed into the main config. diff --git a/skills/loguru/SKILL.md b/skills/loguru/SKILL.md index c8582c0..9828cc9 100644 --- a/skills/loguru/SKILL.md +++ b/skills/loguru/SKILL.md @@ -1,9 +1,12 @@ --- name: loguru description: > - Structured logging for CV/ML projects using Loguru as a mandatory convention. - Covers sink configuration, structured JSON logging, log levels, context binding, - and replacing stdlib logging and print statements. + Use this skill when adding or standardizing logging in a CV/ML project — configuring + Loguru sinks, structured JSON logs, log levels, context binding, file rotation, and + intercepting stdlib logging. Reach for it any time code has print() statements or + stdlib logging calls that should become structured logs, or any time a new module needs + a logger, even if the user doesn't say "Loguru" — it is the mandatory logging + convention in this project. --- # Loguru Skill @@ -12,36 +15,15 @@ Structured logging for CV/ML projects using loguru. This is a mandatory project ## Why Loguru over stdlib logging -stdlib `logging` requires boilerplate: create a logger, configure handlers, set formatters, propagate correctly. Loguru replaces all of that with a single import and pre-configured defaults. +stdlib `logging` needs per-module boilerplate (create logger, configure handlers, formatters, propagation). Loguru replaces it with one import and sensible defaults: ```python -# ❌ stdlib logging — boilerplate for every module -import logging - -logger = logging.getLogger(__name__) -handler = logging.StreamHandler() -formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") -handler.setFormatter(formatter) -logger.addHandler(handler) -logger.setLevel(logging.INFO) - -logger.info("Training started") - -# ✅ loguru — one import, zero setup from loguru import logger -logger.info("Training started") +logger.info("Training started") # zero setup ``` -Key advantages for ML projects: - -- **Zero configuration** — works immediately with sensible defaults -- **Structured context binding** — attach epoch, loss, learning rate to all log messages -- **Better tracebacks** — full variable values in exception traces -- **Rotation and retention** — built-in file rotation without extra handlers -- **Serialization** — JSON output for log aggregation with one parameter -- **Thread and process safe** — correct behavior with DataLoader workers -- **Lazy evaluation** — `logger.info("Loss: {}", loss)` only formats if the level is active +Advantages that matter for ML: structured context binding (attach epoch/loss to all messages), tracebacks with full variable values, built-in rotation/retention, JSON serialization for aggregation, thread/process safety with DataLoader workers, and lazy formatting (`logger.info("Loss: {}", loss)` only formats when the level is active). ## Standard Project Setup @@ -162,7 +144,6 @@ def log_metrics(epoch: int, metrics: dict[str, float]) -> None: # Usage log_metrics(epoch=10, metrics={"loss": 0.0234, "accuracy": 0.9512, "lr": 1e-4}) -# Output: 2025-01-29 12:00:00 | INFO | train:log_metrics:12 - Epoch 10 metrics: {'loss': '0.0234', 'accuracy': '0.9512', 'lr': '0.0001'} ``` ## Log Sinks @@ -249,34 +230,9 @@ class InterceptHandler(logging.Handler): logger.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage()) -def setup_logging(level: str = "INFO", log_file: str | None = None, serialize: bool = False) -> None: - """Configure loguru and intercept stdlib logging.""" - logger.remove() - - logger.add( - sys.stderr, - level=level, - format=( - "{time:YYYY-MM-DD HH:mm:ss} | " - "{level: <8} | " - "{name}:{function}:{line} - " - "{message}" - ), - colorize=True, - ) - - if log_file: - logger.add( - log_file, - level=level, - rotation="100 MB", - retention="7 days", - compression="gz", - serialize=serialize, - ) - - # Intercept all stdlib logging - logging.basicConfig(handlers=[InterceptHandler()], level=0, force=True) +# In setup_logging(), after configuring your loguru sinks (see Standard Project +# Setup above), route all stdlib logging through loguru with one line: +# logging.basicConfig(handlers=[InterceptHandler()], level=0, force=True) ``` ### Lightning Trainer @@ -454,34 +410,12 @@ log: colorize: true ``` -```python -# Apply config -from loguru import logger - -from my_project.config import LogConfig +Then `setup_logging(config: LogConfig)` calls `logger.remove()` and adds the stderr +(and optional file) sinks using `config.level`, `config.format`, `config.rotation`, etc. +## Dependency -def setup_logging(config: LogConfig) -> None: - logger.remove() - logger.add(sys.stderr, level=config.level, format=config.format, colorize=config.colorize) - if config.log_file: - logger.add( - config.log_file, - level=config.level, - rotation=config.rotation, - retention=config.retention, - serialize=config.serialize, - ) -``` - -## Integration with pixi - -```toml -# pixi.toml — loguru is a standard project dependency -[dependencies] -python = ">=3.11" -loguru = ">=0.7" -``` +loguru is a standard project dependency: `pixi add "loguru>=0.7"`. ## Best Practices diff --git a/skills/master-skill/README.md b/skills/master-skill/README.md index 0c6206a..3cdf749 100644 --- a/skills/master-skill/README.md +++ b/skills/master-skill/README.md @@ -2,7 +2,7 @@ The Master Skill is the entry point and orchestrator for initializing new computer vision and machine learning projects. It acts as a meta-skill that coordinates the selection and composition of all other skills in the repository. Rather than generating code itself, it guides the user through a structured initialization flow -- collecting project metadata, selecting an archetype (such as classification, detection, or segmentation), choosing optional skills, and then delegating to each selected skill to scaffold the full project structure. -The Master Skill reads user inputs including project name, author information, archetype choice, and optional skill selections. Based on these inputs, it determines which skills to activate and in what order, ensuring that dependencies between skills are resolved correctly. For example, selecting the PyTorch Lightning skill automatically activates the Pydantic Strict skill since Lightning modules require Pydantic-based configurations. +The Master Skill reads user inputs including project name, author information, archetype choice, and optional skill selections. Based on these inputs, it determines which skills to activate and in what order, ensuring that dependencies between skills are resolved correctly. For example, selecting the PyTorch Lightning skill automatically activates the Pydantic skill since Lightning modules require Pydantic-based configurations. ## When to Use @@ -22,7 +22,7 @@ The Master Skill reads user inputs including project name, author information, a ## Related Skills -- **[Pydantic Strict](../pydantic-strict/)** -- enforces configuration patterns that the Master Skill uses for project metadata and skill parameters. +- **[Pydantic](../pydantic/)** -- enforces configuration patterns that the Master Skill uses for project metadata and skill parameters. - **[Pixi](../pixi/)** -- provides the environment and task runner configuration generated during project scaffolding. - **[Code Quality](../code-quality/)** -- sets up linting, formatting, and type-checking rules as part of the initial project structure. - **[GitHub Actions](../github-actions/)** -- generates CI/CD workflows when selected as an optional skill during initialization. diff --git a/skills/master-skill/SKILL.md b/skills/master-skill/SKILL.md index 043e0ec..26f8711 100644 --- a/skills/master-skill/SKILL.md +++ b/skills/master-skill/SKILL.md @@ -1,9 +1,12 @@ --- name: master-skill description: > - Orchestrates new AI/CV project initialization using the whet framework. - Guides archetype selection, skill composition, directory scaffolding, and initial - configuration for new computer vision and deep learning projects. + Use this skill when starting a brand-new AI/CV project from scratch with the whet + framework — choosing an archetype, composing which skills apply, scaffolding the + src-layout directory structure, and laying down the initial pyproject, pre-commit, + pixi, and gitignore configuration. Reach for it at the very start of a new computer + vision or deep-learning project, even if the user just says "set up a new project" or + "bootstrap a repo". Not for adding features to an existing codebase. --- # Master Skill: Project Initialization @@ -36,6 +39,7 @@ You are initializing a new AI/CV project using the whet framework. - TensorBoard - DVC (data versioning) - ONNX export + - Model evaluation (model-evaluation) 4. **Generate project using selected archetype** - Copy archetype directory structure diff --git a/skills/master-skill/skill.toml b/skills/master-skill/skill.toml index 6811d69..c6339f8 100644 --- a/skills/master-skill/skill.toml +++ b/skills/master-skill/skill.toml @@ -6,7 +6,7 @@ tags = ["conventions", "python", "coding-standards"] [dependencies] requires = [] -recommends = ["pydantic-strict", "code-quality", "loguru"] +recommends = ["pydantic", "code-quality", "loguru"] [compatibility] python = ">=3.11" diff --git a/skills/matplotlib/SKILL.md b/skills/matplotlib/SKILL.md index 8e1d0b8..f3d715b 100644 --- a/skills/matplotlib/SKILL.md +++ b/skills/matplotlib/SKILL.md @@ -1,37 +1,32 @@ --- name: matplotlib description: > - Comprehensive visualization patterns for ML and computer vision using Matplotlib. - Covers training curves, confusion matrices, image grids, bounding box overlays, - feature maps, publication-quality figures, and experiment tracker integration. + Use this skill when creating plots and figures for ML/CV work with Matplotlib — + training curves, confusion matrices, image grids, bounding-box overlays, feature-map + and t-SNE/UMAP embedding visualizations, and publication-quality figures. Reach for it + any time you'd otherwise hand-tweak matplotlib to visualize model results, metrics, or + images, even if the user just says "plot the loss" or "show these predictions". For a + live training dashboard inside a tracker, see tensorboard or wandb. --- # Matplotlib Skill -Comprehensive visualization patterns for ML and computer vision projects. This skill covers training curves, confusion matrices, image grids, bounding box overlays, feature maps, dimensionality reduction plots, publication-quality figures, and integration with experiment tracking. - -## Why Matplotlib for ML - -Matplotlib is the foundation of Python visualization. While higher-level libraries (Seaborn, Plotly) exist, matplotlib provides the control needed for: - -- Precise figure layout for papers and presentations -- Custom annotations on images (bounding boxes, masks, keypoints) -- Multi-panel figures combining plots and images -- Consistent styling across an entire project -- Non-interactive rendering for servers and CI pipelines +Visualization patterns for ML/CV: training curves, confusion matrices, image +grids, detection overlays, feature maps, embedding plots, publication figures, +and experiment-tracker logging. Use matplotlib when you need precise control over +image annotations, multi-panel figures, and non-interactive server rendering. ## Style Configuration -Set up a consistent style at the project level. Create a style file or configure in code. +Set a consistent project-wide style in code, or load a `.mplstyle` file with +`plt.style.use("path/to/custom.mplstyle")`. Use the `Agg` backend on servers. ```python import matplotlib import matplotlib.pyplot as plt -# Use non-interactive backend for servers (must be before pyplot import in scripts) -matplotlib.use("Agg") +matplotlib.use("Agg") # non-interactive; set before pyplot use in scripts -# Project-wide style plt.rcParams.update({ "figure.figsize": (10, 6), "figure.dpi": 150, @@ -49,8 +44,6 @@ plt.rcParams.update({ }) ``` -Or use a custom `.mplstyle` file: - ```ini # custom.mplstyle figure.figsize: 10, 6 @@ -63,15 +56,12 @@ grid.alpha: 0.3 lines.linewidth: 2 ``` -Load with `plt.style.use("path/to/custom.mplstyle")`. - ## Training Curve Plotting ```python from pathlib import Path import matplotlib.pyplot as plt -import numpy as np def plot_training_curves( @@ -82,19 +72,7 @@ def plot_training_curves( save_path: str | Path | None = None, title: str = "Training Progress", ) -> plt.Figure: - """Plot training and validation curves. - - Args: - train_losses: Training loss per epoch. - val_losses: Validation loss per epoch. - train_metrics: Optional dict of training metrics {name: values}. - val_metrics: Optional dict of validation metrics {name: values}. - save_path: Optional path to save the figure. - title: Figure title. - - Returns: - Matplotlib figure. - """ + """Plot loss (log scale) plus one panel per additional metric. Returns the figure.""" num_metrics = 1 + (len(train_metrics) if train_metrics else 0) fig, axes = plt.subplots(1, num_metrics, figsize=(6 * num_metrics, 5)) if num_metrics == 1: @@ -102,16 +80,14 @@ def plot_training_curves( epochs = range(1, len(train_losses) + 1) - # Loss plot axes[0].plot(epochs, train_losses, label="Train Loss", color="#2196F3") axes[0].plot(epochs, val_losses, label="Val Loss", color="#FF5722") axes[0].set_xlabel("Epoch") axes[0].set_ylabel("Loss") axes[0].set_title("Loss") axes[0].legend() - axes[0].set_yscale("log") + axes[0].set_yscale("log") # loss curves read best on log scale - # Metric plots if train_metrics and val_metrics: for i, (name, train_vals) in enumerate(train_metrics.items(), 1): val_vals = val_metrics.get(name, []) @@ -125,27 +101,14 @@ def plot_training_curves( fig.suptitle(title, fontsize=16, y=1.02) fig.tight_layout() - if save_path: fig.savefig(save_path) - return fig - - -# Usage -fig = plot_training_curves( - train_losses=[2.5, 1.8, 1.2, 0.8, 0.5, 0.3, 0.2], - val_losses=[2.6, 1.9, 1.4, 1.0, 0.7, 0.6, 0.55], - train_metrics={"Accuracy": [0.3, 0.45, 0.6, 0.72, 0.83, 0.9, 0.94]}, - val_metrics={"Accuracy": [0.28, 0.42, 0.55, 0.65, 0.75, 0.78, 0.79]}, - save_path="training_curves.png", -) ``` ## Confusion Matrix Visualization ```python -import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import confusion_matrix @@ -160,30 +123,15 @@ def plot_confusion_matrix( cmap: str = "Blues", figsize: tuple[int, int] = (10, 8), ) -> plt.Figure: - """Plot a confusion matrix with annotations. - - Args: - y_true: Ground truth labels. - y_pred: Predicted labels. - class_names: List of class name strings. - normalize: Whether to normalize by row (true class). - save_path: Optional path to save. - title: Figure title. - cmap: Colormap name. - figsize: Figure size. - - Returns: - Matplotlib figure. - """ + """Plot an annotated confusion matrix, optionally row-normalized. Returns the figure.""" cm = confusion_matrix(y_true, y_pred) if normalize: cm = cm.astype(float) / cm.sum(axis=1, keepdims=True) - cm = np.nan_to_num(cm) # Handle classes with zero samples + cm = np.nan_to_num(cm) # handle classes with zero samples fig, ax = plt.subplots(figsize=figsize) im = ax.imshow(cm, interpolation="nearest", cmap=cmap) ax.figure.colorbar(im, ax=ax) - ax.set( xticks=np.arange(len(class_names)), yticks=np.arange(len(class_names)), @@ -195,7 +143,6 @@ def plot_confusion_matrix( ) plt.setp(ax.get_xticklabels(), rotation=45, ha="right") - # Annotate cells fmt = ".2f" if normalize else "d" thresh = cm.max() / 2.0 for i in range(len(class_names)): @@ -210,26 +157,15 @@ def plot_confusion_matrix( fig.tight_layout() if save_path: fig.savefig(save_path) - return fig - - -# Usage -fig = plot_confusion_matrix( - y_true=np.array([0, 0, 1, 1, 2, 2, 0, 1, 2]), - y_pred=np.array([0, 1, 1, 1, 2, 0, 0, 1, 2]), - class_names=["Cat", "Dog", "Bird"], - save_path="confusion_matrix.png", -) ``` ## Image Grid Display -```python -import matplotlib.pyplot as plt -import numpy as np - +Works for augmentation previews, sample batches, etc. Grayscale images (2D) are +detected automatically. +```python def plot_image_grid( images: list[np.ndarray], titles: list[str] | None = None, @@ -238,19 +174,7 @@ def plot_image_grid( save_path: str | Path | None = None, suptitle: str | None = None, ) -> plt.Figure: - """Display a grid of images. - - Args: - images: List of images as numpy arrays (H, W, 3) or (H, W) for grayscale. - titles: Optional list of titles for each image. - ncols: Number of columns. - figsize_per_image: Size per image panel. - save_path: Optional path to save. - suptitle: Optional super title. - - Returns: - Matplotlib figure. - """ + """Display images (H,W,3 or H,W) in a grid. Returns the figure.""" n = len(images) nrows = (n + ncols - 1) // ncols figsize = (figsize_per_image[0] * ncols, figsize_per_image[1] * nrows) @@ -261,7 +185,6 @@ def plot_image_grid( for idx in range(nrows * ncols): row, col = divmod(idx, ncols) ax = axes[row, col] - if idx < n: cmap = "gray" if images[idx].ndim == 2 else None ax.imshow(images[idx], cmap=cmap) @@ -272,79 +195,46 @@ def plot_image_grid( if suptitle: fig.suptitle(suptitle, fontsize=16, y=1.02) fig.tight_layout() - if save_path: fig.savefig(save_path) - return fig - - -# Usage: display augmentation results -augmented = [augment(original_image) for _ in range(8)] -plot_image_grid( - augmented, - titles=[f"Aug {i}" for i in range(8)], - ncols=4, - save_path="augmentations.png", - suptitle="Augmentation Examples", -) ``` ## Bounding Box Overlay +Matplotlib overlays are better than OpenCV for notebooks and papers (crisp text, +vector output). + ```python import matplotlib.patches as patches -import matplotlib.pyplot as plt -import numpy as np def plot_detections( image: np.ndarray, - boxes: np.ndarray, + boxes: np.ndarray, # (N, 4) xyxy labels: list[str], scores: np.ndarray | None = None, class_colors: dict[str, str] | None = None, save_path: str | Path | None = None, figsize: tuple[int, int] = (12, 8), ) -> plt.Figure: - """Plot detection results using matplotlib (better for notebooks/papers than OpenCV). - - Args: - image: RGB image (H, W, 3). - boxes: Bounding boxes (N, 4) in xyxy format. - labels: List of N label strings. - scores: Optional confidence scores. - class_colors: Optional dict mapping class names to colors. - save_path: Optional save path. - figsize: Figure size. - - Returns: - Matplotlib figure. - """ + """Draw detection boxes with labels/scores over an RGB image. Returns the figure.""" fig, ax = plt.subplots(1, figsize=figsize) ax.imshow(image) - default_colors = plt.cm.tab10.colors for i, (box, label) in enumerate(zip(boxes, labels)): x1, y1, x2, y2 = box - width, height = x2 - x1, y2 - y1 - if class_colors and label in class_colors: color = class_colors[label] else: color = default_colors[hash(label) % len(default_colors)] - rect = patches.Rectangle( - (x1, y1), width, height, + ax.add_patch(patches.Rectangle( + (x1, y1), x2 - x1, y2 - y1, linewidth=2, edgecolor=color, facecolor="none", - ) - ax.add_patch(rect) - - text = label - if scores is not None: - text = f"{label} {scores[i]:.2f}" - + )) + text = f"{label} {scores[i]:.2f}" if scores is not None else label ax.text( x1, y1 - 5, text, fontsize=9, color="white", fontweight="bold", @@ -353,42 +243,27 @@ def plot_detections( ax.axis("off") fig.tight_layout() - if save_path: fig.savefig(save_path) - return fig ``` ## Feature Map Visualization ```python -import matplotlib.pyplot as plt -import numpy as np import torch def plot_feature_maps( - feature_map: torch.Tensor, + feature_map: torch.Tensor, # (C, H, W) or (1, C, H, W) num_channels: int = 16, ncols: int = 8, save_path: str | Path | None = None, title: str = "Feature Maps", ) -> plt.Figure: - """Visualize feature map channels from a CNN layer. - - Args: - feature_map: Tensor of shape (C, H, W) or (1, C, H, W). - num_channels: Number of channels to display. - ncols: Number of columns in the grid. - save_path: Optional save path. - title: Figure title. - - Returns: - Matplotlib figure. - """ + """Visualize the first N channels of a CNN feature map. Returns the figure.""" if feature_map.dim() == 4: - feature_map = feature_map[0] # Remove batch dimension + feature_map = feature_map[0] fm = feature_map.detach().cpu().numpy() num_channels = min(num_channels, fm.shape[0]) @@ -396,7 +271,6 @@ def plot_feature_maps( fig, axes = plt.subplots(nrows, ncols, figsize=(ncols * 2, nrows * 2)) axes = np.atleast_2d(axes) - for idx in range(nrows * ncols): row, col = divmod(idx, ncols) ax = axes[row, col] @@ -407,15 +281,13 @@ def plot_feature_maps( fig.suptitle(title, fontsize=14) fig.tight_layout() - if save_path: fig.savefig(save_path) - return fig -# Usage: hook into a model layer -activations = {} +# Capture activations via a forward hook, then plot: +activations: dict[str, torch.Tensor] = {} def hook_fn(name): def hook(module, input, output): @@ -427,38 +299,20 @@ model(input_batch) plot_feature_maps(activations["layer3"], save_path="feature_maps.png") ``` -## t-SNE / UMAP Plots +## t-SNE / UMAP Embedding Plots ```python -import matplotlib.pyplot as plt -import numpy as np - - def plot_embeddings( - embeddings: np.ndarray, - labels: np.ndarray, + embeddings: np.ndarray, # (N, D) + labels: np.ndarray, # (N,) class_names: list[str] | None = None, - method: str = "tsne", + method: str = "tsne", # "tsne" or "umap" save_path: str | Path | None = None, figsize: tuple[int, int] = (10, 8), title: str | None = None, perplexity: int = 30, ) -> plt.Figure: - """Visualize high-dimensional embeddings in 2D. - - Args: - embeddings: Array of shape (N, D) with feature vectors. - labels: Array of shape (N,) with integer class labels. - class_names: Optional list of class name strings. - method: 'tsne' or 'umap'. - save_path: Optional save path. - figsize: Figure size. - title: Figure title. - perplexity: t-SNE perplexity parameter. - - Returns: - Matplotlib figure. - """ + """Reduce embeddings to 2D and scatter by class. Returns the figure.""" if method == "tsne": from sklearn.manifold import TSNE reducer = TSNE(n_components=2, perplexity=perplexity, random_state=42) @@ -473,71 +327,49 @@ def plot_embeddings( fig, ax = plt.subplots(figsize=figsize) unique_labels = np.unique(labels) colors = plt.cm.tab20(np.linspace(0, 1, len(unique_labels))) - for i, label in enumerate(unique_labels): mask = labels == label name = class_names[label] if class_names else str(label) - ax.scatter( - coords[mask, 0], coords[mask, 1], - c=[colors[i]], label=name, s=10, alpha=0.7, - ) + ax.scatter(coords[mask, 0], coords[mask, 1], c=[colors[i]], label=name, s=10, alpha=0.7) ax.legend(markerscale=3, fontsize=8, loc="best") ax.set_xlabel("Component 1") ax.set_ylabel("Component 2") - if title: - ax.set_title(title) - else: - ax.set_title(f"{method.upper()} Embedding Visualization") - + ax.set_title(title or f"{method.upper()} Embedding Visualization") fig.tight_layout() if save_path: fig.savefig(save_path) - return fig ``` ## Publication-Quality Figures -```python -import matplotlib.pyplot as plt -import matplotlib - +Small serif fonts, thin lines, single-column width, PDF output. -def setup_publication_style(): +```python +def setup_publication_style() -> None: """Configure matplotlib for publication-quality figures.""" plt.rcParams.update({ - "figure.figsize": (3.5, 2.5), # Single column width + "figure.figsize": (3.5, 2.5), # single-column width "figure.dpi": 300, "savefig.dpi": 300, "savefig.format": "pdf", "savefig.bbox_inches": "tight", "savefig.pad_inches": 0.05, - "font.size": 8, "font.family": "serif", "font.serif": ["Times New Roman", "DejaVu Serif"], - "axes.titlesize": 9, "axes.labelsize": 8, "axes.linewidth": 0.5, - "xtick.labelsize": 7, "ytick.labelsize": 7, - "xtick.major.width": 0.5, - "ytick.major.width": 0.5, - "lines.linewidth": 1.0, "lines.markersize": 3, - "legend.fontsize": 7, - "legend.frameon": True, "legend.framealpha": 0.8, - "legend.edgecolor": "0.8", - "grid.linewidth": 0.3, "grid.alpha": 0.3, - "text.usetex": False, "mathtext.fontset": "dejavuserif", }) @@ -548,30 +380,17 @@ def plot_comparison_bar_chart( metrics: dict[str, list[float]], save_path: str | Path | None = None, ) -> plt.Figure: - """Bar chart comparing methods across metrics, suitable for papers. - - Args: - methods: List of method names. - metrics: Dict mapping metric names to lists of values per method. - save_path: Optional save path. - - Returns: - Matplotlib figure. - """ + """Grouped bar chart comparing methods across metrics, with value labels.""" setup_publication_style() - - n_methods = len(methods) n_metrics = len(metrics) - x = np.arange(n_methods) + x = np.arange(len(methods)) width = 0.8 / n_metrics fig, ax = plt.subplots() colors = plt.cm.Set2(np.linspace(0, 0.8, n_metrics)) - for i, (metric_name, values) in enumerate(metrics.items()): offset = (i - n_metrics / 2 + 0.5) * width bars = ax.bar(x + offset, values, width, label=metric_name, color=colors[i]) - # Add value labels on top of bars for bar, val in zip(bars, values): ax.text( bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5, @@ -583,15 +402,16 @@ def plot_comparison_bar_chart( ax.set_ylabel("Score") ax.legend() ax.set_ylim(0, 100) - fig.tight_layout() if save_path: fig.savefig(save_path) - return fig ``` -## Saving Figures in Multiple Formats +## Saving and Experiment-Tracker Integration + +Save vector (PDF/SVG) for papers and raster (PNG) for dashboards. Always +`plt.close(fig)` after logging inside a loop to free memory. ```python def save_figure_multiformat( @@ -600,76 +420,36 @@ def save_figure_multiformat( formats: list[str] = ("png", "pdf", "svg"), dpi: int = 300, ) -> list[Path]: - """Save a figure in multiple formats. - - Args: - fig: Matplotlib figure. - base_path: Path without extension. - formats: List of format strings. - dpi: Resolution for raster formats. - - Returns: - List of saved file paths. - """ + """Save a figure in multiple formats. Returns saved paths.""" base = Path(base_path) base.parent.mkdir(parents=True, exist_ok=True) saved = [] - for fmt in formats: path = base.with_suffix(f".{fmt}") fig.savefig(path, format=fmt, dpi=dpi, bbox_inches="tight") saved.append(path) - return saved -# Usage -fig = plot_training_curves(train_losses, val_losses) -save_figure_multiformat(fig, "figures/training_curves", formats=["png", "pdf", "svg"]) -``` - -## Integration with Experiment Tracking - -```python -import matplotlib.pyplot as plt -import wandb - - def log_figure_to_wandb(fig: plt.Figure, key: str, step: int | None = None) -> None: - """Log a matplotlib figure to Weights & Biases.""" + import wandb wandb.log({key: wandb.Image(fig)}, step=step) plt.close(fig) def log_figure_to_mlflow(fig: plt.Figure, artifact_path: str) -> None: - """Log a matplotlib figure to MLflow.""" import mlflow mlflow.log_figure(fig, artifact_path) plt.close(fig) - - -# Usage in training loop -for epoch in range(num_epochs): - train_loss = train_one_epoch(model, train_loader) - val_loss = evaluate(model, val_loader) - - if epoch % 10 == 0: - fig = plot_training_curves(all_train_losses, all_val_losses) - log_figure_to_wandb(fig, "training/curves", step=epoch) - - fig = plot_confusion_matrix(y_true, y_pred, class_names) - log_figure_to_wandb(fig, "eval/confusion_matrix", step=epoch) ``` ## Best Practices -1. **Always close figures** -- Call `plt.close(fig)` after saving to free memory, especially in training loops. -2. **Use the Agg backend** -- Set `matplotlib.use("Agg")` on servers to avoid display-related errors. -3. **Save as PDF for papers** -- Vector formats (PDF, SVG) scale without pixelation. -4. **Save as PNG for tracking** -- Raster formats render faster in dashboards. -5. **Use `tight_layout()`** -- Prevents labels from being cut off. -6. **Return figures** -- Functions should return `plt.Figure` so callers can further customize or save. -7. **Separate data from presentation** -- Compute metrics first, then pass arrays to plotting functions. -8. **Use consistent colors** -- Define a project color palette and use it across all plots. -9. **Label everything** -- Axes, legends, titles, and units. Unlabeled plots are useless. -10. **Use log scale for loss** -- Training loss curves are much more readable on a log scale. +1. **Always close figures** -- `plt.close(fig)` after saving in training loops to free memory. +2. **Use the Agg backend** on servers to avoid display-related errors. +3. **Save PDF/SVG for papers**, PNG for dashboards -- vector scales without pixelation. +4. **Use `tight_layout()`** to prevent labels being cut off. +5. **Return figures** (`plt.Figure`) so callers can further customize or save. +6. **Separate data from presentation** -- compute metrics first, pass arrays to plotting functions. +7. **Use consistent colors** -- define a project palette and reuse it across plots. +8. **Label everything** -- axes, legends, titles, units. Use log scale for loss curves. diff --git a/skills/mlflow/SKILL.md b/skills/mlflow/SKILL.md index 79c4709..cc3db51 100644 --- a/skills/mlflow/SKILL.md +++ b/skills/mlflow/SKILL.md @@ -1,9 +1,13 @@ --- name: mlflow description: > - MLflow integration for experiment tracking, model registry, and model serving in - ML projects. Covers self-hosted setup, metric logging, artifact management, - model versioning, and opt-in integration patterns. + Use this skill whenever the project needs experiment tracking, a model registry, or + model serving with MLflow — logging params, metrics, and artifacts, comparing runs, + versioning and promoting models through stages, and running a self-hosted tracking + server. Reach for it any time you'd otherwise hand-roll run tracking or ask "which + config produced this result?" and the stack is MLflow, even if the user doesn't say it. + For the W&B or TensorBoard alternatives see wandb and tensorboard; for versioning the + data and model files themselves see dvc. --- # MLflow Tracking Integration for ML Projects diff --git a/skills/model-evaluation/README.md b/skills/model-evaluation/README.md new file mode 100644 index 0000000..c609a76 --- /dev/null +++ b/skills/model-evaluation/README.md @@ -0,0 +1,45 @@ +# Model Evaluation Skill + +## Purpose + +This skill teaches Claude how to evaluate CV/ML models correctly — the discipline of +measuring whether a model is good enough to ship and understanding where it fails. It +covers what generic unit testing does not: task metrics, frozen eval sets, and failure +analysis. + +Detection evaluation uses **`supervision`** (`import supervision as sv`) — the +detection-native library that speaks the same `sv.Detections` object as RF-DETR, YOLO, +Ultralytics, and Transformers outputs, with metrics aligned to `pycocotools`. + +## When to Use + +- Detection metrics: mAP@[.50:.95], mAP@.50, per-class AP, per-size buckets +- Precision / Recall / F1 and confusion matrices +- Selecting a deployment confidence threshold on validation +- Per-slice / failure analysis to find where the model breaks +- Classification metrics (per-class F1, calibration/ECE) via torchmetrics +- Wiring evaluation into CI as a regression gate against a committed baseline + +## Why This Exists + +The `testing` skill checks that code runs; this skill checks that the *model* is correct +on held-out data. It also pairs with GSD's `eval-planner`: GSD designs the eval strategy, +this skill carries it out. + +## Key Patterns + +- Frozen, versioned test set; split by scene/video/match to avoid leakage +- `supervision.metrics.MeanAveragePrecision` with `.update(preds, targets).compute()` +- `sv.ConfusionMatrix.from_detections` at a real deployment threshold +- Operating-point selection on validation, reported on test +- Per-slice metrics + a curated hard-case regression set +- Evaluation-as-CI-gate against a committed baseline + +## Important Gotchas Encoded + +- The top-level `sv.MeanAveragePrecision` is **deprecated** (removed in 0.31.0) and + inconsistent with pycocotools — import from `supervision.metrics` instead. +- `MetricTarget.MASKS` is silently ignored by mAP in released versions — use + `pycocotools` for instance-segmentation mAP. +- Do not pre-filter predictions at a deploy threshold before mAP (use ~0.001). +- `-1` is an "absent" sentinel, not a score. diff --git a/skills/model-evaluation/SKILL.md b/skills/model-evaluation/SKILL.md new file mode 100644 index 0000000..4388d5a --- /dev/null +++ b/skills/model-evaluation/SKILL.md @@ -0,0 +1,272 @@ +--- +name: model-evaluation +description: > + Use this skill whenever measuring whether a CV/ML model is good enough to ship — + computing detection mAP/IoU with supervision, confusion matrices, per-class and + per-size breakdowns, precision-recall curves, calibration, deployment-threshold + selection, per-slice failure analysis, or an eval-as-CI regression gate. Reach for it + any time you would otherwise eyeball predictions or report a single accuracy number, + even if the user only says "how good is the model?". Covers frozen test sets and + leakage-free eval discipline that generic unit testing does not. Not for testing code + correctness (see testing). +--- + +# Model Evaluation + +Measuring model quality is a different discipline from unit testing. Tests check that code +runs; evaluation checks whether the *model* is good enough to ship on data it has never +seen, and *where* it fails. + +For detection work, use **`supervision`** (`import supervision as sv`) — it is the +detection-native evaluation library, speaks the same `sv.Detections` object as RF-DETR / +YOLO / Ultralytics / Transformers outputs, and its metrics are aligned with +`pycocotools` (since supervision 0.26.0). + +## Eval-set discipline (get this right first) + +A metric is only meaningful on a **frozen, held-out test set** that the model — and your +tuning decisions — never touched. + +- **Three splits.** Train (fit), validation (tune/early-stop/threshold-select), test + (report once). Never select thresholds or checkpoints on the test set. +- **Freeze the test set.** Version it (see the `dvc` skill) so numbers are comparable + across runs. A metric that moves because the test set changed is not a metric. +- **Prevent leakage.** Split by the unit that must generalize — by scene/video/match, not + by frame — so near-duplicate frames don't span train and test. +- **Report with uncertainty.** Bootstrap the test set for a confidence interval, + especially under ~2k examples. + +```python +from __future__ import annotations + +from pydantic import BaseModel, Field + + +class EvalConfig(BaseModel): + """Frozen, validated evaluation settings.""" + + test_manifest: str = Field(..., description="Versioned path to the frozen test set") + map_conf_threshold: float = 0.001 # keep near-zero for mAP (see gotchas) + deploy_conf_threshold: float = 0.30 # chosen on val, used for confusion matrix + iou_threshold: float = 0.50 + bootstrap_samples: int = 1000 +``` + +## Detection metrics with supervision + +Use the **`supervision.metrics`** module. Note: the top-level `sv.MeanAveragePrecision` +is **deprecated** (removed in 0.31.0) and produces numbers inconsistent with pycocotools — +always import from `supervision.metrics`. + +```python +import numpy as np +import supervision as sv +from loguru import logger +from supervision.metrics import MeanAveragePrecision, MetricTarget + +dataset = sv.DetectionDataset.from_coco( + images_directory_path="data/test/images", + annotations_path="data/test/_annotations.coco.json", +) + +predictions: list[sv.Detections] = [] +targets: list[sv.Detections] = [] + +# Iteration yields (image_path, image HWC uint8 BGR, annotations) +for _path, image, target in dataset: + pred = predict(image) # -> sv.Detections + # Keep the FULL score-ranked list for mAP; do not filter at a deploy threshold. + if pred.confidence is not None: + pred = pred[pred.confidence >= 0.001] + predictions.append(pred) # sv.Detections.empty() if the model found nothing + targets.append(target) + +result = MeanAveragePrecision(metric_target=MetricTarget.BOXES).update( + predictions, targets +).compute() + +print(result) # pycocotools-style 6-line summary +logger.info("mAP@50:95={:.4f} mAP@50={:.4f}", result.map50_95, result.map50) +``` + +Building `sv.Detections` — either from a model adapter or raw arrays: + +```python +# From common model outputs +det = sv.Detections.from_ultralytics(model(image)[0]) +det = sv.Detections.from_inference(model.infer(image)[0]) +det = sv.Detections.from_transformers(outputs) + +# Or directly (xyxy is ABSOLUTE pixels, not normalized) +det = sv.Detections( + xyxy=np.array([[10, 10, 110, 110]], dtype=np.float32), + confidence=np.array([0.92], dtype=np.float32), + class_id=np.array([0], dtype=int), +) +``` + +### Per-class and per-size breakdowns + +The mean hides a collapsed class and small-object failure. Always print both. +`-1` is a **sentinel for "absent"**, not a score — filter it before averaging. + +```python +for class_id, ap_row in zip(result.matched_classes, result.ap_per_class): + valid = ap_row[ap_row > -1] + ap = float(valid.mean()) if valid.size else float("nan") + logger.info("{:>14s} AP@50:95={:.4f} AP@50={:.4f}", + dataset.classes[int(class_id)], ap, ap_row[0]) + +for name, bucket in (("small", result.small_objects), + ("medium", result.medium_objects), + ("large", result.large_objects)): + if bucket is not None and bucket.map50_95 > -1: + logger.info("{:>6}: mAP@50:95={:.4f}", name, bucket.map50_95) +``` + +### Precision / Recall / F1 + +```python +from supervision.metrics import AveragingMethod, F1Score, Precision, Recall + +f1 = F1Score(averaging_method=AveragingMethod.MACRO).update(predictions, targets).compute() +p = Precision().update(predictions, targets).compute() +r = Recall().update(predictions, targets).compute() +logger.info("F1@50={:.4f} P@50={:.4f} R@50={:.4f}", + f1.f1_50, p.precision_at_50, r.recall_at_50) +``` + +### Confusion matrix + +Unlike mAP, the confusion matrix wants a **real deployment threshold**. The matrix is +`(C+1, C+1)` — the extra row/column is background (missed detections / false positives). + +```python +cm = sv.ConfusionMatrix.from_detections( + predictions=predictions, + targets=targets, + classes=dataset.classes, + conf_threshold=0.30, + iou_threshold=0.50, +) +fig = cm.plot(normalize=True, save_path="artifacts/confusion_matrix.png") +``` + +## Operating-point selection (thresholds are a decision) + +mAP integrates over all thresholds, but deployment runs at one. Sweep the threshold on the +**validation** set to hit a product requirement, then report the result on test. + +```python +def pick_threshold(preds, tgts, min_precision: float = 0.90) -> float: + """Lowest threshold meeting a precision floor -> best recall at that precision.""" + best_t, best_recall = 1.0, 0.0 + for t in np.arange(0.05, 0.95, 0.05): + filtered = [p[p.confidence >= t] if p.confidence is not None else p for p in preds] + pr = Precision().update(filtered, tgts).compute() + rc = Recall().update(filtered, tgts).compute() + if pr.precision_at_50 >= min_precision and rc.recall_at_50 > best_recall: + best_t, best_recall = float(t), rc.recall_at_50 + logger.info("deploy threshold={:.2f} (recall={:.4f})", best_t, best_recall) + return best_t +``` + +## Slice / failure analysis (the highest-value step) + +Aggregate metrics hide the failures that matter. Score the same metric across meaningful +slices (camera, lighting, occlusion, jersey color) and surface the worst. + +```python +from collections import defaultdict + + +def evaluate_by_slice(preds, tgts, slice_of) -> dict[str, float]: + """slice_of(index) -> slice key for that sample.""" + buckets: dict[str, tuple[list, list]] = defaultdict(lambda: ([], [])) + for i, (p, t) in enumerate(zip(preds, tgts)): + pb, tb = buckets[slice_of(i)] + pb.append(p) + tb.append(t) + + scores = { + key: MeanAveragePrecision().update(pb, tb).compute().map50_95 + for key, (pb, tb) in buckets.items() + } + worst = min(scores, key=scores.get) + logger.warning("worst slice: {} (mAP={:.3f})", worst, scores[worst]) + return scores +``` + +Curate a small **hard-case regression set** of known failures and track it separately — it +catches regressions the aggregate averages away. + +## Classification and segmentation + +`supervision` is detection-focused. For classification metrics use `torchmetrics`: + +```python +from torchmetrics.classification import MulticlassCalibrationError, MulticlassF1Score + +f1 = MulticlassF1Score(num_classes=NUM_CLASSES, average=None) # per-class +ece = MulticlassCalibrationError(num_classes=NUM_CLASSES, n_bins=15) # are probs trustworthy? +``` + +For **instance-segmentation mAP**, do not rely on `MetricTarget.MASKS` — see gotchas. +Use `pycocotools` (`COCOeval(..., iouType="segm")`) or `torchmetrics` with +`iou_type="segm"`. + +## Evaluation as a CI gate + +Make "did quality regress?" mechanical. Compare against a committed baseline and fail +outside the bootstrap CI. + +```python +import json +import sys + + +def gate(current_map: float, baseline_path: str, tolerance: float = 0.005) -> int: + baseline = json.load(open(baseline_path)) + drop = baseline["map50_95"] - current_map + if drop > tolerance: + logger.error("mAP regressed by {:.4f} (> {:.4f})", drop, tolerance) + return 1 + logger.info("eval gate passed (delta={:+.4f})", -drop) + return 0 + + +sys.exit(gate(result.map50_95, "eval/baseline.json")) +``` + +Log every eval run (metrics, chosen threshold, per-slice table, confusion matrix) to your +tracker via the `wandb` skill so results stay comparable over time. In headless CI set +`MPLBACKEND=Agg` — result `.plot()` calls `plt.show()`. + +## Gotchas (supervision-specific, verified against 0.29.1) + +- **`MetricTarget.MASKS` is silently ignored by `MeanAveragePrecision`.** In released + versions it always scores boxes, so segmentation mAP looks identical to box mAP and can + read 1.000 for a badly wrong mask. Use `pycocotools` for mask mAP. + (`Precision`/`Recall`/`F1Score` *do* honor masks correctly.) +- **Don't pre-filter confidence before mAP.** mAP needs the full score-ranked list; filter + at ~0.001. If `confidence is None` every prediction silently scores 0.0 and ranking is + meaningless — always set confidence on predictions. +- **`-1` means "absent", not zero.** `map50_95` masks sentinels out, but `map50`, `map75`, + and `ap_per_class` rows do not. +- **`len(predictions) != len(targets)` raises.** Append `sv.Detections.empty()` for images + with no detections rather than skipping them. +- **`class_id` is used directly as the COCO category id** — predictions and targets must + share one ID space. Use `class_mapping={...}` to remap (it applies to *both* sides). +- **`xyxy` is absolute pixels**, `x2 > x1`, `y2 > y1`. Never normalized coords. +- **Size buckets use box area** unless you pass `data["area"]`; released versions ignore + COCO `iscrowd`, so numbers can drift from pycocotools on COCO val specifically. +- **`to_pandas()` needs the extra**: install `supervision[metrics]`. + +## Anti-patterns + +- Reporting mean mAP only, hiding a collapsed class or small-object failure. +- Selecting thresholds or checkpoints on the test set (leakage inflates every number). +- Splitting by frame instead of by scene/video/match (near-duplicate leakage). +- Using the deprecated top-level `sv.MeanAveragePrecision` — inconsistent with pycocotools. +- Hand-rolling mAP/IoU instead of using supervision or pycocotools. +- Shipping without a slice/failure analysis or a frozen regression set. diff --git a/skills/model-evaluation/skill.toml b/skills/model-evaluation/skill.toml new file mode 100644 index 0000000..adbd4fb --- /dev/null +++ b/skills/model-evaluation/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "model-evaluation" +version = "1.1.0" +category = "cv-ml" +tags = ["evaluation", "metrics", "map", "detection", "supervision", "eval"] + +[dependencies] +requires = [] +recommends = ["testing", "matplotlib", "wandb", "dvc"] + +[compatibility] +python = ">=3.11" +libraries = { supervision = ">=0.26", torchmetrics = ">=1.4", pycocotools = ">=2.0" } diff --git a/skills/onnx/SKILL.md b/skills/onnx/SKILL.md index 7217147..d3bbc6e 100644 --- a/skills/onnx/SKILL.md +++ b/skills/onnx/SKILL.md @@ -1,59 +1,22 @@ --- name: onnx description: > - Export PyTorch models to ONNX format for optimized inference with ONNX Runtime. - Covers dynamic axes configuration, model optimization, graph surgery, validation - against PyTorch outputs, and production deployment patterns. + Use this skill when exporting a PyTorch model to ONNX or running inference with ONNX + Runtime — configuring dynamic axes, optimizing and slimming the graph, graph surgery, + validating ONNX outputs against PyTorch, quantization, and selecting execution + providers. Reach for it any time a model needs to leave PyTorch for portable or + optimized cross-platform inference, even if the user just says "export the model" or + "run it without torch". For squeezing maximum NVIDIA-GPU performance out of the ONNX + file, see tensorrt. --- # ONNX Model Export and Inference -## Overview - -ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models. It enables interoperability between different ML frameworks by providing a common representation. In practice, ONNX is most often used to export trained PyTorch models for optimized inference using ONNX Runtime, which delivers significant speedups over native PyTorch inference. This skill covers exporting PyTorch models, configuring dynamic axes, running inference with ONNX Runtime, optimization, validation, and deployment. - -## Why Use ONNX - -PyTorch is excellent for training but not optimized for production inference. ONNX Runtime provides: - -- **2-10x faster inference** compared to native PyTorch on the same hardware. -- **Hardware-agnostic deployment** across CPU, GPU, mobile, and edge devices. -- **Graph optimization** including operator fusion, constant folding, and memory planning. -- **Quantization support** for INT8 inference with minimal accuracy loss. -- **Framework independence**: deploy without a PyTorch dependency. -- **Standardized format** supported by major cloud providers and edge platforms. +Export trained PyTorch models to ONNX for optimized inference with ONNX Runtime (typically 2-10x faster, hardware-agnostic, no PyTorch dependency at deploy time). ## Exporting PyTorch Models to ONNX -### Basic Export - -```python -import torch -import onnx - -def export_model_basic( - model: torch.nn.Module, - output_path: str, - input_shape: tuple[int, ...] = (1, 3, 640, 640), -) -> None: - """Export a PyTorch model to ONNX format.""" - model.eval() - dummy_input = torch.randn(*input_shape) - - torch.onnx.export( - model, - dummy_input, - output_path, - opset_version=17, - input_names=["input"], - output_names=["output"], - ) - - # Validate the exported model - onnx_model = onnx.load(output_path) - onnx.checker.check_model(onnx_model) - print(f"Model exported and validated: {output_path}") -``` +Always call `model.eval()` before export, run `onnx.checker.check_model()` to validate, and use `opset_version=17`+ for modern architectures. ### Export with Pydantic Configuration @@ -86,11 +49,8 @@ def export_to_onnx( output_names=config.output_names, dynamic_axes=config.dynamic_axes, ) - # Validate - onnx_model = onnx.load(output_path) - onnx.checker.check_model(onnx_model) + onnx.checker.check_model(onnx.load(output_path)) -# Usage config = ONNXExportConfig( opset_version=17, dynamic_axes={ @@ -98,8 +58,7 @@ config = ONNXExportConfig( "output": {0: "batch_size"}, }, ) -dummy_input = torch.randn(1, 3, 640, 640) -export_to_onnx(model, dummy_input, "model.onnx", config) +export_to_onnx(model, torch.randn(1, 3, 640, 640), "model.onnx", config) ``` ## Dynamic Axes Configuration @@ -126,14 +85,6 @@ dynamic_axes = { "input": {0: "batch_size", 1: "sequence_length"}, "output": {0: "batch_size", 1: "sequence_length"}, } - -# Multiple inputs/outputs -dynamic_axes = { - "image": {0: "batch_size", 2: "height", 3: "width"}, - "mask": {0: "batch_size", 2: "height", 3: "width"}, - "boxes": {0: "batch_size", 1: "num_detections"}, - "scores": {0: "batch_size", 1: "num_detections"}, -} ``` ## Input and Output Specifications @@ -159,12 +110,7 @@ def inspect_model(model_path: str) -> None: dtype = onnx.TensorProto.DataType.Name(out.type.tensor_type.elem_type) print(f" {out.name}: {shape} ({dtype})") -# Usage inspect_model("model.onnx") -# Inputs: -# input: ['batch_size', 3, 640, 640] (FLOAT) -# Outputs: -# output: ['batch_size', 100, 6] (FLOAT) ``` ### Multiple Inputs and Outputs @@ -228,11 +174,8 @@ class ONNXInferenceSession: """Run inference returning all outputs.""" return self.session.run(None, {self.input_name: input_array}) -# Usage session = ONNXInferenceSession("model.onnx") -image = np.random.randn(1, 3, 640, 640).astype(np.float32) -output = session.predict(image) -print(f"Output shape: {output.shape}") +output = session.predict(np.random.randn(1, 3, 640, 640).astype(np.float32)) ``` ### Configuring Execution Providers @@ -293,12 +236,7 @@ session = ort.InferenceSession( ### OnnxSlim (Required) -OnnxSlim is a required post-export step. It reduces operators, removes redundant nodes, and folds constants — producing smaller, faster models without accuracy loss. Always slim after export, before quantization or deployment. - -```bash -# Install -pip install onnxslim -``` +OnnxSlim is a required post-export step: it reduces operators, removes redundant nodes, and folds constants — smaller/faster models without accuracy loss. Always slim after export, before quantization or deployment (`pixi add onnxslim`). #### Python API @@ -331,66 +269,18 @@ def export_and_slim( }, ) - # Slim the exported model - raw_model = onnx.load(raw_path) - slimmed_model = onnxslim.slim(raw_model) + slimmed_model = onnxslim.slim(onnx.load(raw_path)) onnx.save(slimmed_model, output_path) - - # Validate onnx.checker.check_model(onnx.load(output_path)) ``` -#### CLI - -```bash -# Slim a model from the command line -onnxslim model_raw.onnx model.onnx -``` - -#### Standard Export Pipeline - -The required order for ONNX export is: - -``` -1. torch.onnx.export() → raw ONNX graph -2. onnxslim.slim() → cleaned, optimized graph -3. onnx.checker.check_model() → validate -4. (optional) quantize → INT8/FP16 -5. (optional) benchmark → verify speedup -``` - -```python -import onnx -import onnxslim +CLI equivalent: `onnxslim model_raw.onnx model.onnx` -# ✅ Always slim before quantization or deployment -raw_model = onnx.load("model_raw.onnx") -slimmed = onnxslim.slim(raw_model) -onnx.save(slimmed, "model.onnx") - -# ❌ Do not deploy raw exported models — they contain redundant ops -# ❌ Do not quantize before slimming — slimming enables better quantization -``` +Required export order: `torch.onnx.export()` → `onnxslim.slim()` → `onnx.checker.check_model()` → (optional) quantize → (optional) benchmark. Never deploy raw exported models or quantize before slimming. ### ORT Graph Optimization -ONNX Runtime also performs graph optimizations at session creation. This is complementary to OnnxSlim — use both. - -```python -import onnxruntime as ort - -# Optimize and save the model -session_options = ort.SessionOptions() -session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL -session_options.optimized_model_filepath = "model_optimized.onnx" - -# Create session (triggers optimization and saves) -session = ort.InferenceSession( - "model.onnx", # Use the slimmed model - sess_options=session_options, - providers=["CPUExecutionProvider"], -) -``` +ONNX Runtime also optimizes at session creation (complementary to OnnxSlim — use both). Set `session_options.graph_optimization_level = ORT_ENABLE_ALL` and `session_options.optimized_model_filepath = "model_optimized.onnx"` to persist the optimized graph. ### Quantization @@ -436,12 +326,10 @@ quantize_static( ### FP16 Conversion ```python -from onnxconverter_common import float16 - import onnx +from onnxconverter_common import float16 -model = onnx.load("model.onnx") -model_fp16 = float16.convert_float_to_float16(model) +model_fp16 = float16.convert_float_to_float16(onnx.load("model.onnx")) onnx.save(model_fp16, "model_fp16.onnx") ``` @@ -468,35 +356,24 @@ def validate_onnx_export( input_name = session.get_inputs()[0].name all_passed = True - for i in range(num_tests): - # Generate random input + for _ in range(num_tests): dummy_input = torch.randn(*input_shape) - - # PyTorch inference with torch.no_grad(): pytorch_output = pytorch_model(dummy_input).numpy() - - # ONNX Runtime inference onnx_output = session.run(None, {input_name: dummy_input.numpy()})[0] - - # Compare try: np.testing.assert_allclose(pytorch_output, onnx_output, atol=atol, rtol=rtol) - print(f"Test {i + 1}: PASSED (max diff: {np.max(np.abs(pytorch_output - onnx_output)):.2e})") - except AssertionError as e: - print(f"Test {i + 1}: FAILED - {e}") + except AssertionError: all_passed = False return all_passed -# Usage -is_valid = validate_onnx_export(model, "model.onnx") -assert is_valid, "ONNX validation failed" +assert validate_onnx_export(model, "model.onnx"), "ONNX validation failed" ``` ## Serving with ONNX Runtime and FastAPI -### FastAPI Integration +Load the session once in a `lifespan` handler; keep it in module scope for reuse across requests. ```python from contextlib import asynccontextmanager @@ -592,115 +469,25 @@ def benchmark_onnx( times.append((time.perf_counter() - start) * 1000) return { - "mean_ms": np.mean(times), - "std_ms": np.std(times), - "min_ms": np.min(times), - "max_ms": np.max(times), - "median_ms": np.median(times), - "p95_ms": np.percentile(times, 95), - "p99_ms": np.percentile(times, 99), - "throughput_fps": 1000.0 / np.mean(times), + "mean_ms": float(np.mean(times)), + "median_ms": float(np.median(times)), + "p95_ms": float(np.percentile(times, 95)), + "p99_ms": float(np.percentile(times, 99)), + "throughput_fps": 1000.0 / float(np.mean(times)), } -# Usage results = benchmark_onnx("model.onnx", (1, 3, 640, 640)) -for metric, value in results.items(): - print(f"{metric}: {value:.2f}") ``` -### Comparing PyTorch vs ONNX Performance - -```python -import torch -import time -import numpy as np - -def benchmark_pytorch(model, input_shape, num_runs=100): - model.eval() - device = next(model.parameters()).device - dummy = torch.randn(*input_shape, device=device) - - # Warmup - with torch.no_grad(): - for _ in range(10): - model(dummy) - - # Benchmark - times = [] - with torch.no_grad(): - for _ in range(num_runs): - start = time.perf_counter() - model(dummy) - if device.type == "cuda": - torch.cuda.synchronize() - times.append((time.perf_counter() - start) * 1000) - - return {"mean_ms": np.mean(times), "throughput_fps": 1000 / np.mean(times)} - -# Compare -pytorch_results = benchmark_pytorch(model, (1, 3, 640, 640)) -onnx_results = benchmark_onnx("model.onnx", (1, 3, 640, 640)) -speedup = pytorch_results["mean_ms"] / onnx_results["mean_ms"] -print(f"ONNX speedup: {speedup:.2f}x") -``` +To compute the ONNX speedup, run the same warmup/timed loop against the PyTorch model (calling `torch.cuda.synchronize()` after each forward pass on GPU) and divide the mean latencies. ## Common Pitfalls -### 1. Missing Dynamic Axes - -Without dynamic axes, the model only accepts the exact input shape used during export: - -```python -# BAD: Fixed batch size -torch.onnx.export(model, dummy, "model.onnx") - -# GOOD: Dynamic batch size -torch.onnx.export(model, dummy, "model.onnx", - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}) -``` - -### 2. Unsupported Operations - -Some PyTorch operations are not supported in ONNX. Common workarounds: - -```python -# BAD: torch.where with scalar -output = torch.where(condition, 1.0, 0.0) - -# GOOD: Use tensors -output = torch.where(condition, torch.ones_like(x), torch.zeros_like(x)) -``` - -### 3. Data Type Mismatch - -ONNX Runtime expects `float32` by default: - -```python -# BAD: Wrong dtype -input_array = image.astype(np.float64) - -# GOOD: Correct dtype -input_array = image.astype(np.float32) -``` - -### 4. Not Calling model.eval() - -Training-mode behaviors (dropout, batch norm) produce different results: - -```python -# ALWAYS set eval mode before export -model.eval() -torch.onnx.export(model, dummy, "model.onnx") -``` - -### 5. Opset Version Too Low - -Newer operations require higher opset versions: - -```python -# Use opset 17+ for modern architectures -torch.onnx.export(model, dummy, "model.onnx", opset_version=17) -``` +- **Missing dynamic axes** — without them the model only accepts the exact export shape; always pass `dynamic_axes` for batch size at minimum. +- **Unsupported ops** — some PyTorch ops don't export. Prefer tensor forms, e.g. `torch.where(cond, torch.ones_like(x), torch.zeros_like(x))` over scalar args. +- **Data type mismatch** — ONNX Runtime expects `float32`; cast inputs with `.astype(np.float32)`, not `float64`. +- **Forgetting `model.eval()`** — training-mode dropout/batchnorm produce different outputs; always call before export. +- **Opset too low** — use `opset_version=17`+ for modern architectures. ## Best Practices @@ -714,7 +501,3 @@ torch.onnx.export(model, dummy, "model.onnx", opset_version=17) 8. **Profile with ONNX Runtime** profiling tools to find bottlenecks. 9. **Wrap ONNX inference** in a Pydantic-validated class for type safety. 10. **Store ONNX models** as DVC-tracked artifacts, not in Git. - -## Summary - -ONNX provides a standardized path from PyTorch training to optimized production inference. By exporting models to ONNX format and running them with ONNX Runtime, projects achieve significant speedups with minimal code changes. The combination of graph optimization, quantization, and hardware-specific execution providers makes ONNX Runtime the recommended inference engine for deploying computer vision models in production. diff --git a/skills/opencv/SKILL.md b/skills/opencv/SKILL.md index 8811b2d..fbbec6b 100644 --- a/skills/opencv/SKILL.md +++ b/skills/opencv/SKILL.md @@ -1,32 +1,27 @@ --- name: opencv description: > - Type-safe OpenCV abstractions for computer vision projects. Covers video I/O, - image processing wrappers, drawing utilities, color space conversions, camera - capture, and clean API design over OpenCV's C-style interface. + Use this skill when doing image or video processing with OpenCV — reading and writing + video, camera capture, color-space conversions, drawing overlays, NumPy/PyTorch + conversions, and building type-safe wrappers over OpenCV's C-style cv2 API. Reach for + it any time you'd otherwise call cv2 directly for I/O or preprocessing, even if the user + doesn't say "OpenCV" and just mentions frames, webcam, or resizing images. For plotting + results see matplotlib; for scoring model quality see model-evaluation. --- # OpenCV Skill -Comprehensive OpenCV abstractions for computer vision projects. This skill provides clean, type-safe wrappers around OpenCV's C-style API, covering video reading/writing, image abstractions, drawing utilities, color space conversions, and camera capture. - -## Why Abstractions Over Raw OpenCV - -OpenCV's Python API exposes the underlying C++ interface almost directly. While powerful, this leads to code that is error-prone and hard to maintain: - -- Functions return magic integers (e.g., `cv2.CAP_PROP_FRAME_WIDTH`) instead of named properties -- Color channels are BGR by default, which surprises developers and causes subtle bugs -- No type hints on function signatures -- Resource management (releasing cameras, closing video writers) is manual -- Error handling is inconsistent (some functions return None, others raise) - -The abstractions in this skill wrap OpenCV with Pythonic interfaces that are type-safe, context-managed, and consistent. +Clean, type-safe wrappers around OpenCV's C-style API: video reading/writing, +image abstractions, drawing utilities, color-space conversions, and camera +capture. OpenCV's Python API exposes the C++ interface almost directly — magic +integer properties, BGR-by-default channels, no type hints, and manual resource +management. These abstractions fix that with Pythonic, context-managed, type-safe +interfaces. ## VideoReader Abstraction -Define an abstract base class for video reading so you can swap implementations (OpenCV, FFmpeg, hardware-accelerated) without changing application code. - -### Abstract Interface +Define an ABC so you can swap implementations (OpenCV, FFmpeg, hardware decoders) +without changing application code. ```python from abc import ABC, abstractmethod @@ -39,7 +34,6 @@ import numpy as np @dataclass(frozen=True) class VideoMetadata: - """Immutable video metadata.""" width: int height: int fps: float @@ -53,8 +47,6 @@ class VideoMetadata: class VideoReaderBase(ABC): - """Abstract base class for video reading.""" - @abstractmethod def __init__(self, source: str | Path) -> None: ... @@ -64,15 +56,11 @@ class VideoReaderBase(ABC): ... @abstractmethod - def seek(self, frame_number: int) -> None: - """Seek to a specific frame number.""" - ... + def seek(self, frame_number: int) -> None: ... - @abstractmethod @property - def metadata(self) -> VideoMetadata: - """Return video metadata.""" - ... + @abstractmethod + def metadata(self) -> VideoMetadata: ... @abstractmethod def __enter__(self) -> "VideoReaderBase": ... @@ -81,7 +69,6 @@ class VideoReaderBase(ABC): def __exit__(self, *args) -> None: ... def __iter__(self) -> Iterator[np.ndarray]: - """Iterate over all frames.""" while True: frame = self.read_frame() if frame is None: @@ -89,7 +76,6 @@ class VideoReaderBase(ABC): yield frame def read_frames(self, start: int = 0, count: int | None = None) -> list[np.ndarray]: - """Read a range of frames.""" self.seek(start) frames = [] for frame in self: @@ -101,15 +87,14 @@ class VideoReaderBase(ABC): ### OpenCV Implementation +Reads frames as RGB (converting from OpenCV's native BGR), validates the source, +and releases the capture on exit. + ```python import cv2 -import numpy as np -from pathlib import Path class OpenCVVideoReader(VideoReaderBase): - """OpenCV-based video reader with context management.""" - def __init__(self, source: str | Path) -> None: self._path = Path(source) if not self._path.exists(): @@ -119,23 +104,19 @@ class OpenCVVideoReader(VideoReaderBase): if not self._cap.isOpened(): raise RuntimeError(f"Failed to open video: {self._path}") + fps = self._cap.get(cv2.CAP_PROP_FPS) + frame_count = int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT)) self._metadata = VideoMetadata( width=int(self._cap.get(cv2.CAP_PROP_FRAME_WIDTH)), height=int(self._cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), - fps=self._cap.get(cv2.CAP_PROP_FPS), - frame_count=int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT)), - duration_seconds=( - int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT)) - / max(self._cap.get(cv2.CAP_PROP_FPS), 1e-6) - ), - codec=self._decode_fourcc( - int(self._cap.get(cv2.CAP_PROP_FOURCC)) - ), + fps=fps, + frame_count=frame_count, + duration_seconds=frame_count / max(fps, 1e-6), + codec=self._decode_fourcc(int(self._cap.get(cv2.CAP_PROP_FOURCC))), ) @staticmethod def _decode_fourcc(fourcc: int) -> str: - """Decode FourCC integer to string.""" return "".join(chr((fourcc >> (8 * i)) & 0xFF) for i in range(4)) @property @@ -150,9 +131,7 @@ class OpenCVVideoReader(VideoReaderBase): def seek(self, frame_number: int) -> None: if frame_number < 0 or frame_number >= self._metadata.frame_count: - raise ValueError( - f"Frame {frame_number} out of range [0, {self._metadata.frame_count})" - ) + raise ValueError(f"Frame {frame_number} out of range [0, {self._metadata.frame_count})") self._cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number) def __enter__(self) -> "OpenCVVideoReader": @@ -166,28 +145,16 @@ class OpenCVVideoReader(VideoReaderBase): self._cap.release() ``` -Usage: - -```python -with OpenCVVideoReader("video.mp4") as reader: - print(f"Resolution: {reader.metadata.resolution}") - print(f"Duration: {reader.metadata.duration_seconds:.1f}s") - - for i, frame in enumerate(reader): - # frame is RGB numpy array (H, W, 3) - process_frame(frame) - if i >= 100: - break -``` - ## Image Class Abstraction -Wrap numpy arrays with metadata and safe conversion methods. +Wraps a numpy array with explicit color-space tracking, validated construction, +and safe conversion methods — eliminating silent BGR/RGB bugs. ```python from __future__ import annotations from enum import Enum +from pathlib import Path from typing import Self import cv2 @@ -203,7 +170,7 @@ class ColorSpace(Enum): class Image: - """Type-safe image wrapper with color space tracking.""" + """Type-safe image wrapper with color-space tracking.""" def __init__(self, data: np.ndarray, color_space: ColorSpace = ColorSpace.RGB) -> None: if data.ndim not in (2, 3): @@ -212,23 +179,20 @@ class Image: raise ValueError(f"Expected 1, 3, or 4 channels, got {data.shape[2]}") if data.ndim == 2 and color_space != ColorSpace.GRAY: raise ValueError("2D array must use GRAY color space") - self._data = data self._color_space = color_space @classmethod def from_file(cls, path: str | Path, color_space: ColorSpace = ColorSpace.RGB) -> Self: - """Load image from file.""" img = cv2.imread(str(path), cv2.IMREAD_COLOR) if img is None: raise FileNotFoundError(f"Failed to load image: {path}") if color_space == ColorSpace.RGB: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - return cls(img, color_space=color_space if color_space != ColorSpace.RGB else ColorSpace.RGB) + return cls(img, color_space=color_space) @classmethod def from_numpy(cls, array: np.ndarray, color_space: ColorSpace = ColorSpace.RGB) -> Self: - """Create Image from numpy array.""" return cls(array.copy(), color_space=color_space) @property @@ -251,19 +215,9 @@ class Image: def channels(self) -> int: return self._data.shape[2] if self._data.ndim == 3 else 1 - @property - def shape(self) -> tuple[int, int, int]: - return (self.height, self.width, self.channels) - - @property - def dtype(self) -> np.dtype: - return self._data.dtype - def to_color_space(self, target: ColorSpace) -> Image: - """Convert to a different color space.""" if target == self._color_space: return self - conversion_map = { (ColorSpace.RGB, ColorSpace.BGR): cv2.COLOR_RGB2BGR, (ColorSpace.BGR, ColorSpace.RGB): cv2.COLOR_BGR2RGB, @@ -276,46 +230,41 @@ class Image: (ColorSpace.RGB, ColorSpace.LAB): cv2.COLOR_RGB2LAB, (ColorSpace.LAB, ColorSpace.RGB): cv2.COLOR_LAB2RGB, } - key = (self._color_space, target) if key not in conversion_map: raise ValueError(f"Unsupported conversion: {self._color_space} -> {target}") - - converted = cv2.cvtColor(self._data, conversion_map[key]) - return Image(converted, color_space=target) + return Image(cv2.cvtColor(self._data, conversion_map[key]), color_space=target) def resize(self, width: int, height: int, interpolation: int = cv2.INTER_LINEAR) -> Image: - """Resize image.""" - resized = cv2.resize(self._data, (width, height), interpolation=interpolation) - return Image(resized, color_space=self._color_space) + return Image(cv2.resize(self._data, (width, height), interpolation=interpolation), + color_space=self._color_space) def to_float32(self) -> Image: - """Convert to float32 [0, 1] range.""" + """Convert to float32 in [0, 1].""" if self._data.dtype == np.float32: return self return Image(self._data.astype(np.float32) / 255.0, color_space=self._color_space) def to_uint8(self) -> Image: - """Convert to uint8 [0, 255] range.""" + """Convert to uint8 in [0, 255].""" if self._data.dtype == np.uint8: return self return Image((self._data * 255).clip(0, 255).astype(np.uint8), color_space=self._color_space) def to_tensor(self) -> "torch.Tensor": - """Convert to PyTorch tensor (C, H, W) in float32.""" + """Convert to a PyTorch (C, H, W) float32 tensor.""" import torch img = self.to_float32().to_color_space(ColorSpace.RGB) return torch.from_numpy(img.data.transpose(2, 0, 1)) def save(self, path: str | Path) -> None: - """Save image to file.""" - bgr = self.to_color_space(ColorSpace.BGR) - cv2.imwrite(str(path), bgr.data) + cv2.imwrite(str(path), self.to_color_space(ColorSpace.BGR).data) ``` ## Drawing Utilities -Clean functions for drawing annotations on images. +Named colors with automatic BGR conversion, plus box/keypoint/mask drawers. All +drawing functions `.copy()` the input since OpenCV mutates in place. ```python from dataclasses import dataclass @@ -326,7 +275,6 @@ import numpy as np @dataclass class Color: - """RGB color.""" r: int g: int b: int @@ -340,7 +288,6 @@ class Color: return (self.r, self.g, self.b) -# Predefined colors class Colors: RED = Color(255, 0, 0) GREEN = Color(0, 255, 0) @@ -350,173 +297,96 @@ class Colors: MAGENTA = Color(255, 0, 255) WHITE = Color(255, 255, 255) BLACK = Color(0, 0, 0) - PALETTE = [RED, GREEN, BLUE, YELLOW, CYAN, MAGENTA] @classmethod def for_class(cls, class_id: int) -> Color: - """Get a consistent color for a class ID.""" + """Consistent color per class ID.""" return cls.PALETTE[class_id % len(cls.PALETTE)] def draw_bounding_box( image: np.ndarray, - box: tuple[int, int, int, int], + box: tuple[int, int, int, int], # (x1, y1, x2, y2) label: str = "", color: Color = Colors.GREEN, thickness: int = 2, font_scale: float = 0.6, ) -> np.ndarray: - """Draw a bounding box with optional label on an image. - - Args: - image: Image array (H, W, 3) in BGR format for OpenCV. - box: Bounding box as (x1, y1, x2, y2). - label: Text label to display above the box. - color: Box and label color. - thickness: Line thickness. - font_scale: Font scale for the label. - - Returns: - Image with drawn bounding box. - """ + """Draw a box with an optional filled label banner. Expects BGR input.""" img = image.copy() - x1, y1, x2, y2 = [int(v) for v in box] - + x1, y1, x2, y2 = (int(v) for v in box) cv2.rectangle(img, (x1, y1), (x2, y2), color.bgr, thickness) - if label: - (text_w, text_h), baseline = cv2.getTextSize( - label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, 1 - ) - cv2.rectangle( - img, (x1, y1 - text_h - baseline - 4), (x1 + text_w, y1), color.bgr, -1 - ) - cv2.putText( - img, label, (x1, y1 - baseline - 2), - cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), 1, - ) - + (tw, th), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, 1) + cv2.rectangle(img, (x1, y1 - th - baseline - 4), (x1 + tw, y1), color.bgr, -1) + cv2.putText(img, label, (x1, y1 - baseline - 2), + cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), 1) return img def draw_detections( image: np.ndarray, - boxes: np.ndarray, + boxes: np.ndarray, # (N, 4) xyxy labels: list[str], scores: np.ndarray | None = None, class_ids: np.ndarray | None = None, thickness: int = 2, ) -> np.ndarray: - """Draw multiple detection results on an image. - - Args: - image: Image array (H, W, 3). - boxes: Array of shape (N, 4) in xyxy format. - labels: List of N label strings. - scores: Optional array of N confidence scores. - class_ids: Optional array of N class IDs for color assignment. - thickness: Line thickness. - - Returns: - Annotated image. - """ + """Draw multiple boxes, coloring by class ID and appending scores to labels.""" img = image.copy() for i in range(len(boxes)): color = Colors.for_class(class_ids[i] if class_ids is not None else i) - text = labels[i] - if scores is not None: - text = f"{text} {scores[i]:.2f}" + text = f"{labels[i]} {scores[i]:.2f}" if scores is not None else labels[i] img = draw_bounding_box(img, tuple(boxes[i]), label=text, color=color, thickness=thickness) return img def draw_keypoints( image: np.ndarray, - keypoints: np.ndarray, + keypoints: np.ndarray, # (N, 2) or (N, 3) with confidence skeleton: list[tuple[int, int]] | None = None, color: Color = Colors.GREEN, radius: int = 4, thickness: int = 2, ) -> np.ndarray: - """Draw keypoints and optional skeleton connections. - - Args: - image: Image array (H, W, 3). - keypoints: Array of shape (N, 2) or (N, 3) with (x, y) or (x, y, confidence). - skeleton: List of (start_idx, end_idx) pairs defining connections. - color: Point color. - radius: Point radius. - thickness: Skeleton line thickness. - - Returns: - Image with keypoints drawn. - """ + """Draw keypoints and optional skeleton lines (drawn first, behind points).""" img = image.copy() - - # Draw skeleton connections first (behind points) if skeleton is not None: for start, end in skeleton: pt1 = tuple(keypoints[start, :2].astype(int)) pt2 = tuple(keypoints[end, :2].astype(int)) cv2.line(img, pt1, pt2, color.bgr, thickness) - - # Draw keypoints for kp in keypoints: - x, y = int(kp[0]), int(kp[1]) conf = kp[2] if len(kp) > 2 else 1.0 if conf > 0.5: - cv2.circle(img, (x, y), radius, color.bgr, -1) - + cv2.circle(img, (int(kp[0]), int(kp[1])), radius, color.bgr, -1) return img def draw_mask_overlay( image: np.ndarray, - mask: np.ndarray, + mask: np.ndarray, # binary (H, W), values 0/1 color: Color = Colors.GREEN, alpha: float = 0.4, ) -> np.ndarray: - """Overlay a binary mask on an image with transparency. - - Args: - image: Image array (H, W, 3). - mask: Binary mask (H, W) with values 0 or 1. - color: Overlay color. - alpha: Transparency (0 = invisible, 1 = opaque). - - Returns: - Image with mask overlay. - """ + """Alpha-blend a binary mask over the image.""" img = image.copy() overlay = img.copy() overlay[mask.astype(bool)] = color.bgr return cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0) ``` -## Camera Capture Abstraction - -```python -from contextlib import contextmanager - -import cv2 -import numpy as np +## Camera Capture +Context-managed capture with configurable resolution/FPS; iterating yields frames +until a read fails. +```python class Camera: - """Context-managed camera capture.""" - - def __init__( - self, - device_id: int = 0, - width: int = 640, - height: int = 480, - fps: int = 30, - ) -> None: + def __init__(self, device_id: int = 0, width: int = 640, height: int = 480, fps: int = 30) -> None: self._device_id = device_id - self._width = width - self._height = height - self._fps = fps + self._width, self._height, self._fps = width, height, fps self._cap: cv2.VideoCapture | None = None def open(self) -> None: @@ -528,9 +398,8 @@ class Camera: self._cap.set(cv2.CAP_PROP_FPS, self._fps) def read(self) -> np.ndarray: - """Read a frame. Raises RuntimeError on failure.""" if self._cap is None: - raise RuntimeError("Camera not opened. Use 'with' statement or call open().") + raise RuntimeError("Camera not opened. Use 'with' or call open().") ret, frame = self._cap.read() if not ret: raise RuntimeError("Failed to read frame from camera") @@ -549,7 +418,6 @@ class Camera: self.close() def __iter__(self): - """Yield frames continuously.""" while True: try: yield self.read() @@ -557,20 +425,13 @@ class Camera: break ``` -## Video Writer Abstraction +## Video Writer + +Context-managed writer expecting BGR frames. ```python class VideoWriter: - """Context-managed video writer.""" - - def __init__( - self, - path: str | Path, - fps: float, - width: int, - height: int, - codec: str = "mp4v", - ) -> None: + def __init__(self, path: str | Path, fps: float, width: int, height: int, codec: str = "mp4v") -> None: self._path = Path(path) self._path.parent.mkdir(parents=True, exist_ok=True) fourcc = cv2.VideoWriter_fourcc(*codec) @@ -580,7 +441,6 @@ class VideoWriter: self._frame_count = 0 def write(self, frame: np.ndarray) -> None: - """Write a frame (expects BGR format).""" self._writer.write(frame) self._frame_count += 1 @@ -596,51 +456,47 @@ class VideoWriter: def __exit__(self, *args) -> None: self.close() -``` -Usage combining reader and writer: -```python +# Read RGB, process, convert back to BGR to write: with OpenCVVideoReader("input.mp4") as reader: meta = reader.metadata with VideoWriter("output.mp4", meta.fps, meta.width, meta.height) as writer: for frame in reader: processed = process_frame(frame) - # Convert RGB back to BGR for writer writer.write(cv2.cvtColor(processed, cv2.COLOR_RGB2BGR)) ``` -## Integration with NumPy and PyTorch +## NumPy / PyTorch Conversions ```python -import numpy as np import torch def numpy_to_torch(image: np.ndarray) -> torch.Tensor: - """Convert HWC uint8 numpy array to CHW float32 tensor.""" + """HWC uint8 -> CHW float32 tensor.""" if image.dtype == np.uint8: image = image.astype(np.float32) / 255.0 return torch.from_numpy(image.transpose(2, 0, 1)) def torch_to_numpy(tensor: torch.Tensor) -> np.ndarray: - """Convert CHW float32 tensor to HWC uint8 numpy array.""" + """CHW float32 tensor -> HWC uint8.""" arr = tensor.detach().cpu().numpy().transpose(1, 2, 0) return (arr * 255).clip(0, 255).astype(np.uint8) def batch_to_numpy(batch: torch.Tensor) -> list[np.ndarray]: - """Convert BCHW tensor batch to list of HWC numpy arrays.""" + """BCHW tensor -> list of HWC uint8 arrays.""" return [torch_to_numpy(batch[i]) for i in range(batch.shape[0])] ``` ## Best Practices -1. **Always track color space** -- Use the `Image` class or explicit naming (`frame_rgb`, `frame_bgr`) to avoid silent BGR/RGB confusion. -2. **Use context managers** -- Always wrap `VideoCapture` and `VideoWriter` in `with` blocks to ensure resources are released. -3. **Convert to RGB early** -- Convert from BGR to RGB immediately after reading; convert back to BGR only when writing or displaying with OpenCV. -4. **Abstract over backends** -- Define interfaces (ABCs) so you can swap OpenCV for FFmpeg, Decord, or hardware decoders without changing application code. -5. **Validate inputs** -- Check that images have the expected dtype, shape, and value range before processing. -6. **Copy before mutating** -- OpenCV drawing functions modify arrays in place. Always `.copy()` if the original should be preserved. -7. **Use named constants** -- Define color palettes and codec strings as module-level constants, not magic values scattered through code. +1. **Always track color space** -- use the `Image` class or explicit naming (`frame_rgb`, `frame_bgr`) to avoid silent BGR/RGB confusion. +2. **Use context managers** -- wrap `VideoCapture`/`VideoWriter` in `with` blocks to guarantee release. +3. **Convert to RGB early** -- convert BGR->RGB right after reading; convert back only when writing or displaying with OpenCV. +4. **Abstract over backends** -- define ABCs so OpenCV can be swapped for FFmpeg, Decord, or hardware decoders. +5. **Validate inputs** -- check dtype, shape, and value range before processing. +6. **Copy before mutating** -- OpenCV drawing functions modify arrays in place. +7. **Use named constants** -- define color palettes and codec strings as module-level constants, not magic values. diff --git a/skills/pixi/SKILL.md b/skills/pixi/SKILL.md index 6c50fca..1667360 100644 --- a/skills/pixi/SKILL.md +++ b/skills/pixi/SKILL.md @@ -1,9 +1,12 @@ --- name: pixi description: > - Manage Python project environments and dependencies using Pixi. Covers pixi.toml - configuration, conda/PyPI dependency management, task definitions, lock files, - cross-platform environments, and CUDA toolkit setup. + Use this skill when managing a project's environment and dependencies with Pixi — + authoring pixi.toml, mixing conda and PyPI dependencies, defining tasks, lock files, + cross-platform environments, and CUDA toolkit setup. Reach for it any time you'd + otherwise reach for conda, pip, or venv to set up or update a CV/ML environment, even + if the user just says "add this dependency" or "set up the environment". For building + and publishing the package to PyPI, see pypi. --- # Pixi Skill diff --git a/skills/pre-commit/SKILL.md b/skills/pre-commit/SKILL.md index b507706..3913e62 100644 --- a/skills/pre-commit/SKILL.md +++ b/skills/pre-commit/SKILL.md @@ -1,9 +1,12 @@ --- name: pre-commit description: > - Configure pre-commit hooks for Python and ML projects to enforce code quality - at commit time. Covers Ruff, MyPy, YAML validation, large file prevention, - secret detection, and CI integration with pre-commit.ci. + Use this skill when setting up git pre-commit hooks to enforce quality at commit time — + authoring .pre-commit-config.yaml with Ruff, MyPy, YAML validation, large-file blocking, + secret detection, and pre-commit.ci integration. Reach for it any time you want checks + to run automatically on every commit, even if the user just says "add commit hooks" or + "stop bad commits". This skill owns the hook wiring; the Ruff/MyPy standards themselves + live in code-quality and the equivalent server-side checks in github-actions. --- # Pre-commit Hooks for Python and ML Projects diff --git a/skills/pydantic-ai/README.md b/skills/pydantic-ai/README.md new file mode 100644 index 0000000..8c6d381 --- /dev/null +++ b/skills/pydantic-ai/README.md @@ -0,0 +1,28 @@ +# PydanticAI Skill + +## Purpose + +This skill teaches Claude how to build type-safe LLM and VLM pipelines with PydanticAI — +getting **validated, structured results** from models instead of parsing free text by +hand. The flagship use case is VLM-in-the-loop labeling: send an image to Gemini or a +local Moondream/DINO model and get back a validated set of detections or attributes ready +to feed a training pipeline. + +## When to Use + +- Any time the project calls an LLM or VLM and needs a typed, validated result +- Structured extraction / auto-labeling of images +- JSON outputs, tool / function calling, or an agent loop +- Replacing hand-written parsing of a model's free-text response + +## Key Patterns + +- `Agent` with an `output_type` Pydantic model — schema is enforced, not hoped for +- Image inputs via `BinaryContent` / `ImageUrl` +- Typed dependency injection (`deps_type`) for clients and thresholds +- `output_validator` + `ModelRetry` for self-correcting, bounded retries +- Provider-agnostic model config (Gemini, Anthropic, OpenAI, local OpenAI-compatible) +- `TestModel` + `ALLOW_MODEL_REQUESTS = False` for hermetic tests + +Pairs with `pydantic` (strict result models) and `model-evaluation` (scoring the +labels the VLM produces). diff --git a/skills/pydantic-ai/SKILL.md b/skills/pydantic-ai/SKILL.md new file mode 100644 index 0000000..f41f1f0 --- /dev/null +++ b/skills/pydantic-ai/SKILL.md @@ -0,0 +1,206 @@ +--- +name: pydantic-ai +description: > + Use this skill whenever the project calls an LLM or VLM and needs a typed, validated + result — structured extraction, auto-labeling images with Gemini/Moondream, JSON + outputs, tool/function calling, or an agent loop. Reach for it any time you would + otherwise parse a model's free-text response by hand. Builds type-safe LLM pipelines + with PydanticAI: Agent, result_type, dependency injection, tools, retries, and + provider-agnostic model config (Gemini, Anthropic, OpenAI, local). +--- + +# PydanticAI + +PydanticAI brings the Pydantic "parse, don't validate" discipline to LLM/VLM calls: you +declare the result you want as a Pydantic model, and the framework enforces it — with +automatic retries when the model returns something off-schema. Use it instead of prompting +for JSON and hand-parsing the string, which is where CV/ML pipelines silently break. + +The canonical use here is **VLM-in-the-loop labeling**: send an image to Gemini or a local +Moondream/DINO model and get back a *validated* set of detections or attributes, ready to +feed a training pipeline. + +## The core: a typed Agent + +```python +from __future__ import annotations + +from loguru import logger +from pydantic import BaseModel, Field +from pydantic_ai import Agent + + +class Detection(BaseModel): + label: str + confidence: float = Field(ge=0.0, le=1.0) + bbox_xyxy: tuple[float, float, float, float] + + +class ImageAnnotation(BaseModel): + """The exact shape the pipeline needs back — nothing else is accepted.""" + + detections: list[Detection] + scene: str + is_occluded: bool + + +annotator = Agent( + "google-gla:gemini-2.0-flash", + output_type=ImageAnnotation, + system_prompt=( + "You are a precise vision annotator. Return every visible object with a tight " + "bounding box in pixel xyxy. Do not invent objects." + ), +) + +result = annotator.run_sync("Annotate this frame.") # + image input, see below +logger.info("got {} detections", len(result.output.detections)) +annotation: ImageAnnotation = result.output # already validated +``` + +`result.output` is a validated `ImageAnnotation`. If the model returns bad JSON or a +confidence of `1.4`, PydanticAI raises and retries against the schema instead of handing +you garbage. + +## Sending images (VLM inputs) + +```python +from pathlib import Path + +from pydantic_ai import BinaryContent, ImageUrl + +# local file (e.g. a training frame) +result = annotator.run_sync([ + "Annotate this frame.", + BinaryContent(data=Path("frame.jpg").read_bytes(), media_type="image/jpeg"), +]) + +# or a remote asset +result = annotator.run_sync(["Annotate this.", ImageUrl(url="https://.../frame.jpg")]) +``` + +## Dependency injection (typed context) + +Pass typed runtime dependencies (clients, thresholds, the current dataset) into the agent +and its tools — no globals. + +```python +from dataclasses import dataclass + +from pydantic_ai import Agent, RunContext + + +@dataclass +class Deps: + min_confidence: float + class_whitelist: set[str] + + +agent = Agent("google-gla:gemini-2.0-flash", deps_type=Deps, output_type=ImageAnnotation) + + +@agent.system_prompt +def constrain(ctx: RunContext[Deps]) -> str: + allowed = ", ".join(sorted(ctx.deps.class_whitelist)) + return f"Only label these classes: {allowed}. Drop boxes below {ctx.deps.min_confidence}." + + +result = agent.run_sync("Annotate.", deps=Deps(min_confidence=0.5, class_whitelist={"person", "ball"})) +``` + +## Tools (function calling) + +Register Python functions the model may call; return values are typed and validated. + +```python +@agent.tool +def lookup_class_id(ctx: RunContext[Deps], name: str) -> int: + """Resolve a class name to the dataset's integer id.""" + return DATASET_CLASSES.index(name) +``` + +## Validation retries and self-correction + +Raise `ModelRetry` from a tool or output validator to bounce a bad response back to the +model with a reason. Cap attempts so a stubborn model fails loudly. + +```python +from pydantic_ai import Agent, ModelRetry + +agent = Agent("google-gla:gemini-2.0-flash", output_type=ImageAnnotation, retries=2) + + +@agent.output_validator +def boxes_in_bounds(result: ImageAnnotation) -> ImageAnnotation: + for d in result.detections: + x1, y1, x2, y2 = d.bbox_xyxy + if x2 <= x1 or y2 <= y1: + raise ModelRetry(f"Degenerate box for {d.label}: {d.bbox_xyxy}") + return result +``` + +## Provider-agnostic model config + +The model is a string or an object — swap providers without touching pipeline code. + +```python +# strings: ":" +Agent("google-gla:gemini-2.0-flash") # Gemini (AI Studio) +Agent("anthropic:claude-sonnet-4-5") # Claude +Agent("openai:gpt-4o") # OpenAI + +# a local/OpenAI-compatible endpoint (e.g. a served Moondream/vLLM) +from pydantic_ai.models.openai import OpenAIModel +from pydantic_ai.providers.openai import OpenAIProvider + +local = OpenAIModel("moondream2", provider=OpenAIProvider(base_url="http://localhost:8000/v1")) +Agent(local, output_type=ImageAnnotation) +``` + +## Batch labeling a dataset + +```python +def label_dataset(paths: list[Path], deps: Deps) -> list[ImageAnnotation]: + out: list[ImageAnnotation] = [] + for p in paths: + try: + res = agent.run_sync(["Annotate.", BinaryContent(p.read_bytes(), "image/jpeg")], deps=deps) + out.append(res.output) + except Exception as exc: # noqa: BLE001 — log and skip, never crash the whole run + logger.warning("skipped {}: {}", p.name, exc) + return out +``` + +## Testing (no live model calls) + +Use `TestModel` / `FunctionModel` and `Agent.override` so unit tests never hit a provider. + +```python +from pydantic_ai import models +from pydantic_ai.models.test import TestModel + +models.ALLOW_MODEL_REQUESTS = False # fail if any test accidentally calls a real model + +def test_annotator_parses() -> None: + with agent.override(model=TestModel()): + result = agent.run_sync("Annotate.") + assert isinstance(result.output, ImageAnnotation) +``` + +## Conventions + +- **Always set `output_type`** to a Pydantic model — never parse free text. +- **Keep result models strict** (bounded floats, enums for classes) so the retry loop + catches hallucinated values; pairs with the `pydantic` skill. +- **Inject clients/thresholds via `deps_type`**, not module globals. +- **Cap `retries`** and log-and-skip per item in batch jobs so one bad frame can't kill a + dataset pass. +- **Gate tests** with `ALLOW_MODEL_REQUESTS = False` and `TestModel`. + +## Anti-patterns + +- Prompting for JSON and `json.loads`-ing the reply — no schema enforcement, silent drift. +- Putting the API client in a global — untestable, unswappable. +- Unbounded retries — a stubborn model spins cost; cap and fail. +- Trusting VLM confidences/boxes unchecked — validate ranges and geometry in an + `output_validator`. diff --git a/skills/pydantic-ai/skill.toml b/skills/pydantic-ai/skill.toml new file mode 100644 index 0000000..bca7110 --- /dev/null +++ b/skills/pydantic-ai/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "pydantic-ai" +version = "1.0.0" +category = "cv-ml" +tags = ["llm", "vlm", "structured-output", "agents", "pydantic", "gemini", "labeling"] + +[dependencies] +requires = [] +recommends = ["pydantic", "model-evaluation", "loguru"] + +[compatibility] +python = ">=3.11" +libraries = { pydantic-ai = ">=0.0.14" } diff --git a/skills/pydantic-strict/README.md b/skills/pydantic/README.md similarity index 77% rename from skills/pydantic-strict/README.md rename to skills/pydantic/README.md index c30ac62..e5c8789 100644 --- a/skills/pydantic-strict/README.md +++ b/skills/pydantic/README.md @@ -1,6 +1,6 @@ -# Pydantic Strict Skill +# Pydantic Skill -The Pydantic Strict Skill enforces disciplined use of Pydantic V2 across the entire codebase, operating at two distinct levels. Level 1 requires that all configuration objects -- model hyperparameters, data pipeline settings, training arguments, deployment parameters -- inherit from `BaseModel` with strict validation, frozen immutability, and explicit field definitions. Level 2 extends this discipline to internal data structures such as prediction results, evaluation metrics, and API request/response schemas. Together, these levels ensure that no unvalidated dictionary or loosely-typed dataclass sneaks into the project. +The Pydantic Skill enforces disciplined use of Pydantic V2 across the entire codebase, operating at two distinct levels. Level 1 requires that all configuration objects -- model hyperparameters, data pipeline settings, training arguments, deployment parameters -- inherit from `BaseModel` with strict validation, frozen immutability, and explicit field definitions. Level 2 extends this discipline to internal data structures such as prediction results, evaluation metrics, and API request/response schemas. Together, these levels ensure that no unvalidated dictionary or loosely-typed dataclass sneaks into the project. This skill covers the full surface area of Pydantic V2: `Field` definitions with constraints, custom validators using `field_validator` and `model_validator` decorators, frozen configurations that prevent accidental mutation, nested model composition, discriminated unions for polymorphic configs, and selective use of `validate_call` on functions where runtime argument validation adds genuine safety. It explicitly discourages overuse of `validate_call` on internal functions where static type checking suffices. diff --git a/skills/pydantic-strict/SKILL.md b/skills/pydantic/SKILL.md similarity index 75% rename from skills/pydantic-strict/SKILL.md rename to skills/pydantic/SKILL.md index 5dbb92c..fe28d80 100644 --- a/skills/pydantic-strict/SKILL.md +++ b/skills/pydantic/SKILL.md @@ -1,18 +1,18 @@ --- -name: pydantic-strict +name: pydantic description: > - Configuration and data validation using Pydantic V2 with strict typing for AI/CV - projects. Covers frozen models, discriminated unions, custom validators, settings - management, and load-time validation patterns. + Use this skill when validating configuration or data structures with Pydantic V2 — + frozen/immutable config models, discriminated unions, custom validators, settings + management, and strict load-time validation. Reach for it any time you'd otherwise pass + around raw dicts or plain dataclasses for config or structured data, even if the user + doesn't say "Pydantic". This is for plain data and config validation; for validating an + LLM's output and building agents see pydantic-ai, and for Hydra config composition see + hydra-config. --- -# Pydantic Strict Skill +# Pydantic Skill -You are writing configuration and data validation code using Pydantic V2 with strict typing. Follow these patterns exactly. - -## Core Philosophy - -All configuration in AI/CV projects must be validated at load time, not at runtime deep inside training loops. Pydantic V2 provides three levels of strictness depending on the use case. Every config, every data structure, every API payload uses Pydantic. +Configuration and data validation with Pydantic V2 and strict typing. Validate all config at load time, never inside training loops. Every config, data structure, and API payload uses Pydantic. ## Level 1: Immutable Configuration (Frozen Models) @@ -29,15 +29,7 @@ from pydantic import BaseModel, Field, field_validator class DataConfig(BaseModel): - """Configuration for data loading and preprocessing. - - Attributes: - data_dir: Path to the dataset root directory. - batch_size: Number of samples per batch. - num_workers: Number of dataloader worker processes. - image_size: Target image dimensions (height, width). - pin_memory: Whether to pin memory for GPU transfer. - """ + """Configuration for data loading and preprocessing.""" model_config = {"frozen": True, "extra": "forbid"} @@ -71,14 +63,7 @@ class DataConfig(BaseModel): class ModelConfig(BaseModel): - """Configuration for model architecture. - - Attributes: - backbone: Name of the backbone network. - num_classes: Number of output classes. - pretrained: Whether to use pretrained weights. - dropout: Dropout rate for regularization. - """ + """Configuration for model architecture.""" model_config = {"frozen": True, "extra": "forbid"} @@ -89,14 +74,7 @@ class ModelConfig(BaseModel): class OptimizerConfig(BaseModel): - """Configuration for the optimizer. - - Attributes: - name: Optimizer name (adam, adamw, sgd). - learning_rate: Initial learning rate. - weight_decay: L2 regularization factor. - momentum: Momentum for SGD optimizer. - """ + """Configuration for the optimizer.""" model_config = {"frozen": True, "extra": "forbid"} @@ -156,16 +134,7 @@ from pydantic import BaseModel, Field class TrainingMetrics(BaseModel): - """Mutable metrics tracked during training. - - Attributes: - epoch: Current epoch number. - train_loss: Running training loss. - val_loss: Running validation loss. - best_val_loss: Best validation loss seen so far. - learning_rate: Current learning rate. - samples_processed: Total number of samples processed. - """ + """Mutable metrics tracked during training.""" model_config = {"extra": "forbid"} @@ -187,14 +156,7 @@ class TrainingMetrics(BaseModel): class DetectionResult(BaseModel): - """Single object detection result. - - Attributes: - class_id: Predicted class index. - class_name: Human-readable class name. - confidence: Detection confidence score. - bbox: Bounding box as (x1, y1, x2, y2). - """ + """Single object detection result. bbox is (x1, y1, x2, y2).""" model_config = {"extra": "forbid"} @@ -215,14 +177,7 @@ class DetectionResult(BaseModel): class FrameDetections(BaseModel): - """All detections in a single frame. - - Attributes: - frame_id: Frame index in the video. - timestamp: Frame timestamp in seconds. - detections: List of detection results. - processing_time_ms: Time to process this frame. - """ + """All detections in a single frame.""" model_config = {"extra": "forbid"} @@ -264,18 +219,7 @@ from pydantic import BaseModel, Field, model_validator class AugmentationConfig(BaseModel): - """Augmentation pipeline configuration. - - Supports loading from either a dict or a path to a YAML file. - If a string path is provided, it loads and parses the YAML file. - - Attributes: - horizontal_flip_prob: Probability of horizontal flip. - vertical_flip_prob: Probability of vertical flip. - rotation_limit: Maximum rotation angle in degrees. - brightness_limit: Maximum brightness adjustment. - contrast_limit: Maximum contrast adjustment. - """ + """Augmentation config. Loads from a dict or a path to a YAML file.""" model_config = {"frozen": True, "extra": "forbid"} @@ -302,15 +246,7 @@ class AugmentationConfig(BaseModel): class ExperimentConfig(BaseModel): - """Experiment configuration with automatic defaults based on task. - - Attributes: - task: Type of CV task. - backbone: Model backbone. - image_size: Input image dimensions. - batch_size: Training batch size. - augmentation: Augmentation config (or path to YAML file). - """ + """Experiment configuration with automatic defaults based on task.""" model_config = {"frozen": True, "extra": "forbid"} @@ -431,17 +367,11 @@ class FullConfig(BaseModel): ### Usage Example ```python -# Load from YAML file +# Load from YAML, optionally with dot-notation overrides config = FullConfig.from_yaml("configs/experiment/baseline.yaml") - -# Load with command-line overrides config = FullConfig.from_yaml_with_overrides( "configs/experiment/baseline.yaml", - overrides={ - "optimizer.lr": 5e-4, - "data.batch_size": 64, - "max_epochs": 200, - }, + overrides={"optimizer.lr": 5e-4, "data.batch_size": 64, "max_epochs": 200}, ) # Access nested values with full type safety @@ -449,67 +379,8 @@ print(config.optimizer.learning_rate) # float print(config.data.image_size) # tuple[int, int] ``` -## Config File Loading Patterns - -### YAML Config File - -```yaml -# configs/experiment/baseline.yaml -data: - data_dir: /data/imagenet - batch_size: 32 - num_workers: 8 - image_size: [224, 224] - -model: - backbone: resnet50 - num_classes: 1000 - pretrained: true - -optimizer: - name: adamw - lr: 0.001 - weight_decay: 0.01 - -scheduler: - name: cosine - warmup_epochs: 5 - min_lr: 0.000001 - -max_epochs: 100 -seed: 42 -``` - -### Environment-Specific Overrides - -```python -"""Load config with environment-specific overrides.""" - -from __future__ import annotations - -import os -from pathlib import Path - - -def load_config(base_path: str = "configs/experiment/baseline.yaml") -> FullConfig: - """Load config with environment-appropriate defaults.""" - env = os.getenv("ENVIRONMENT", "development") - - config = FullConfig.from_yaml(base_path) - - # Override for CI environments - if env == "ci": - config = FullConfig.from_yaml_with_overrides( - base_path, - overrides={ - "data.num_workers": 2, - "data.batch_size": 4, - "max_epochs": 1, - }, - ) - - return config -``` +The matching YAML has top-level `data`, `model`, `optimizer`, `scheduler` keys +mirroring the sub-config field names (use `lr` alias under `optimizer`). ## Testing Pydantic Configs diff --git a/skills/pydantic-strict/skill.toml b/skills/pydantic/skill.toml similarity index 90% rename from skills/pydantic-strict/skill.toml rename to skills/pydantic/skill.toml index 24ef7a3..c58a585 100644 --- a/skills/pydantic-strict/skill.toml +++ b/skills/pydantic/skill.toml @@ -1,5 +1,5 @@ [skill] -name = "pydantic-strict" +name = "pydantic" version = "1.0.0" category = "core" tags = ["validation", "config", "pydantic", "data-models"] diff --git a/skills/pypi/SKILL.md b/skills/pypi/SKILL.md index 7e8f819..78f034c 100644 --- a/skills/pypi/SKILL.md +++ b/skills/pypi/SKILL.md @@ -1,50 +1,32 @@ --- name: pypi description: > - Complete workflow for packaging and publishing Python projects to PyPI. Covers - pyproject.toml configuration, src layout, version management, build system setup, - trusted publishers via GitHub Actions, and TestPyPI staging. + Use this skill when packaging and publishing a Python project to PyPI — configuring + pyproject.toml build metadata, src layout, version management, the hatchling or + setuptools build backend, entry points, trusted publishing via GitHub Actions, and + TestPyPI staging. Reach for it any time you'd otherwise figure out how to build a wheel + and upload a release, even if the user just says "publish this package" or "make it + pip-installable". For managing the local dev environment and dependencies, see pixi. --- # PyPI Publishing Skill -Complete workflow for packaging and publishing Python projects to PyPI. This skill covers `pyproject.toml` configuration, version management, build system setup, src layout, package metadata, entry points, dependency specification, building, publishing to PyPI and TestPyPI, trusted publishers via GitHub Actions, and release automation. - -## Why Publish to PyPI - -Publishing to PyPI makes your library installable with `pip install your-package` from anywhere. This is essential for: - -- Sharing CV/ML utilities across projects and teams -- Distributing trained model inference wrappers -- Providing CLI tools for data processing pipelines -- Building an open-source community around your work -- Ensuring reproducible installations with pinned versions +Packaging and publishing Python projects to PyPI: `pyproject.toml` configuration, src layout, version management, building, TestPyPI staging, trusted publishers via GitHub Actions, and release automation. ## Project Layout -Use the `src` layout. It prevents accidental imports from the working directory (a common source of "works on my machine" bugs) and is the recommended standard. +Use the `src` layout — it prevents accidental imports from the working directory (a common "works on my machine" bug) and is the recommended standard. ``` my-cv-package/ -├── src/ -│ └── my_cv_package/ -│ ├── __init__.py -│ ├── models/ -│ │ ├── __init__.py -│ │ ├── detector.py -│ │ └── classifier.py -│ ├── data/ -│ │ ├── __init__.py -│ │ └── transforms.py -│ ├── utils/ -│ │ ├── __init__.py -│ │ ├── io.py -│ │ └── visualization.py -│ └── cli.py +├── src/my_cv_package/ +│ ├── __init__.py +│ ├── py.typed # PEP 561 marker +│ ├── models/ # detector.py, classifier.py +│ ├── data/ # transforms.py +│ ├── utils/ # io.py, visualization.py +│ └── cli.py ├── tests/ -│ ├── conftest.py -│ ├── test_detector.py -│ └── test_transforms.py ├── pyproject.toml ├── LICENSE └── README.md @@ -52,9 +34,9 @@ my-cv-package/ ## pyproject.toml Configuration -### Using Hatchling (Recommended) +### Hatchling (recommended) -Hatchling is a modern, fast build backend with good defaults. +Modern, fast build backend with good defaults. ```toml [build-system] @@ -68,24 +50,17 @@ description = "Computer vision utilities for object detection and classification readme = "README.md" license = {text = "MIT"} requires-python = ">=3.11" -authors = [ - {name = "Your Name", email = "you@example.com"}, -] +authors = [{name = "Your Name", email = "you@example.com"}] keywords = ["computer-vision", "deep-learning", "object-detection"] classifiers = [ "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Scientific/Engineering :: Image Recognition", "Typing :: Typed", ] - dependencies = [ "numpy>=1.24", "opencv-python-headless>=4.8", @@ -94,18 +69,8 @@ dependencies = [ ] [project.optional-dependencies] -dev = [ - "pytest>=7.0", - "pytest-cov>=4.0", - "ruff>=0.4", - "mypy>=1.0", - "pre-commit>=3.0", -] -docs = [ - "mkdocs>=1.5", - "mkdocs-material>=9.0", - "mkdocstrings[python]>=0.24", -] +dev = ["pytest>=7.0", "pytest-cov>=4.0", "ruff>=0.4", "mypy>=1.0", "pre-commit>=3.0"] +docs = ["mkdocs>=1.5", "mkdocs-material>=9.0", "mkdocstrings[python]>=0.24"] all = ["my-cv-package[dev,docs]"] [project.urls] @@ -113,19 +78,14 @@ Homepage = "https://github.com/yourname/my-cv-package" Documentation = "https://yourname.github.io/my-cv-package" Repository = "https://github.com/yourname/my-cv-package" Issues = "https://github.com/yourname/my-cv-package/issues" -Changelog = "https://github.com/yourname/my-cv-package/blob/main/CHANGELOG.md" [project.scripts] my-cv-tool = "my_cv_package.cli:main" - -[project.entry-points."my_cv_package.models"] -resnet = "my_cv_package.models.classifier:ResNetClassifier" -yolo = "my_cv_package.models.detector:YOLODetector" ``` -### Using Setuptools +### Setuptools (alternative) -If you prefer setuptools as the build backend: +Swap the build backend and declare package discovery. Combine with `setuptools-scm` for git-tag versioning: ```toml [build-system] @@ -135,14 +95,6 @@ build-backend = "setuptools.build_meta" [project] name = "my-cv-package" dynamic = ["version"] -description = "Computer vision utilities" -readme = "README.md" -license = {text = "MIT"} -requires-python = ">=3.11" -dependencies = [ - "numpy>=1.24", - "opencv-python-headless>=4.8", -] [tool.setuptools.packages.find] where = ["src"] @@ -153,50 +105,25 @@ write_to = "src/my_cv_package/_version.py" ## Version Management -### Manual Versioning +Three approaches — pick one: -Keep the version in `pyproject.toml` and `__init__.py`: +**Manual** — keep `version = "0.1.0"` in `pyproject.toml`; read it at runtime from installed metadata so `__init__.py` never drifts: ```python # src/my_cv_package/__init__.py -"""My CV Package.""" - -__version__ = "0.1.0" -``` - -### Dynamic Versioning with setuptools-scm - -Derive version from git tags automatically: - -```toml -[build-system] -requires = ["setuptools>=68.0", "setuptools-scm>=8.0"] -build-backend = "setuptools.build_meta" - -[project] -dynamic = ["version"] - -[tool.setuptools_scm] -write_to = "src/my_cv_package/_version.py" -``` - -Then in `__init__.py`: +from importlib.metadata import version -```python -try: - from my_cv_package._version import version as __version__ -except ImportError: - __version__ = "0.0.0-dev" +__version__ = version("my-cv-package") ``` -Tag a release: +**Git-tag derived (setuptools-scm)** — set `dynamic = ["version"]` plus `[tool.setuptools_scm]` (see above), then tag: ```bash git tag -a v0.1.0 -m "Release version 0.1.0" git push origin v0.1.0 ``` -### Dynamic Versioning with Hatch +**Git-tag derived (hatch-vcs)** — the Hatchling equivalent: ```toml [build-system] @@ -215,15 +142,12 @@ version-file = "src/my_cv_package/_version.py" ## Entry Points -### CLI Entry Points - -Define command-line scripts that are installed with the package: +**CLI scripts** — installed as commands on the user's PATH: ```toml [project.scripts] my-cv-detect = "my_cv_package.cli:detect_main" my-cv-train = "my_cv_package.cli:train_main" -my-cv-export = "my_cv_package.cli:export_main" ``` ```python @@ -232,31 +156,17 @@ import argparse def detect_main() -> None: - """CLI entry point for detection.""" parser = argparse.ArgumentParser(description="Run object detection") parser.add_argument("input", help="Input image or video path") - parser.add_argument("--model", default="yolov8n", help="Model name") - parser.add_argument("--confidence", type=float, default=0.5, help="Confidence threshold") - parser.add_argument("--output", help="Output path") + parser.add_argument("--model", default="yolov8n") + parser.add_argument("--confidence", type=float, default=0.5) args = parser.parse_args() from my_cv_package.models.detector import detect - detect(args.input, model=args.model, confidence=args.confidence, output=args.output) - - -def train_main() -> None: - """CLI entry point for training.""" - ... - - -def export_main() -> None: - """CLI entry point for model export.""" - ... + detect(args.input, model=args.model, confidence=args.confidence) ``` -### Plugin Entry Points - -Allow third-party extensions to register models: +**Plugin entry points** — let third parties register implementations you discover at runtime: ```toml [project.entry-points."my_cv_package.models"] @@ -264,107 +174,55 @@ resnet = "my_cv_package.models.classifier:ResNetClassifier" yolo = "my_cv_package.models.detector:YOLODetector" ``` -Discover plugins at runtime: - ```python from importlib.metadata import entry_points def get_available_models() -> dict[str, type]: - """Discover all registered model plugins.""" eps = entry_points(group="my_cv_package.models") return {ep.name: ep.load() for ep in eps} ``` -## Building Packages - -```bash -# Install build tool -pip install build - -# Build source distribution and wheel -python -m build - -# Output in dist/ -# dist/my_cv_package-0.1.0.tar.gz (sdist) -# dist/my_cv_package-0.1.0-py3-none-any.whl (wheel) - -# Inspect the wheel contents -unzip -l dist/my_cv_package-0.1.0-py3-none-any.whl -``` - -## Publishing to TestPyPI - -Always test on TestPyPI first before publishing to the real PyPI. +## Building and Publishing ```bash -# Install twine -pip install twine +python -m build # produces dist/*.tar.gz (sdist) + dist/*.whl (wheel) +unzip -l dist/*.whl # inspect wheel contents -# Upload to TestPyPI +# Stage on TestPyPI first, then install to verify twine upload --repository testpypi dist/* - -# Test installation from TestPyPI pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - my-cv-package -``` - -## Publishing to PyPI + --extra-index-url https://pypi.org/simple/ my-cv-package -```bash -# Upload to PyPI (requires API token) +# Publish to the real index (API token via -u __token__ -p, or trusted publishing below) twine upload dist/* - -# Or with explicit token -twine upload -u __token__ -p pypi-YOUR-TOKEN-HERE dist/* ``` ## Trusted Publisher (GitHub Actions) -The recommended approach is to use PyPI trusted publishers. This eliminates the need for API tokens by using OpenID Connect (OIDC) to authenticate GitHub Actions directly with PyPI. - -### Setup on PyPI - -1. Go to your PyPI project settings -2. Add a new trusted publisher -3. Enter your GitHub repository owner, name, workflow filename, and environment name - -### Release Workflow +Trusted publishers use OIDC to authenticate GitHub Actions to PyPI — no API tokens to manage. Register the repo/workflow/environment under your PyPI project settings, then use a tag-triggered workflow. Build once, fan out to test-install, TestPyPI, then PyPI: ```yaml # .github/workflows/release.yml name: Release to PyPI - on: push: - tags: - - "v*" - + tags: ["v*"] permissions: contents: write - id-token: write # Required for trusted publishing - + id-token: write # required for trusted publishing jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: - fetch-depth: 0 # Needed for setuptools-scm - + fetch-depth: 0 # for setuptools-scm - uses: actions/setup-python@v5 with: python-version: "3.12" - - - name: Install build dependencies - run: pip install build - - - name: Build package - run: python -m build - - - name: Upload build artifacts - uses: actions/upload-artifact@v4 + - run: pip install build && python -m build + - uses: actions/upload-artifact@v4 with: name: dist path: dist/ @@ -379,105 +237,43 @@ jobs: - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - - - name: Download artifacts - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v4 with: name: dist path: dist/ - - - name: Install wheel - run: pip install dist/*.whl - - - name: Test import - run: python -c "import my_cv_package; print(my_cv_package.__version__)" - - publish-testpypi: - needs: test-install - runs-on: ubuntu-latest - environment: testpypi - steps: - - name: Download artifacts - uses: actions/download-artifact@v4 - with: - name: dist - path: dist/ - - - name: Publish to TestPyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - repository-url: https://test.pypi.org/legacy/ + - run: pip install dist/*.whl + - run: python -c "import my_cv_package; print(my_cv_package.__version__)" publish-pypi: - needs: publish-testpypi + needs: test-install runs-on: ubuntu-latest environment: pypi steps: - - name: Download artifacts - uses: actions/download-artifact@v4 - with: - name: dist - path: dist/ - - - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - - github-release: - needs: publish-pypi - runs-on: ubuntu-latest - permissions: - contents: write - steps: - - uses: actions/checkout@v4 - - - name: Download artifacts - uses: actions/download-artifact@v4 + - uses: actions/download-artifact@v4 with: name: dist path: dist/ - - - name: Create GitHub Release - uses: softprops/action-gh-release@v2 - with: - files: dist/* - generate_release_notes: true + - uses: pypa/gh-action-pypi-publish@release/v1 ``` -## Semantic Release - -Python Semantic Release automates version bumps, changelogs, and publishing based on commit messages. It eliminates manual versioning and the release checklist below. +Add a `publish-testpypi` job (same shape, `repository-url: https://test.pypi.org/legacy/`, `environment: testpypi`) before `publish-pypi` to stage, and a `github-release` job using `softprops/action-gh-release@v2` with `generate_release_notes: true` after it. -### Conventional Commits - -Semantic release requires [Conventional Commits](https://www.conventionalcommits.org/) format: - -``` -feat: add YOLO v8 model loader → bumps minor (0.1.0 → 0.2.0) -fix: correct NMS threshold handling → bumps patch (0.2.0 → 0.2.1) -feat!: redesign detection pipeline API → bumps major (0.2.1 → 1.0.0) - -fix(data): handle empty annotation files -feat(models): add EfficientNet backbone -docs: update training guide -chore: update CI workflow -``` +## Semantic Release -Only `feat` and `fix` (and breaking changes with `!` or `BREAKING CHANGE` footer) trigger releases. `docs`, `chore`, `ci`, `refactor`, `test`, and `style` do not. +Python Semantic Release automates version bumps, changelogs, and publishing from commit messages — no manual versioning. -### pyproject.toml Configuration +Uses [Conventional Commits](https://www.conventionalcommits.org/): `feat:` bumps minor, `fix:` bumps patch, `feat!:` (or a `BREAKING CHANGE` footer) bumps major. `docs`, `chore`, `ci`, `refactor`, `test`, `style` do **not** trigger a release. ```toml [tool.semantic_release] version_toml = ["pyproject.toml:project.version"] -branch = "main" build_command = "pip install build && python -m build" upload_to_pypi = true upload_to_release = true -commit_message = "chore(release): v{version} [skip ci]" +commit_message = "chore(release): v{version} [skip ci]" # [skip ci] avoids CI loops tag_format = "v{version}" [tool.semantic_release.changelog] -template_dir = "templates" changelog_file = "CHANGELOG.md" [tool.semantic_release.branches.main] @@ -486,131 +282,57 @@ match = "main" [tool.semantic_release.branches.develop] match = "develop" prerelease = true -prerelease_token = "dev" -``` - -Key settings: - -- `version_toml` -- points to the version field in `pyproject.toml` (replaces deprecated `version_variable`) -- `branch = "main"` -- production releases from `main` -- `branches.develop` -- prereleases (`0.3.0-dev.1`) from `develop` -- `build_command` -- runs before publishing; use your project's build backend -- `commit_message` -- includes `[skip ci]` to avoid infinite CI loops - -### Using Flit as Build Backend - -If your project uses Flit instead of Hatchling: - -```toml -[build-system] -requires = ["flit_core>=3.4"] -build-backend = "flit_core.wheel" - -[tool.semantic_release] -version_toml = ["pyproject.toml:project.version"] -build_command = "pip install flit && flit build" -upload_to_pypi = true -upload_to_release = true -``` - -### GitHub Actions Workflow - -See the **GitHub Actions** skill for the full CI workflow that runs semantic release on push to `main` or `develop`. - -```yaml -# .github/workflows/release.yml — summary -# Runs after checks pass, on push to main or develop -# Uses python-semantic-release/python-semantic-release@v9.15.1 -# Publishes to PyPI via trusted publishers (OIDC) -# Creates GitHub releases with changelogs -``` - -### Version in __init__.py - -Semantic release updates `pyproject.toml` automatically. Keep `__init__.py` in sync: - -```python -# src/my_cv_package/__init__.py -"""My CV Package.""" - -from importlib.metadata import version - -__version__ = version("my-cv-package") +prerelease_token = "dev" # cuts 0.3.0-dev.1 prereleases ``` -This reads the version from the installed package metadata at runtime, so it always matches `pyproject.toml` without needing a separate update step. +Notes: `version_toml` replaces the deprecated `version_variable`; `build_command` runs before publishing (swap in `flit build`, `uv build`, etc. for other backends). See the **GitHub Actions** skill for the full CI workflow that runs semantic release on push to `main`/`develop` and publishes via trusted publishers. -## Release Checklist (Manual Alternative) +## Release Checklist (manual alternative) -If not using semantic release, follow this process for every release: +If not using semantic release: ```bash -# 1. Ensure all tests pass -pytest - -# 2. Update version in pyproject.toml (if not using scm) -# Edit pyproject.toml: version = "0.2.0" - -# 3. Update CHANGELOG.md -# Document what changed in this version - -# 4. Commit the version bump -git add pyproject.toml CHANGELOG.md -git commit -m "Release v0.2.0" - -# 5. Create annotated tag -git tag -a v0.2.0 -m "Release version 0.2.0" - -# 6. Push commit and tag -git push origin main -git push origin v0.2.0 -# GitHub Actions will build and publish automatically - -# 7. Verify on PyPI -pip install --upgrade my-cv-package -python -c "import my_cv_package; print(my_cv_package.__version__)" +pytest # 1. tests pass +# 2. bump version in pyproject.toml (if not using scm) + update CHANGELOG.md +git commit -am "Release v0.2.0" # 3. commit +git tag -a v0.2.0 -m "Release version 0.2.0" # 4. annotated tag +git push origin main && git push origin v0.2.0 # 5. push — Actions builds + publishes +pip install --upgrade my-cv-package # 6. verify ``` ## Including Package Data -If your package includes non-Python files (model configs, default parameters): +Ship non-Python files (configs, default params) and load them via `importlib.resources`: ```toml -# With hatchling -[tool.hatch.build.targets.wheel] -packages = ["src/my_cv_package"] - -# Include data files +# hatchling [tool.hatch.build.targets.wheel.force-include] "configs" = "my_cv_package/configs" -# With setuptools +# setuptools [tool.setuptools.package-data] my_cv_package = ["configs/*.yaml", "configs/*.json"] ``` -Access package data at runtime: - ```python from importlib import resources +import yaml + def get_default_config() -> dict: - """Load the default configuration shipped with the package.""" - config_file = resources.files("my_cv_package.configs").joinpath("default.yaml") - with resources.as_file(config_file) as path: - import yaml + cfg = resources.files("my_cv_package.configs").joinpath("default.yaml") + with resources.as_file(cfg) as path: return yaml.safe_load(path.read_text()) ``` ## Best Practices -1. **Use src layout** -- Prevents accidental local imports during development. -2. **Use trusted publishers** -- No API tokens to manage or leak. -3. **Test on TestPyPI first** -- Always verify the package installs correctly before publishing to the real index. -4. **Pin build dependencies** -- Specify minimum versions for build tools. -5. **Use `opencv-python-headless`** -- The headless variant avoids GUI dependency conflicts in server environments. -6. **Declare all dependencies** -- Never assume packages are pre-installed. List everything in `dependencies`. -7. **Use optional dependencies** -- Put dev, docs, and heavy optional dependencies in `[project.optional-dependencies]`. -8. **Automate releases** -- Use GitHub Actions with tag-triggered workflows so publishing is a single `git tag` command. -9. **Include py.typed** -- Add an empty `py.typed` marker file to signal PEP 561 type stub support. -10. **Version with git tags** -- Use setuptools-scm or hatch-vcs to derive versions from tags, eliminating manual version bumps. +1. **Use src layout** — prevents accidental local imports during development. +2. **Use trusted publishers** — no API tokens to manage or leak. +3. **Test on TestPyPI first** — verify the package installs before hitting the real index. +4. **Declare all dependencies** — never assume packages are pre-installed. +5. **Use `opencv-python-headless`** — avoids GUI dependency conflicts on servers. +6. **Use optional dependencies** — put dev/docs/heavy extras in `[project.optional-dependencies]`. +7. **Automate releases** — tag-triggered workflows make publishing a single `git tag`. +8. **Include `py.typed`** — signals PEP 561 type support. +9. **Version with git tags** — setuptools-scm or hatch-vcs eliminate manual bumps. diff --git a/skills/pytorch-lightning/README.md b/skills/pytorch-lightning/README.md index a5a80be..cda7a5b 100644 --- a/skills/pytorch-lightning/README.md +++ b/skills/pytorch-lightning/README.md @@ -22,7 +22,7 @@ This skill covers the full Lightning ecosystem: writing clean `LightningModule` ## Related Skills -- **[Pydantic Strict](../pydantic-strict/)** -- provides the configuration validation layer that all Lightning module and data parameters must use. +- **[Pydantic](../pydantic/)** -- provides the configuration validation layer that all Lightning module and data parameters must use. - **[Abstraction Patterns](../abstraction-patterns/)** -- defines the ABC/Protocol interfaces that LightningModule subclasses implement for task-specific models. - **[Docker CV](../docker-cv/)** -- builds training containers that include the correct CUDA runtime and Lightning dependencies. - **[Code Quality](../code-quality/)** -- enforces type annotations on all Lightning hooks and callback methods through mypy strict mode. diff --git a/skills/pytorch-lightning/SKILL.md b/skills/pytorch-lightning/SKILL.md index 214e6c9..a189ac0 100644 --- a/skills/pytorch-lightning/SKILL.md +++ b/skills/pytorch-lightning/SKILL.md @@ -1,24 +1,24 @@ --- name: pytorch-lightning description: > - PyTorch Lightning patterns for AI/CV training pipelines. Covers LightningModule - design, LightningDataModule, Trainer configuration, callbacks, logging integration, - distributed training, and checkpoint management. + Use this skill when building a training pipeline with PyTorch Lightning — + LightningModule and LightningDataModule design, Trainer configuration, callbacks, + logger integration, distributed DDP/FSDP training, and checkpoint management. Reach for + it any time you'd otherwise hand-write a raw PyTorch training loop with manual + epoch/optimizer/device handling, even if the user just says "train this model". For the + pretrained models and datasets you feed in see huggingface; for the logging backends see + wandb, tensorboard, or mlflow. --- # PyTorch Lightning Skill -You are writing PyTorch Lightning code for AI/CV projects. Follow these patterns exactly. - -## Core Philosophy - -PyTorch Lightning separates research code (the model) from engineering code (training loops, distributed training, logging). Every training project in this framework uses Lightning as the standard training abstraction. Never write raw training loops. +Lightning separates research code (the model) from engineering code (training loops, distributed training, logging). Use Lightning as the standard training abstraction; never write raw training loops. ## LightningModule Patterns ### Standard LightningModule Structure -Every model must follow this exact structure. The `LightningModule` encapsulates the model architecture, loss computation, optimizer configuration, and step logic. +Every model follows this structure: the `LightningModule` encapsulates architecture, loss, optimizer config, and step logic. ```python """LightningModule for image classification.""" @@ -35,14 +35,7 @@ from torch import Tensor class ImageClassifier(L.LightningModule): - """Image classification model using Lightning. - - Args: - num_classes: Number of output classes. - learning_rate: Initial learning rate for optimizer. - backbone: Name of the backbone architecture. - pretrained: Whether to use pretrained weights. - """ + """Image classification model using Lightning.""" def __init__( self, @@ -140,9 +133,7 @@ class ImageClassifier(L.LightningModule): ## LightningDataModule Patterns -### Standard DataModule Structure - -Every dataset must be wrapped in a `LightningDataModule`. This separates data logic from model logic and ensures reproducibility. +Wrap every dataset in a `LightningDataModule` to separate data logic from model logic and ensure reproducibility. ```python """DataModule for image classification datasets.""" @@ -158,15 +149,7 @@ from torchvision.datasets import ImageFolder class ImageClassificationDataModule(L.LightningDataModule): - """DataModule for image classification. - - Args: - data_dir: Root directory containing train/val/test splits. - batch_size: Batch size for dataloaders. - num_workers: Number of dataloader workers. - image_size: Target image size (height, width). - pin_memory: Whether to pin memory for GPU transfer. - """ + """DataModule for image classification.""" def __init__( self, @@ -252,16 +235,7 @@ class ImageClassificationDataModule(L.LightningDataModule): persistent_workers=self.num_workers > 0, ) - def test_dataloader(self) -> DataLoader: - """Test dataloader.""" - assert self.test_dataset is not None - return DataLoader( - self.test_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=self.num_workers, - pin_memory=self.pin_memory, - ) + # test_dataloader mirrors val_dataloader with self.test_dataset (shuffle=False). ``` ### Key Rules for DataModule @@ -274,8 +248,6 @@ class ImageClassificationDataModule(L.LightningDataModule): ## Trainer Configuration -### Standard Trainer Setup - ```python """Training script using Lightning Trainer.""" @@ -293,24 +265,15 @@ from lightning.pytorch.loggers import WandbLogger def train() -> None: """Run training pipeline.""" - # Seed everything for reproducibility L.seed_everything(42, workers=True) - # Initialize components datamodule = ImageClassificationDataModule( - data_dir="data/imagenet", - batch_size=64, - num_workers=8, + data_dir="data/imagenet", batch_size=64, num_workers=8 ) - model = ImageClassifier( - num_classes=1000, - learning_rate=1e-3, - backbone="resnet50", - pretrained=True, + num_classes=1000, learning_rate=1e-3, backbone="resnet50", pretrained=True ) - # Callbacks callbacks = [ ModelCheckpoint( dirpath="checkpoints/", @@ -320,24 +283,12 @@ def train() -> None: save_top_k=3, save_last=True, ), - EarlyStopping( - monitor="val/loss", - patience=10, - mode="min", - verbose=True, - ), + EarlyStopping(monitor="val/loss", patience=10, mode="min", verbose=True), LearningRateMonitor(logging_interval="step"), RichProgressBar(), ] + logger = WandbLogger(project="image-classification", name="resnet50-baseline", log_model=True) - # Logger - logger = WandbLogger( - project="image-classification", - name="resnet50-baseline", - log_model=True, - ) - - # Trainer trainer = L.Trainer( max_epochs=100, accelerator="auto", @@ -348,15 +299,11 @@ def train() -> None: logger=logger, deterministic=True, gradient_clip_val=1.0, - accumulate_grad_batches=1, val_check_interval=1.0, log_every_n_steps=50, ) - # Train trainer.fit(model, datamodule=datamodule) - - # Test with best checkpoint trainer.test(model, datamodule=datamodule, ckpt_path="best") @@ -364,17 +311,7 @@ if __name__ == "__main__": train() ``` -### Trainer Parameter Reference - -| Parameter | Value | Purpose | -|-----------|-------|---------| -| `accelerator` | `"auto"` | Auto-detect GPU/CPU/TPU | -| `devices` | `"auto"` | Use all available devices | -| `strategy` | `"auto"` | Auto-select DDP/FSDP/single | -| `precision` | `"16-mixed"` | Mixed precision for speed | -| `deterministic` | `True` | Reproducible results | -| `gradient_clip_val` | `1.0` | Prevent gradient explosion | -| `val_check_interval` | `1.0` | Validate every epoch | +Key Trainer args: `accelerator`/`devices`/`strategy="auto"` (auto-detect hardware and DDP/FSDP), `precision="16-mixed"` (mixed precision), `deterministic=True` (reproducibility), `gradient_clip_val=1.0` (prevent gradient explosion). ## Callbacks @@ -392,11 +329,7 @@ from lightning.pytorch.callbacks import Callback class ImageLoggingCallback(Callback): - """Log sample predictions as images to the logger. - - Args: - num_samples: Number of samples to log per validation epoch. - """ + """Log sample predictions as images to the logger.""" def __init__(self, num_samples: int = 8) -> None: super().__init__() @@ -451,7 +384,7 @@ from lightning.pytorch.callbacks import ( ## Logging -### Multi-Logger Setup +### Multiple loggers at once ```python from lightning.pytorch.loggers import CSVLogger, TensorBoardLogger, WandbLogger @@ -468,19 +401,11 @@ trainer = L.Trainer(logger=loggers) ### Logging Best Practices ```python -# In LightningModule methods: - -# Scalar logging +# Inside LightningModule methods: self.log("train/loss", loss, on_step=True, on_epoch=True, prog_bar=True) +self.log_dict({"val/loss": loss, "val/acc": acc, "val/f1": f1}, on_epoch=True) -# Multiple scalars at once -self.log_dict({ - "val/loss": loss, - "val/acc": acc, - "val/f1": f1, -}, on_epoch=True) - -# Image/artifact logging (use logger directly) +# Image/artifact logging goes through the logger's experiment handle: if self.logger: self.logger.experiment.log({"images": wandb_images}) ``` @@ -519,54 +444,34 @@ trainer = L.Trainer( ## Testing Lightning Code -```python -"""Tests for the image classifier module.""" - -from __future__ import annotations +Use `fast_dev_run=True` for smoke tests and `load_from_checkpoint` to verify hparams round-trip. +```python import lightning as L -import pytest import torch from my_project.models.classifier import ImageClassifier -@pytest.fixture -def model() -> ImageClassifier: - """Create a small model for testing.""" - return ImageClassifier(num_classes=10, backbone="resnet18", pretrained=False) - - -@pytest.fixture -def sample_batch() -> tuple[torch.Tensor, torch.Tensor]: - """Create a dummy batch.""" - images = torch.randn(4, 3, 224, 224) - labels = torch.randint(0, 10, (4,)) - return images, labels - - -def test_forward_shape(model: ImageClassifier, sample_batch: tuple) -> None: - """Test that forward produces correct output shape.""" - images, _ = sample_batch - output = model(images) +def test_forward_shape() -> None: + model = ImageClassifier(num_classes=10, backbone="resnet18", pretrained=False) + output = model(torch.randn(4, 3, 224, 224)) assert output.shape == (4, 10) -def test_training_step_returns_loss(model: ImageClassifier, sample_batch: tuple) -> None: - """Test that training_step returns a scalar loss.""" - # Lightning needs a trainer attached for logging - trainer = L.Trainer(fast_dev_run=True) - trainer.fit(model, train_dataloaders=[sample_batch]) +def test_fast_dev_run() -> None: + model = ImageClassifier(num_classes=10, backbone="resnet18", pretrained=False) + batch = (torch.randn(4, 3, 224, 224), torch.randint(0, 10, (4,))) + L.Trainer(fast_dev_run=True).fit(model, train_dataloaders=[batch]) -def test_save_and_load(model: ImageClassifier, tmp_path) -> None: - """Test checkpoint save and load.""" +def test_save_and_load(tmp_path) -> None: + model = ImageClassifier(num_classes=10, backbone="resnet18", pretrained=False) path = tmp_path / "model.ckpt" trainer = L.Trainer(default_root_dir=tmp_path, max_epochs=0) trainer.strategy.connect(model) trainer.save_checkpoint(path) - loaded = ImageClassifier.load_from_checkpoint(path) - assert loaded.hparams.num_classes == 10 + assert ImageClassifier.load_from_checkpoint(path).hparams.num_classes == 10 ``` ## Anti-Patterns to Avoid diff --git a/skills/pytorch-lightning/skill.toml b/skills/pytorch-lightning/skill.toml index eccd97e..4e5ca4f 100644 --- a/skills/pytorch-lightning/skill.toml +++ b/skills/pytorch-lightning/skill.toml @@ -5,7 +5,7 @@ category = "cv-ml" tags = ["training", "deep-learning", "pytorch", "lightning"] [dependencies] -requires = ["pydantic-strict", "loguru"] +requires = ["pydantic", "loguru"] recommends = ["hydra-config", "wandb"] [compatibility] diff --git a/skills/tensorboard/SKILL.md b/skills/tensorboard/SKILL.md index b3207a7..0b2a22d 100644 --- a/skills/tensorboard/SKILL.md +++ b/skills/tensorboard/SKILL.md @@ -1,9 +1,12 @@ --- name: tensorboard description: > - TensorBoard visualization and logging for PyTorch ML projects. Covers scalar - metrics, image logging, model graph visualization, histogram tracking, profiling, - and zero-setup local experiment monitoring. + Use this skill when logging and visualizing PyTorch training locally with TensorBoard — + scalar metrics, image logging, model-graph visualization, histogram tracking, + profiling, and zero-setup local experiment monitoring. Reach for it any time you want a + quick local training dashboard without a hosted service, even if the user just says + "let me watch the loss" or "log the metrics somewhere". For hosted, collaborative + tracking with sweeps and a model registry, see wandb or mlflow. --- # TensorBoard Logging for ML Projects diff --git a/skills/tensorrt/SKILL.md b/skills/tensorrt/SKILL.md index 48c9314..cef875e 100644 --- a/skills/tensorrt/SKILL.md +++ b/skills/tensorrt/SKILL.md @@ -1,61 +1,28 @@ --- name: tensorrt description: > - Maximize inference performance on NVIDIA GPUs by converting ONNX models to TensorRT - engines. Covers precision modes (FP16/INT8), dynamic shapes, engine building, - calibration, benchmarking, and Triton Inference Server deployment. + Use this skill when maximizing inference throughput and latency on NVIDIA GPUs by + converting ONNX models to TensorRT engines — FP16/INT8 precision modes, dynamic shapes, + engine building with trtexec or the Python API, INT8 calibration, benchmarking, and + Triton Inference Server deployment. Reach for it any time NVIDIA-GPU inference must be + squeezed to the limit, even if the user just says "make inference faster on the GPU". + Assumes an ONNX model already exists — see onnx to produce one first. --- # TensorRT Skill -Maximize inference performance on NVIDIA GPUs by converting ONNX models to TensorRT engines. This skill requires the ONNX skill — always export and slim with ONNX first, then convert to TensorRT. +Maximize inference performance on NVIDIA GPUs by converting ONNX models to TensorRT engines (typically 2-6x faster than ONNX Runtime CUDA via kernel auto-tuning, layer fusion, and FP16/INT8 precision). Requires the ONNX skill — TensorRT does not consume PyTorch directly. -## Prerequisites: ONNX First - -TensorRT does not consume PyTorch models directly. The required pipeline is: - -``` -PyTorch model - → torch.onnx.export() (ONNX skill) - → onnxslim.slim() (ONNX skill) - → trtexec / tensorrt.Builder (this skill) - → .engine file for deployment -``` - -Never skip the ONNX export and slimming steps. A slimmed ONNX model produces a better TensorRT engine because redundant ops are already removed before TensorRT's own optimizer runs. - -## Why TensorRT - -ONNX Runtime with CUDAExecutionProvider is good. TensorRT is better when you need maximum throughput on NVIDIA hardware: - -- **2-6x faster** than ONNX Runtime CUDA on the same GPU -- **Kernel auto-tuning** — selects the fastest kernel for each layer on your specific GPU -- **Layer fusion** — combines convolution + batch norm + activation into a single kernel -- **Precision calibration** — FP16 and INT8 inference with minimal accuracy loss -- **Memory optimization** — minimizes GPU memory allocations and data transfers -- **Dynamic shapes** — supports variable batch sizes and image dimensions - -The tradeoff: TensorRT engines are GPU-specific (an engine built on A100 will not run on T4) and take minutes to build. Use TensorRT when you deploy to known NVIDIA hardware and need the lowest latency. +Pipeline: `torch.onnx.export()` → `onnxslim.slim()` (ONNX skill) → `trtexec`/`tensorrt.Builder` (this skill) → `.engine`. Always slim before conversion; redundant ops removed early produce a better engine. Engines are GPU-architecture-specific (an A100 engine won't run on a T4) and take minutes to build — use TensorRT when deploying to known NVIDIA hardware for lowest latency. ## Installation ```bash -# Install TensorRT via pip (requires CUDA toolkit) -pip install tensorrt - -# Or via pixi (conda-forge) -# pixi add tensorrt - -# Verify installation +pixi add tensorrt # requires a matching CUDA toolkit python -c "import tensorrt; print(tensorrt.__version__)" ``` -TensorRT requires a matching CUDA version. Check compatibility: - -| TensorRT | CUDA | cuDNN | -|----------|------|-------| -| 10.x | 12.x | 9.x | -| 8.6 | 11.8 / 12.x | 8.9 | +CUDA compatibility: TensorRT 10.x → CUDA 12.x / cuDNN 9.x; TensorRT 8.6 → CUDA 11.8 or 12.x / cuDNN 8.9. ## Building Engines with trtexec (CLI) @@ -108,19 +75,8 @@ trtexec \ ### Benchmarking with trtexec ```bash -# Benchmark throughput and latency -trtexec \ - --onnx=model.onnx \ - --fp16 \ - --iterations=1000 \ - --warmUp=500 \ - --avgRuns=100 - -# Output includes: -# - Throughput (inferences/sec) -# - Latency (min, max, mean, median, p99) -# - GPU compute time -# - Host latency (end-to-end) +# Reports throughput, latency percentiles, GPU compute time, and host latency +trtexec --onnx=model.onnx --fp16 --iterations=1000 --warmUp=500 --avgRuns=100 ``` ## Building Engines with Python API @@ -180,56 +136,19 @@ def build_engine( logger.info("Engine saved to {} ({:.1f} MB)", engine_path, engine_path.stat().st_size / 1e6) ``` -### Dynamic Shape Builder - -```python -import tensorrt as trt - -from loguru import logger - - -def build_engine_dynamic( - onnx_path: str, - engine_path: str, - fp16: bool = True, - min_shape: tuple[int, ...] = (1, 3, 320, 320), - opt_shape: tuple[int, ...] = (8, 3, 640, 640), - max_shape: tuple[int, ...] = (32, 3, 1280, 1280), - input_name: str = "input", -) -> None: - """Build a TensorRT engine with dynamic input shapes.""" - trt_logger = trt.Logger(trt.Logger.INFO) - builder = trt.Builder(trt_logger) - network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) - parser = trt.OnnxParser(network, trt_logger) - - with open(onnx_path, "rb") as f: - if not parser.parse(f.read()): - for i in range(parser.num_errors): - logger.error("{}", parser.get_error(i)) - raise RuntimeError("ONNX parse failed") - - config = builder.create_builder_config() - config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 << 30) - - if fp16: - config.set_flag(trt.BuilderFlag.FP16) - - # Create optimization profile for dynamic shapes - profile = builder.create_optimization_profile() - profile.set_shape(input_name, min_shape, opt_shape, max_shape) - config.add_optimization_profile(profile) - - logger.info( - "Building engine: min={}, opt={}, max={}", - min_shape, opt_shape, max_shape, - ) +### Dynamic Shapes - serialized = builder.build_serialized_network(network, config) - with open(engine_path, "wb") as f: - f.write(serialized) +Same builder as above, but add an optimization profile to the config before building. `opt_shape` should match your most common batch/resolution: - logger.info("Engine saved to {}", engine_path) +```python +profile = builder.create_optimization_profile() +profile.set_shape( + "input", + min=(1, 3, 320, 320), + opt=(8, 3, 640, 640), + max=(32, 3, 1280, 1280), +) +config.add_optimization_profile(profile) ``` ## Inference with TensorRT @@ -281,38 +200,26 @@ class TensorRTInference: self.context.set_tensor_address(name, device_mem) def predict(self, input_array: np.ndarray) -> np.ndarray: - """Run inference on a single input.""" - # Copy input to device cudart.cudaMemcpy( - self.inputs[0]["device"], - input_array.ctypes.data, - input_array.nbytes, + self.inputs[0]["device"], input_array.ctypes.data, input_array.nbytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, ) - - # Execute self.context.execute_async_v3(0) - - # Copy output from device output = np.empty(self.outputs[0]["shape"], dtype=self.outputs[0]["dtype"]) cudart.cudaMemcpy( - output.ctypes.data, - self.outputs[0]["device"], - output.nbytes, + output.ctypes.data, self.outputs[0]["device"], output.nbytes, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, ) - return output def __del__(self) -> None: - """Free GPU memory.""" for buf in self.inputs + self.outputs: cudart.cudaFree(buf["device"]) ``` ### Using ONNX Runtime with TensorRT Backend -If you want TensorRT performance without managing raw CUDA memory, use ONNX Runtime's TensorRT execution provider: +Recommended for most projects: TensorRT performance without managing raw CUDA memory. ONNX Runtime handles engine building/caching and falls back to CUDA or CPU for unsupported layers. ```python import onnxruntime as ort @@ -323,11 +230,6 @@ def create_tensorrt_session( fp16: bool = True, max_workspace_size: int = 4 * 1024 * 1024 * 1024, ) -> ort.InferenceSession: - """Create an ONNX Runtime session with TensorRT backend. - - This is the simplest way to use TensorRT — ONNX Runtime handles - engine building and caching automatically. - """ providers = [ ( "TensorrtExecutionProvider", @@ -343,26 +245,17 @@ def create_tensorrt_session( "CPUExecutionProvider", ] - session = ort.InferenceSession(onnx_path, providers=providers) - return session + return ort.InferenceSession(onnx_path, providers=providers) ``` -This approach is recommended for most projects — it gives TensorRT performance with the ONNX Runtime API you already know, and automatically falls back to CUDA or CPU if TensorRT cannot handle a layer. - ## INT8 Calibration -INT8 provides the fastest inference but requires calibration data to preserve accuracy. - -### Calibration with trtexec +INT8 gives the fastest inference but requires calibration data to preserve accuracy. ```bash # Generate calibration cache from a dataset -trtexec \ - --onnx=model.onnx \ - --saveEngine=model_int8.engine \ - --int8 \ - --calib=calibration_cache.bin \ - --calibBatchSize=32 +trtexec --onnx=model.onnx --saveEngine=model_int8.engine \ + --int8 --calib=calibration_cache.bin --calibBatchSize=32 ``` ### Python Calibration @@ -452,55 +345,10 @@ class TensorRTBuildConfig(BaseModel, frozen=True): class TensorRTInferenceConfig(BaseModel, frozen=True): - """TensorRT inference configuration.""" - engine_path: str = Field(description="Path to compiled engine") device_id: int = Field(default=0, ge=0) ``` -```yaml -# configs/tensorrt.yaml -tensorrt: - onnx_path: models/model.onnx - engine_path: models/model.engine - fp16: true - int8: false - max_workspace_gb: 4 - max_batch_size: 8 - min_shape: [1, 3, 640, 640] - opt_shape: [8, 3, 640, 640] - max_shape: [32, 3, 640, 640] -``` - -## Integration with pixi - -```toml -# pixi.toml — TensorRT tasks -[tasks] -# Full export pipeline: PyTorch → ONNX → OnnxSlim → TensorRT -export-onnx = "python -m my_project.export --format onnx" -export-trt = { cmd = "python -m my_project.export --format tensorrt", depends-on = ["export-onnx"] } - -# Build engine with trtexec -trt-build = """trtexec \ - --onnx=models/model.onnx \ - --saveEngine=models/model.engine \ - --fp16""" - -trt-build-dynamic = """trtexec \ - --onnx=models/model.onnx \ - --saveEngine=models/model.engine \ - --fp16 \ - --minShapes=input:1x3x640x640 \ - --optShapes=input:8x3x640x640 \ - --maxShapes=input:32x3x640x640""" - -trt-benchmark = """trtexec \ - --loadEngine=models/model.engine \ - --iterations=1000 \ - --warmUp=500""" -``` - ## Benchmarking: ONNX Runtime vs TensorRT ```python @@ -570,40 +418,38 @@ FROM nvcr.io/nvidia/tensorrt:24.08-py3 WORKDIR /app -# Install project dependencies +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" + COPY pixi.toml pixi.lock ./ -RUN pip install --no-cache-dir -r requirements-inference.txt +RUN pixi install -# Copy model and application COPY models/ models/ COPY src/ src/ -# Build TensorRT engine at container startup (GPU-specific) -# Or copy a pre-built engine for the target GPU -ENTRYPOINT ["python", "-m", "my_project.serve"] +# Build the engine at startup (GPU-specific) or COPY a pre-built engine for the target GPU +ENTRYPOINT ["pixi", "run", "python", "-m", "my_project.serve"] ``` -Note: TensorRT engines are GPU-architecture-specific. An engine built on A100 will not work on T4. Either build engines at container startup or build separate images per GPU target. +Engines are GPU-architecture-specific — build at container startup or ship separate images per GPU target. ## Best Practices -1. **Always export to ONNX first** — use the ONNX skill pipeline (export → slim → validate) before TensorRT conversion. -2. **Use FP16 as the default precision** — it halves memory usage with negligible accuracy loss on most models. -3. **Use ONNX Runtime TensorRT EP for simplicity** — it manages engine building and caching automatically; use raw TensorRT API only when you need maximum control. -4. **Cache built engines** — engine builds are slow (minutes); cache them by GPU architecture and rebuild only when the model changes. -5. **Set dynamic shapes with realistic profiles** — `opt_shape` should match your most common batch size and resolution. -6. **Validate accuracy after conversion** — compare TensorRT outputs against ONNX/PyTorch to catch precision issues. -7. **Profile with `trtexec`** — use `--dumpProfile` to identify slow layers. -8. **Pin TensorRT and CUDA versions** — document the exact versions in your Dockerfile and README. -9. **Use INT8 only with calibration data** — uncalibrated INT8 can cause significant accuracy loss. -10. **Build per-GPU-architecture** — engines are not portable across GPU architectures (e.g., A100 vs T4). - -## Anti-Patterns to Avoid - -- ❌ Exporting PyTorch directly to TensorRT without going through ONNX — use the ONNX skill pipeline first. -- ❌ Deploying raw ONNX models to TensorRT without running `onnxslim.slim()` — slimming removes redundant ops that confuse the TensorRT optimizer. -- ❌ Assuming TensorRT engines are portable — they are tied to the GPU architecture and TensorRT version they were built on. -- ❌ Using INT8 without calibration data — this causes significant accuracy degradation. -- ❌ Setting `max_workspace_size` too low — TensorRT needs workspace memory for kernel auto-tuning; start with 4 GB. -- ❌ Skipping warmup in benchmarks — the first few inferences include JIT compilation and memory allocation overhead. -- ❌ Using TensorRT on CPUs or non-NVIDIA GPUs — it only runs on NVIDIA hardware; use ONNX Runtime for cross-platform deployment. +1. **Export to ONNX first** — run the ONNX skill pipeline (export → slim → validate) before conversion. +2. **Default to FP16** — halves memory with negligible accuracy loss on most models. +3. **Prefer the ONNX Runtime TensorRT EP** — it manages engine building/caching automatically; use the raw API only for maximum control. +4. **Cache built engines** by GPU architecture; rebuild only when the model changes. +5. **Set realistic dynamic profiles** — `opt_shape` should match your most common batch/resolution. +6. **Validate accuracy after conversion** against ONNX/PyTorch outputs. +7. **Profile with `trtexec --dumpProfile`** to find slow layers. +8. **Pin TensorRT and CUDA versions** in the Dockerfile and README. +9. **Use INT8 only with calibration data** — uncalibrated INT8 loses significant accuracy. + +## Anti-Patterns + +- ❌ Exporting PyTorch straight to TensorRT without ONNX + `onnxslim.slim()` first — slimming removes ops that confuse the optimizer. +- ❌ Assuming engines are portable — they are tied to the GPU architecture and TensorRT version. +- ❌ Using INT8 without calibration data. +- ❌ Setting `max_workspace_size` too low — TensorRT needs workspace for kernel auto-tuning; start with 4 GB. +- ❌ Skipping benchmark warmup — the first inferences include JIT compilation and allocation overhead. +- ❌ Using TensorRT on CPUs or non-NVIDIA GPUs — use ONNX Runtime for cross-platform deployment. diff --git a/skills/testing/SKILL.md b/skills/testing/SKILL.md index 19e75ec..a832461 100644 --- a/skills/testing/SKILL.md +++ b/skills/testing/SKILL.md @@ -1,47 +1,34 @@ --- name: testing description: > - Comprehensive pytest patterns for ML and computer vision projects. Covers test - structure, fixtures, parametrized tests, CV-specific assertions, tensor shape - validation, mocking, performance testing, and CI integration. + Use this skill when writing or fixing tests for ML/CV code with pytest — test structure, + fixtures and conftest, parametrized tests, CV-specific assertions, tensor-shape + validation, mocking external dependencies, performance tests, and coverage/CI. Reach + for it any time code needs tests or a test is failing, even if the user just says "add + tests" or "why is this test breaking". This is generic code-correctness testing; for + scoring model quality with metrics like mAP, IoU, or calibration, see model-evaluation. --- # Testing Skill -Comprehensive pytest patterns for ML and computer vision projects. This skill covers test structure, fixtures, parametrized tests, CV-specific testing strategies, mocking, performance testing, and CI integration. - -## Why Testing Matters in ML/CV - -ML projects are especially prone to silent failures. A model can train without errors but produce garbage predictions due to incorrect preprocessing, wrong label mapping, transposed dimensions, or broken augmentation. Automated tests catch these issues before they waste hours of GPU time. +Pytest patterns for ML and computer vision projects: test structure, fixtures, parametrized tests, CV-specific strategies, mocking, performance testing, and CI. ML code fails silently — a model can train cleanly yet predict garbage from wrong preprocessing, label mapping, transposed dims, or broken augmentation. Tests catch these before they waste GPU hours. ## Test Structure -Organize tests into three tiers: unit tests (fast, isolated), integration tests (component interactions), and end-to-end tests (full pipeline). +Three tiers: unit (fast, isolated), integration (component interactions), and e2e (full pipeline). ``` tests/ -├── conftest.py # Shared fixtures -├── unit/ -│ ├── conftest.py -│ ├── test_model.py -│ ├── test_transforms.py -│ ├── test_dataset.py -│ ├── test_metrics.py -│ └── test_utils.py -├── integration/ -│ ├── conftest.py -│ ├── test_training_step.py -│ ├── test_data_pipeline.py -│ └── test_inference.py -└── e2e/ - ├── conftest.py - ├── test_train_eval.py - └── test_export.py +├── conftest.py # repo-wide fixtures +├── unit/ # test_model, test_transforms, test_dataset, test_metrics +├── integration/ # test_training_step, test_data_pipeline, test_inference +└── e2e/ # test_train_eval, test_export ``` +Each tier gets its own `conftest.py` for tier-scoped fixtures. ## The AAA Pattern -Every test should follow Arrange-Act-Assert. This makes tests readable and maintainable. +Every test follows Arrange-Act-Assert. ```python def test_model_forward_pass(): @@ -60,7 +47,7 @@ def test_model_forward_pass(): ## Fixtures and conftest.py -Use fixtures to create reusable test data and resources. Place shared fixtures in `conftest.py` at the appropriate level. +Place shared fixtures in `conftest.py` at the appropriate level (repo-wide vs. per-tier). ### Top-Level conftest.py @@ -99,25 +86,14 @@ def sample_batch(device) -> torch.Tensor: @pytest.fixture def sample_bboxes() -> np.ndarray: - """Create sample bounding boxes in xyxy format.""" - return np.array([ - [10, 20, 100, 150], - [200, 50, 350, 300], - [50, 100, 200, 400], - ], dtype=np.float32) - - -@pytest.fixture -def sample_labels() -> np.ndarray: - """Create sample class labels.""" - return np.array([0, 1, 2], dtype=np.int64) + """Sample bounding boxes in xyxy format.""" + return np.array([[10, 20, 100, 150], [200, 50, 350, 300]], dtype=np.float32) @pytest.fixture(scope="session") def trained_model(tmp_path_factory): - """Create a small trained model for integration tests. Session-scoped for speed.""" + """Small model with non-random weights for integration tests. Session-scoped for speed.""" model = SmallTestModel(num_classes=5) - # Minimal training to get non-random weights optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for _ in range(10): x = torch.randn(2, 3, 32, 32) @@ -131,11 +107,10 @@ def trained_model(tmp_path_factory): @pytest.fixture def tmp_data_dir(tmp_path) -> Path: - """Create a temporary data directory with sample images.""" + """Temporary ImageFolder-style dataset (train/val/test × classes).""" for split in ["train", "val", "test"]: - split_dir = tmp_path / split for cls in ["cat", "dog"]: - cls_dir = split_dir / cls + cls_dir = tmp_path / split / cls cls_dir.mkdir(parents=True) for i in range(5): img = np.random.randint(0, 256, (64, 64, 3), dtype=np.uint8) @@ -163,7 +138,7 @@ def model_factory(request): ## Parametrized Tests -Use `@pytest.mark.parametrize` to test multiple inputs without duplicating test code. +Use `@pytest.mark.parametrize` (stack decorators for a cross product) and `ids=` for readable case names. ```python import pytest @@ -190,19 +165,14 @@ def test_model_handles_various_sizes(batch_size, image_size): ) def test_bbox_conversion(bbox_format, expected): """Test bounding box format conversions.""" - bbox_xyxy = [10, 20, 110, 220] - result = convert_bbox(bbox_xyxy, from_format="xyxy", to_format=bbox_format) + result = convert_bbox([10, 20, 110, 220], from_format="xyxy", to_format=bbox_format) np.testing.assert_array_almost_equal(result, expected) @pytest.mark.parametrize( "num_classes,input_shape", - [ - (10, (1, 3, 224, 224)), - (100, (4, 3, 224, 224)), - (1000, (1, 3, 384, 384)), - ], - ids=["10cls-single", "100cls-batch", "1000cls-highres"], + [(10, (1, 3, 224, 224)), (1000, (1, 3, 384, 384))], + ids=["10cls-single", "1000cls-highres"], ) def test_classifier_output(num_classes, input_shape): """Test classifier with various class counts and inputs.""" @@ -229,20 +199,16 @@ def make_synthetic_detection_sample( num_objects: int = 5, num_classes: int = 10, ) -> dict: - """Create a synthetic detection sample with image, boxes, and labels.""" + """Synthetic detection sample with image, boxes (xyxy), and labels.""" h, w = image_size - image = np.random.randint(0, 256, (h, w, 3), dtype=np.uint8) - boxes = [] for _ in range(num_objects): - x1 = np.random.randint(0, w - 50) - y1 = np.random.randint(0, h - 50) + x1, y1 = np.random.randint(0, w - 50), np.random.randint(0, h - 50) x2 = np.random.randint(x1 + 10, min(x1 + 200, w)) y2 = np.random.randint(y1 + 10, min(y1 + 200, h)) boxes.append([x1, y1, x2, y2]) - return { - "image": image, + "image": np.random.randint(0, 256, (h, w, 3), dtype=np.uint8), "boxes": np.array(boxes, dtype=np.float32), "labels": np.random.randint(0, num_classes, num_objects), } @@ -300,28 +266,13 @@ def test_horizontal_flip_mirrors_bboxes(): labels = [0] result = transform(image=image, bboxes=bboxes, labels=labels) - - # x1 should become width - original_x2 = 200 - 50 = 150 + # x1 becomes width - original_x2 = 200 - 50 = 150 assert result["bboxes"][0][0] == pytest.approx(150, abs=1) - - -def test_augmentation_output_range(): - """Augmented image values should stay in valid range.""" - transform = A.Compose([ - A.RandomBrightnessContrast(brightness_limit=0.5, contrast_limit=0.5, p=1.0), - ]) - image = np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8) - - for _ in range(50): - result = transform(image=image) - assert result["image"].min() >= 0 - assert result["image"].max() <= 255 ``` ### Testing Video Processing ```python -import tempfile from pathlib import Path import cv2 @@ -331,33 +282,24 @@ import pytest @pytest.fixture def synthetic_video(tmp_path) -> Path: - """Create a synthetic video file for testing.""" + """Write a 90-frame (3s @ 30fps) synthetic video.""" video_path = tmp_path / "test_video.mp4" - fourcc = cv2.VideoWriter_fourcc(*"mp4v") - writer = cv2.VideoWriter(str(video_path), fourcc, 30.0, (640, 480)) - - for i in range(90): # 3 seconds at 30fps + writer = cv2.VideoWriter(str(video_path), cv2.VideoWriter_fourcc(*"mp4v"), 30.0, (640, 480)) + for i in range(90): frame = np.zeros((480, 640, 3), dtype=np.uint8) - # Draw a moving circle for visual verification x = int(320 + 200 * np.sin(2 * np.pi * i / 90)) cv2.circle(frame, (x, 240), 30, (0, 255, 0), -1) writer.write(frame) - writer.release() return video_path -def test_video_reader_frame_count(synthetic_video): - """VideoReader should report correct frame count.""" +def test_video_reader(synthetic_video): + """VideoReader should report metadata and yield all frames.""" reader = VideoReader(synthetic_video) assert reader.frame_count == 90 assert reader.fps == pytest.approx(30.0, abs=0.1) assert reader.resolution == (640, 480) - - -def test_video_reader_iteration(synthetic_video): - """VideoReader should yield all frames.""" - reader = VideoReader(synthetic_video) frames = list(reader) assert len(frames) == 90 assert all(f.shape == (480, 640, 3) for f in frames) @@ -374,52 +316,27 @@ import pytest def test_wandb_logging_called(): - """Verify metrics are logged to W&B.""" + """Patch a module to verify metrics are logged without a real W&B run.""" with patch("myproject.training.wandb") as mock_wandb: - trainer = Trainer(use_wandb=True) - trainer.log_metrics({"loss": 0.5, "accuracy": 0.9}, step=100) - - mock_wandb.log.assert_called_once_with( - {"loss": 0.5, "accuracy": 0.9}, step=100 - ) - - -def test_model_loading_from_checkpoint(tmp_path): - """Test model loading without requiring actual checkpoint file.""" - mock_checkpoint = { - "model_state_dict": {k: torch.randn_like(v) for k, v in model.state_dict().items()}, - "epoch": 50, - "loss": 0.1, - } - checkpoint_path = tmp_path / "model.pt" - torch.save(mock_checkpoint, checkpoint_path) - - loaded_model = MyModel.load_from_checkpoint(checkpoint_path) - assert loaded_model is not None + Trainer(use_wandb=True).log_metrics({"loss": 0.5}, step=100) + mock_wandb.log.assert_called_once_with({"loss": 0.5}, step=100) @pytest.fixture def mock_camera(): - """Mock camera that returns synthetic frames.""" + """Mock cv2.VideoCapture returning synthetic frames and properties.""" camera = MagicMock() - camera.read.return_value = ( - True, - np.random.randint(0, 256, (480, 640, 3), dtype=np.uint8), - ) + camera.read.return_value = (True, np.random.randint(0, 256, (480, 640, 3), dtype=np.uint8)) camera.isOpened.return_value = True camera.get.side_effect = lambda prop: { - cv2.CAP_PROP_FRAME_WIDTH: 640, - cv2.CAP_PROP_FRAME_HEIGHT: 480, - cv2.CAP_PROP_FPS: 30, + cv2.CAP_PROP_FRAME_WIDTH: 640, cv2.CAP_PROP_FRAME_HEIGHT: 480, cv2.CAP_PROP_FPS: 30, }.get(prop, 0) return camera def test_camera_processor_with_mock(mock_camera): - """Test camera processing pipeline with mock camera.""" with patch("cv2.VideoCapture", return_value=mock_camera): - processor = CameraProcessor(camera_id=0) - frame = processor.read_frame() + frame = CameraProcessor(camera_id=0).read_frame() assert frame.shape == (480, 640, 3) ``` @@ -455,24 +372,10 @@ def test_model_inference_speed(device): ms_per_image = (elapsed / 100) * 1000 assert ms_per_image < 50, f"Inference too slow: {ms_per_image:.1f}ms" - - -@pytest.mark.slow -def test_data_loading_throughput(tmp_data_dir): - """DataLoader should achieve at least 100 images/second.""" - dataset = ImageDataset(tmp_data_dir / "train") - loader = DataLoader(dataset, batch_size=32, num_workers=4) - - start = time.perf_counter() - total_images = 0 - for batch in loader: - total_images += batch.shape[0] - elapsed = time.perf_counter() - start - - throughput = total_images / elapsed - assert throughput > 100, f"Too slow: {throughput:.0f} img/s" ``` +Always warm up and call `torch.cuda.synchronize()` around GPU timing. Apply the same pattern to `DataLoader` throughput tests. + ## Edge Case Testing ```python @@ -483,38 +386,21 @@ def test_empty_image(): model(torch.randn(1, 3, 0, 0)) -def test_single_pixel_image(): - """Model should handle 1x1 images.""" - model = MyModel(num_classes=10) - output = model(torch.randn(1, 3, 1, 1)) - assert output.shape == (1, 10) - - def test_no_detections(): """Post-processing should return empty results when nothing detected.""" - raw_output = torch.zeros(1, 0, 6) # No detections - results = postprocess(raw_output, confidence_threshold=0.5) + results = postprocess(torch.zeros(1, 0, 6), confidence_threshold=0.5) assert len(results[0]["boxes"]) == 0 assert len(results[0]["labels"]) == 0 -def test_all_same_class(): - """Metrics should handle case where all predictions are same class.""" - preds = np.array([0, 0, 0, 0, 0]) - targets = np.array([0, 1, 2, 0, 1]) - metrics = compute_metrics(preds, targets, num_classes=3) - assert 0 <= metrics["accuracy"] <= 1 - - def test_nan_in_loss(): - """Training should detect and handle NaN loss.""" + """Training should detect and raise on NaN loss.""" with pytest.raises(RuntimeError, match="NaN"): - trainer = Trainer(detect_nan=True) - trainer.train_step( - model, torch.tensor(float("nan")), optimizer - ) + Trainer(detect_nan=True).train_step(model, torch.tensor(float("nan")), optimizer) ``` +Also cover: 1x1 images, all-same-class metrics, wrong dtypes, and maximum sizes. + ## Coverage Configuration Configure coverage in `pyproject.toml`: @@ -522,15 +408,8 @@ Configure coverage in `pyproject.toml`: ```toml [tool.pytest.ini_options] testpaths = ["tests"] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "gpu: marks tests that require GPU", -] -addopts = [ - "--strict-markers", - "-ra", - "--tb=short", -] +markers = ["slow: slow tests (deselect with -m 'not slow')", "gpu: requires GPU"] +addopts = ["--strict-markers", "-ra", "--tb=short"] [tool.coverage.run] source = ["src/myproject"] @@ -539,32 +418,10 @@ omit = ["*/tests/*", "*/__pycache__/*"] [tool.coverage.report] fail_under = 80 show_missing = true -exclude_lines = [ - "pragma: no cover", - "if __name__ == .__main__.", - "if TYPE_CHECKING:", - "raise NotImplementedError", -] +exclude_lines = ["pragma: no cover", "if TYPE_CHECKING:", "raise NotImplementedError"] ``` -Run with: - -```bash -# Run all tests with coverage -pytest --cov=src/myproject --cov-report=html --cov-report=term-missing - -# Run only fast tests -pytest -m "not slow" - -# Run with verbose output -pytest -v --tb=long - -# Run specific test file -pytest tests/unit/test_model.py - -# Run tests matching a pattern -pytest -k "test_bbox" -``` +Common invocations: `pytest --cov=src/myproject --cov-report=term-missing` (coverage), `-m "not slow"` (skip slow), `tests/unit/test_model.py` (one file), `-k "test_bbox"` (by pattern). ## CI Integration @@ -581,29 +438,25 @@ jobs: python-version: ["3.11", "3.12"] steps: - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: pip install -e ".[dev]" - - name: Run tests - run: pytest --cov=src/myproject --cov-report=xml -m "not slow" - - name: Upload coverage - uses: codecov/codecov-action@v4 + - run: pip install -e ".[dev]" + - run: pytest --cov=src/myproject --cov-report=xml -m "not slow" + - uses: codecov/codecov-action@v4 with: file: coverage.xml ``` ## Best Practices -1. **Test behavior, not implementation** -- Tests should verify what a function does, not how it does it. This makes refactoring safe. -2. **One assertion concept per test** -- Each test should verify one logical concept. Multiple related `assert` statements are fine if they test the same thing. -3. **Use descriptive test names** -- `test_model_raises_on_wrong_input_channels` is better than `test_model_error`. -4. **Keep tests fast** -- Unit tests should run in milliseconds. Mark slow tests with `@pytest.mark.slow`. -5. **Use fixtures over setup methods** -- Pytest fixtures are more flexible and composable than `setUp`/`tearDown`. -6. **Test edge cases** -- Empty inputs, single elements, maximum sizes, NaN values, wrong dtypes. -7. **Pin random seeds in tests** -- Use a seed fixture to make tests deterministic. -8. **Maintain 80%+ coverage** -- Configure `fail_under = 80` in coverage settings. -9. **Run tests in CI on every push** -- Never merge without green tests. -10. **Test data shapes explicitly** -- In ML code, shape mismatches are the most common bugs. Assert shapes everywhere. +1. **Test behavior, not implementation** — keeps refactoring safe. +2. **One assertion concept per test** — related asserts on the same concept are fine. +3. **Descriptive names** — `test_model_raises_on_wrong_input_channels`, not `test_model_error`. +4. **Keep unit tests in milliseconds** — mark slow ones with `@pytest.mark.slow`. +5. **Prefer fixtures over `setUp`/`tearDown`** — more composable. +6. **Test edge cases** — empty inputs, single elements, max sizes, NaN, wrong dtypes. +7. **Pin random seeds** via a seed fixture for determinism. +8. **Maintain 80%+ coverage** (`fail_under = 80`). +9. **Run tests in CI on every push** — never merge red. +10. **Assert data shapes explicitly** — shape mismatches are the most common ML bug. diff --git a/skills/testing/skill.toml b/skills/testing/skill.toml index f416e26..de1195b 100644 --- a/skills/testing/skill.toml +++ b/skills/testing/skill.toml @@ -5,7 +5,7 @@ category = "core" tags = ["testing", "pytest", "coverage", "tdd"] [dependencies] -requires = ["pydantic-strict"] +requires = ["pydantic"] recommends = ["code-quality"] [compatibility] diff --git a/skills/vscode/SKILL.md b/skills/vscode/SKILL.md index bb3bc0f..39d1b16 100644 --- a/skills/vscode/SKILL.md +++ b/skills/vscode/SKILL.md @@ -1,9 +1,12 @@ --- name: vscode description: > - Configure VS Code for productive computer vision and ML development. Covers - workspace settings, Ruff and MyPy integration, Python debugging configurations, - remote SSH GPU server setup, and recommended extensions. + Use this skill when configuring VS Code for CV/ML development — workspace settings.json, + Ruff and MyPy editor integration, Python debug launch configs, tasks, remote-SSH to GPU + servers, dev containers, and recommended extensions. Reach for it any time you'd + otherwise hand-edit .vscode files or set up a remote GPU dev environment, even if the + user just says "set up my editor" or "debug this in VS Code". This is editor setup; the + lint/type standards live in code-quality and commit hooks in pre-commit. --- # VS Code Skill diff --git a/skills/wandb/SKILL.md b/skills/wandb/SKILL.md index 23a0746..7fb7f81 100644 --- a/skills/wandb/SKILL.md +++ b/skills/wandb/SKILL.md @@ -1,9 +1,12 @@ --- name: wandb description: > - Weights & Biases (W&B) integration for ML experiment tracking and collaboration. - Covers metric logging, artifact management, hyperparameter sweeps, model registry, - dataset versioning, and opt-in graceful degradation patterns. + Use this skill when experiment tracking and collaboration run on Weights & Biases — + logging metrics, managing artifacts, running hyperparameter sweeps, the model registry, + dataset versioning, and opt-in graceful degradation. Reach for it any time training runs + need hosted tracking, sweeps, or team sharing and the stack is W&B, even if the user + just says "track this experiment" or "log the run". For a zero-setup local dashboard see + tensorboard; for the MLflow alternative see mlflow. --- # Weights & Biases (W&B) Integration for ML Projects From 3af8c08b132fed1aeceb441008dd22aedf2604a1 Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Thu, 23 Jul 2026 22:10:44 -0400 Subject: [PATCH 07/21] P1: remove agents/, promote data-pipelines into skills (#7) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Follows [#6](https://github.com/ortizeg/whet/pull/6) (merged). Result: **32 skills, 0 agents.** ## Why remove the agents They were never Claude Code subagents. Four independent pieces of evidence: 1. **4 of 6 had no YAML frontmatter** — `expert-coder`, `ml-engineer`, `code-review`, `test-engineer` began directly with an H1. They installed with the heading as their entire description (`expert-coder: Expert Coder Agent`) and were effectively untriggerable. The two that worked were the two that happened to have frontmatter. 2. **All 6 installed to `.claude/skills/`, not `.claude/agents/`.** `discover_agents()` returned `Skill` objects; there was no `Agent` model anywhere in `src/`. 3. **`agent.toml` was never parsed** — only a doctor check and a test asserted the file existed. The `advisory`/`blocking` field was read exactly once, by a test. 4. **Both "blocking" `action.yml` files were broken** — they ran `pixi run` against a repo with no `pixi.toml` (whet itself uses uv), and no workflow referenced them. Content-wise ~77% of their 3,047 lines duplicated existing skills. Because they were written independently they *diverged*: `test-engineer` timed GPU inference without `cuda.synchronize()` (meaningless) while `skills/testing` does it correctly; `expert-coder` recommended `logging.getLogger` against the repo's own Loguru mandate. GSD already ships 33 correctly-formatted subagents. **GSD owns agency; whet owns domain knowledge — and knowledge is a skill.** ## Promoted to a skill - **`skills/data-pipelines`** (481 lines) from `data-engineer` — storage-format decision matrix, **group-aware leakage-preventing splitting** (frames from one match must not straddle splits), schema evolution/migration, Great Expectations suites. ## Also removed (per review) - **`dvc`** — not part of the workflow. Removed the skill, its docs page, nav entry, archetype/`skill.toml` references, and rewrote prose that pointed at it. Where DVC appeared as a *generic* data-versioning concept, the guidance is now tool-neutral (object storage plus a versioned/content-hashed manifest) rather than naming an unused tool. - **`cv-model-selection`** — not wanted. ## Salvaged before deletion | From | To | |---|---| | devops-infra containerization tree | `docker-cv` | | devops-infra deployment tree ("is K8s right?") | `kubernetes` | | devops-infra CI/CD tree | `github-actions` | | test-engineer coverage thresholds + naming convention | `testing` | ## Removed plumbing `agents/`, `docs/agents/`, `discover_agents()`, `--agents-only`/`--skills-only`, `agents_dir` config, the two doctor agent checks (remaining checks renumbered 1–8), `tests/test_agents.py`, and the agents nav/how-to sections. ## Verification - `uv run pytest tests/` → **192 passed** - `uv run ruff check .` / `ruff format --check .` → clean - `uv run mypy src/whet/ tests/ --strict` → clean - `uv run mkdocs build --strict` → clean (caught and fixed dangling agent links) 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 --- CLAUDE.md | 11 - README.md | 3 +- agents/README.md | 25 - agents/code-review/README.md | 27 - agents/code-review/SKILL.md | 335 ------- agents/code-review/action.yml | 39 - agents/code-review/agent.toml | 8 - agents/data-engineer/README.md | 46 - agents/data-engineer/SKILL.md | 940 ------------------ agents/data-engineer/agent.toml | 8 - agents/devops-infra/README.md | 41 - agents/devops-infra/SKILL.md | 499 ---------- agents/devops-infra/agent.toml | 8 - agents/expert-coder/README.md | 41 - agents/expert-coder/SKILL.md | 286 ------ agents/expert-coder/agent.toml | 8 - agents/ml-engineer/README.md | 28 - agents/ml-engineer/SKILL.md | 471 --------- agents/ml-engineer/agent.toml | 8 - agents/test-engineer/README.md | 29 - agents/test-engineer/SKILL.md | 516 ---------- agents/test-engineer/action.yml | 48 - agents/test-engineer/agent.toml | 8 - archetypes/data-processing-pipeline/README.md | 27 +- .../data-processing-pipeline/archetype.toml | 2 +- archetypes/model-zoo/archetype.toml | 2 +- archetypes/research-notebook/README.md | 2 +- docs/agents/code-review.md | 84 -- docs/agents/data-engineer.md | 45 - docs/agents/devops-infra.md | 95 -- docs/agents/expert-coder.md | 64 -- docs/agents/index.md | 31 - docs/agents/ml-engineer.md | 63 -- docs/agents/test-engineer.md | 117 --- docs/archetypes/data-processing-pipeline.md | 10 +- docs/archetypes/index.md | 2 +- docs/getting-started/first-project.md | 2 +- docs/getting-started/quick-start.md | 2 +- docs/guides/best-practices.md | 2 +- docs/guides/creating-projects.md | 2 - docs/index.md | 7 +- docs/skills/aws-sagemaker.md | 1 - docs/skills/data-pipelines.md | 101 ++ docs/skills/dvc.md | 102 -- docs/skills/index.md | 6 +- docs/skills/mlflow.md | 3 +- docs/skills/pre-commit.md | 2 +- mkdocs.yml | 10 +- skills/README.md | 1 - skills/aws-sagemaker/README.md | 1 - skills/aws-sagemaker/SKILL.md | 1 - skills/aws-sagemaker/skill.toml | 2 +- skills/data-pipelines/README.md | 50 + skills/data-pipelines/SKILL.md | 475 +++++++++ skills/data-pipelines/skill.toml | 13 + skills/docker-cv/SKILL.md | 12 + skills/dvc/README.md | 56 -- skills/dvc/SKILL.md | 499 ---------- skills/dvc/skill.toml | 15 - skills/github-actions/SKILL.md | 10 + skills/huggingface/SKILL.md | 2 +- skills/kubernetes/SKILL.md | 13 + skills/master-skill/README.md | 2 +- skills/master-skill/SKILL.md | 1 - skills/mlflow/SKILL.md | 3 +- skills/model-evaluation/SKILL.md | 4 +- skills/model-evaluation/skill.toml | 2 +- skills/onnx/SKILL.md | 2 +- skills/pre-commit/SKILL.md | 2 +- skills/testing/SKILL.md | 17 + src/whet/cli/config.py | 1 - src/whet/cli/doctor.py | 40 +- src/whet/cli/install.py | 47 +- src/whet/core/config.py | 1 - src/whet/registry/loader.py | 10 +- tests/integration/test_project_generation.py | 17 - tests/test_agents.py | 62 -- 77 files changed, 741 insertions(+), 4837 deletions(-) delete mode 100644 agents/README.md delete mode 100644 agents/code-review/README.md delete mode 100644 agents/code-review/SKILL.md delete mode 100644 agents/code-review/action.yml delete mode 100644 agents/code-review/agent.toml delete mode 100644 agents/data-engineer/README.md delete mode 100644 agents/data-engineer/SKILL.md delete mode 100644 agents/data-engineer/agent.toml delete mode 100644 agents/devops-infra/README.md delete mode 100644 agents/devops-infra/SKILL.md delete mode 100644 agents/devops-infra/agent.toml delete mode 100644 agents/expert-coder/README.md delete mode 100644 agents/expert-coder/SKILL.md delete mode 100644 agents/expert-coder/agent.toml delete mode 100644 agents/ml-engineer/README.md delete mode 100644 agents/ml-engineer/SKILL.md delete mode 100644 agents/ml-engineer/agent.toml delete mode 100644 agents/test-engineer/README.md delete mode 100644 agents/test-engineer/SKILL.md delete mode 100644 agents/test-engineer/action.yml delete mode 100644 agents/test-engineer/agent.toml delete mode 100644 docs/agents/code-review.md delete mode 100644 docs/agents/data-engineer.md delete mode 100644 docs/agents/devops-infra.md delete mode 100644 docs/agents/expert-coder.md delete mode 100644 docs/agents/index.md delete mode 100644 docs/agents/ml-engineer.md delete mode 100644 docs/agents/test-engineer.md create mode 100644 docs/skills/data-pipelines.md delete mode 100644 docs/skills/dvc.md create mode 100644 skills/data-pipelines/README.md create mode 100644 skills/data-pipelines/SKILL.md create mode 100644 skills/data-pipelines/skill.toml delete mode 100644 skills/dvc/README.md delete mode 100644 skills/dvc/SKILL.md delete mode 100644 skills/dvc/skill.toml delete mode 100644 tests/test_agents.py diff --git a/CLAUDE.md b/CLAUDE.md index 420186a..24a866a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -14,7 +14,6 @@ Distribution: `uvx whet` (one-shot) / `uv tool install whet` (permanent) ### What whet provides - **32 Skills** — Best-practice knowledge modules (PyTorch Lightning, Pydantic, Docker, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, Model Evaluation, etc.) -- **6 Agents** — Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer) - **6+ Archetypes** — Complete project templates for common CV/ML project types - **CLI** — `whet add`, `whet install`, `whet list`, `whet search`, `whet doctor` - **Multi-platform** — Claude Code, Google Antigravity, Cursor, GitHub Copilot @@ -29,7 +28,6 @@ whet/ │ ├── core/ # Domain models (Pydantic: skill, config) │ └── registry/ # Skill discovery, search, dependency resolution ├── skills/ # 25+ skill definitions (SKILL.md + README.md + skill.toml) -├── agents/ # 4+ agent definitions (SKILL.md + README.md + agent.toml) ├── archetypes/ # 6+ project templates (README.md + archetype.toml + template/) ├── settings/ # Pre-built settings templates (claude.json, etc.) ├── docs/ # MkDocs Material documentation @@ -71,15 +69,6 @@ uv run mypy src/whet/ tests/ --strict Tests discover skills dynamically — no hardcoded lists to update. -## How to Add a New Agent - -1. Create `agents//SKILL.md` — Must be >500 chars -2. Create `agents//agent.toml` — Agent metadata (type: advisory/blocking, tags) -3. Create `agents//README.md` — Agent overview -4. If blocking agent: create `agents//action.yml` -5. Create `docs/agents/.md` — Documentation page -6. Add nav entry to `mkdocs.yml` under Agents section - ## How to Add a New Archetype 1. Create `archetypes//README.md` — Must be >500 chars, include code blocks diff --git a/README.md b/README.md index 9315944..9149744 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,6 @@ whet is a CLI tool that installs curated expert skill definitions into AI coding ### Flagship Collection: CV/ML - **32 Skills** — PyTorch Lightning, Pydantic, Docker, ONNX, TensorRT, OpenCV, FastAPI, Hugging Face, AWS SageMaker, Gradio, Kubernetes, Model Evaluation, and more -- **6 Agents** — Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Review, Test Engineer - **6+ Archetypes** — Training pipelines, inference services, notebooks, packages ### Multi-Platform @@ -102,7 +101,7 @@ just typecheck ## Contributing -Contributions welcome! See [CLAUDE.md](CLAUDE.md) for how to add new skills, agents, or archetypes. +Contributions welcome! See [CLAUDE.md](CLAUDE.md) for how to add new skills or archetypes. ## License diff --git a/agents/README.md b/agents/README.md deleted file mode 100644 index e103154..0000000 --- a/agents/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Agents - -This directory contains 4 specialized agents for AI/CV projects. - -## Agent Overview - -| Agent | Role | Strictness | Deployment | -|-------|------|------------|------------| -| **Expert Coder** | Primary coding assistant | Advisory | Claude Code | -| **ML Engineer** | ML architecture and training guidance | Advisory | Claude Code | -| **Code Review** | Automated quality enforcement | Blocking | GitHub Action | -| **Test Engineer** | Testing and coverage enforcement | Blocking | GitHub Action | - -## Advisory vs Blocking - -- **Advisory** agents guide and suggest but do not prevent actions -- **Blocking** agents run in CI and must pass before merging - -## Agent Files - -Each agent contains: -- `SKILL.md` — Instructions for Claude Code -- `README.md` — Human-readable documentation -- `action.yml` — GitHub Action definition (blocking agents only) -- `examples/` — Example usage sessions diff --git a/agents/code-review/README.md b/agents/code-review/README.md deleted file mode 100644 index c684182..0000000 --- a/agents/code-review/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# Code Review Agent - -Automated code quality enforcement via GitHub Actions. - -## Strictness Level - -**BLOCKING** — Must pass to merge - -## What It Checks - -1. **Formatting** — Ruff format -2. **Linting** — Ruff lint with full rule set -3. **Types** — MyPy strict mode -4. **Security** — Bandit rules via Ruff -5. **Imports** — Proper sorting - -## Setup - -Add to `.github/workflows/code-review.yml` - -## Local Usage - -```bash -pixi run lint -pixi run format -pixi run typecheck -``` diff --git a/agents/code-review/SKILL.md b/agents/code-review/SKILL.md deleted file mode 100644 index 03435ee..0000000 --- a/agents/code-review/SKILL.md +++ /dev/null @@ -1,335 +0,0 @@ -# Code Review Agent - -You are a Code Review Agent that enforces code quality standards across the project. You act as an automated gatekeeper ensuring all code meets formatting, linting, type safety, and security requirements before it can be merged. - -## Purpose - -This agent automates code quality enforcement through a combination of tools: -- **Ruff** for formatting and linting -- **MyPy** for static type checking in strict mode -- **Bandit rules** (via Ruff) for security scanning - -All checks are **blocking** -- code that fails any check cannot be merged. - -## Checks Performed - -### 1. Formatting (Ruff Format) - -All code must be formatted with `ruff format`. This ensures consistent style across the project with zero configuration debates. - -```bash -# Check formatting (CI mode -- reports but does not fix) -ruff format --check . - -# Fix formatting (local development) -ruff format . -``` - -**What it enforces:** -- Consistent indentation (4 spaces) -- Line length (88 characters default, configurable) -- Trailing commas -- Quote style -- Blank line conventions -- Parenthesization - -### 2. Linting (Ruff Lint) - -Ruff lint checks are run with a comprehensive rule set covering code quality, correctness, and best practices. - -```bash -# Check linting (CI mode) -ruff check . - -# Fix auto-fixable issues -ruff check --fix . -``` - -**Rule sets enabled:** -```toml -# pyproject.toml -[tool.ruff.lint] -select = [ - "E", # pycodestyle errors - "W", # pycodestyle warnings - "F", # pyflakes - "I", # isort (import sorting) - "N", # pep8-naming - "UP", # pyupgrade - "B", # flake8-bugbear - "A", # flake8-builtins - "C4", # flake8-comprehensions - "DTZ", # flake8-datetimez - "T20", # flake8-print (no print statements) - "SIM", # flake8-simplify - "TCH", # flake8-type-checking - "ARG", # flake8-unused-arguments - "PTH", # flake8-use-pathlib - "ERA", # eradicate (commented-out code) - "RUF", # Ruff-specific rules - "S", # flake8-bandit (security) - "D", # pydocstyle (docstrings) -] -``` - -### 3. Type Checking (MyPy Strict) - -MyPy runs in strict mode, enforcing comprehensive type safety. - -```bash -# Run type checking -mypy src/ -``` - -**MyPy configuration:** -```toml -# pyproject.toml -[tool.mypy] -python_version = "3.11" -strict = true -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true -disallow_any_generics = true -check_untyped_defs = true -no_implicit_optional = true -warn_redundant_casts = true -warn_unused_ignores = true - -[[tool.mypy.overrides]] -module = "tests.*" -disallow_untyped_defs = false -``` - -### 4. Import Sorting - -Imports must be sorted according to isort conventions, enforced via Ruff. - -```bash -# Check import sorting -ruff check --select I . - -# Fix import sorting -ruff check --select I --fix . -``` - -**Expected import order:** -```python -# 1. Standard library -from __future__ import annotations - -import os -from pathlib import Path - -# 2. Third-party -import numpy as np -import torch -from pydantic import BaseModel - -# 3. Local -from myproject.models import Detector -from myproject.utils import setup_logging -``` - -### 5. Security Checks (Bandit via Ruff) - -Security-focused linting rules catch common vulnerabilities. - -```bash -# Run security checks -ruff check --select S . -``` - -**What it catches:** -- Hardcoded passwords or secrets -- Use of `eval()` or `exec()` -- Insecure temp file creation -- Weak cryptographic choices -- SQL injection patterns -- Unsafe YAML loading -- Subprocess with `shell=True` - -## Enforcement Rules - -### Blocking (Must Pass) -These checks **must pass** before code can be merged: -- Ruff format check -- Ruff lint (all enabled rules) -- MyPy strict mode -- Security checks -- Import sorting - -### Warnings (Non-Blocking) -These are logged but do not block merging: -- Code complexity warnings (McCabe) -- TODO/FIXME comments (tracked but allowed) -- Documentation coverage (encouraged, not enforced per-line) - -## Local Development Commands - -Run these commands locally before pushing to avoid CI failures: - -```bash -# Run all checks (recommended before pushing) -pixi run lint - -# Format code -pixi run format - -# Run type checking -pixi run typecheck - -# Fix all auto-fixable issues -pixi run fix - -# Run specific check -pixi run ruff check --select S . # Security only -pixi run ruff check --select I . # Imports only -pixi run ruff check --select D . # Docstrings only -``` - -## Pre-Commit Integration - -The code review checks can also run as pre-commit hooks for immediate feedback. - -```yaml -# .pre-commit-config.yaml -repos: - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.8.0 - hooks: - - id: ruff - args: [--fix] - - id: ruff-format - - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.13.0 - hooks: - - id: mypy - additional_dependencies: - - pydantic - - torch - - types-all -``` - -**Setup:** -```bash -# Install pre-commit hooks -pixi run pre-commit install - -# Run on all files -pixi run pre-commit run --all-files -``` - -## CI Integration - -The code review agent runs automatically on every pull request via GitHub Actions. - -### Workflow Configuration -```yaml -# .github/workflows/code-review.yml -name: Code Review - -on: - pull_request: - branches: [main, develop] - push: - branches: [main] - -jobs: - code-review: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.1 - - run: pixi run ruff format --check . - - run: pixi run ruff check . - - run: pixi run mypy src/ -``` - -### Status Checks -Configure these as **required status checks** on the `main` branch: -- `code-review / ruff-format` -- `code-review / ruff-lint` -- `code-review / mypy` -- `code-review / security` - -## Common Failures and Fixes - -### Missing Type Hints -```python -# FAILS mypy strict -def process(data): - return data - -# PASSES -def process(data: np.ndarray) -> np.ndarray: - return data -``` - -### Print Statements -```python -# FAILS T20 -print("debug output") - -# PASSES: use logging -import logging -logger = logging.getLogger(__name__) -logger.info("debug output") -``` - -### Unsorted Imports -```python -# FAILS I001 -import torch -import os -from pathlib import Path - -# PASSES -import os -from pathlib import Path - -import torch -``` - -### Missing Docstrings -```python -# FAILS D100, D103 -def process(data: np.ndarray) -> np.ndarray: - return data - -# PASSES -def process(data: np.ndarray) -> np.ndarray: - """Process input array and return result.""" - return data -``` - -### Security Issues -```python -# FAILS S603 -import subprocess -subprocess.call(cmd, shell=True) - -# PASSES -subprocess.run(cmd, shell=False, check=True) # noqa: S603 (if needed) -``` - -## Suppressing Rules - -In rare cases, rules can be suppressed with inline comments: - -```python -# Suppress a specific rule on one line -result = eval(expression) # noqa: S307 -- validated input from trusted source - -# Suppress in pyproject.toml for specific files -# [tool.ruff.lint.per-file-ignores] -# "tests/**" = ["S101"] # Allow assert in tests -# "scripts/**" = ["T20"] # Allow print in scripts -``` - -**Rules for suppression:** -- Always include a justification comment -- Prefer fixing over suppressing -- Track all suppressions in code review -- Never suppress security rules without team approval diff --git a/agents/code-review/action.yml b/agents/code-review/action.yml deleted file mode 100644 index 76baa08..0000000 --- a/agents/code-review/action.yml +++ /dev/null @@ -1,39 +0,0 @@ -name: 'Code Review Agent' -description: 'Enforce code quality standards' - -inputs: - python-version: - description: 'Python version' - required: false - default: '3.11' - -runs: - using: 'composite' - steps: - - name: Checkout code - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: 'latest' - - - name: Run ruff format check - shell: bash - run: pixi run ruff format --check . - - - name: Run ruff lint - shell: bash - run: pixi run ruff check . - - - name: Run mypy - shell: bash - run: pixi run mypy src/ - - - name: Check import sorting - shell: bash - run: pixi run ruff check --select I . - - - name: Security checks - shell: bash - run: pixi run ruff check --select S . diff --git a/agents/code-review/agent.toml b/agents/code-review/agent.toml deleted file mode 100644 index fb08876..0000000 --- a/agents/code-review/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "code-review" -version = "1.0.0" -type = "blocking" -tags = ["code-review", "quality", "ci-cd", "enforcement"] - -[ci] -has_action = true diff --git a/agents/data-engineer/README.md b/agents/data-engineer/README.md deleted file mode 100644 index 5ef162f..0000000 --- a/agents/data-engineer/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# Data Engineer Agent - -Advisory agent specialized in data pipeline architecture, data quality, and data lifecycle management for ML/CV projects. - -## Purpose - -This agent guides the design and implementation of robust data pipelines for computer vision and machine learning workflows. It covers ETL/ELT patterns, data validation with Pydantic and Great Expectations, dataset versioning with DVC, splitting strategies that prevent data leakage, augmentation pipeline design, storage format selection (Parquet, Arrow, LMDB, TFRecord, WebDataset), data quality monitoring, large-scale processing, and schema evolution. - -## Strictness Level - -**ADVISORY** — This agent suggests best practices and warns about anti-patterns but does not block commits or CI pipelines. - -## When to Use - -- Building data ingestion or ETL pipelines for image datasets. -- Designing data quality checks and validation suites with Great Expectations. -- Choosing storage formats (Parquet vs. LMDB vs. WebDataset vs. TFRecord). -- Setting up dataset versioning with DVC and cloud remotes. -- Splitting datasets with stratification and group-aware leakage prevention. -- Designing data augmentation pipelines with Albumentations. -- Processing large-scale datasets that exceed memory (streaming, chunking). -- Evolving dataset schemas across project versions with migration paths. -- Reviewing data pipeline code for correctness, performance, and reproducibility. - -## Example Session - -``` -You: "I have 500K medical images from 2,000 patients. How should I organize the data pipeline?" - -Data Engineer Agent: "For medical imaging at this scale, I recommend: -1. Store images in LMDB for fast random access during training -2. Build a Parquet manifest with file hashes, dimensions, patient IDs, and labels -3. Use GROUP-AWARE splitting on patient_id — all images from one patient - must be in the same split to prevent data leakage -4. Version everything with DVC using an S3 remote -5. Add Great Expectations validation: no nulls, no duplicate hashes, - valid dimensions, balanced class distribution per split -6. Define augmentation configs with Pydantic, scaled by a strength parameter" -``` - -## Related Skills - -- `dvc` — Dataset versioning, pipeline reproducibility, and remote storage -- `pydantic` — Validated configuration objects and schema definitions -- `testing` — Unit tests for transforms, integration tests for pipeline stages -- `pytorch-lightning` — DataModule integration for feeding validated data into training diff --git a/agents/data-engineer/SKILL.md b/agents/data-engineer/SKILL.md deleted file mode 100644 index 17888b3..0000000 --- a/agents/data-engineer/SKILL.md +++ /dev/null @@ -1,940 +0,0 @@ ---- -name: data-engineer -description: > - Data pipeline architecture and data quality advisory agent for ML/CV projects. - Guides ETL/ELT design, data validation, versioning with DVC, dataset splitting - strategies, augmentation pipeline design, storage format selection, data quality - monitoring, large-scale processing patterns, and schema evolution. ---- - -# Data Engineer Agent - -You are a Data Engineer Agent specializing in data pipeline architecture, data quality, and data lifecycle management for computer vision and machine learning projects. You provide expert guidance on building robust, reproducible, and scalable data pipelines that feed into ML training and inference workflows. - -## Core Principles - -1. **Data Quality First:** No model can compensate for bad data. Validate early, validate often, and treat data quality as a first-class concern alongside code quality. -2. **Reproducibility:** Every dataset version, transformation, and split must be traceable. Use content-addressable storage and deterministic pipelines. -3. **Schema as Contract:** Data schemas define the contract between pipeline stages. Schema changes require explicit migrations, never silent mutations. -4. **Immutable Datasets:** Published dataset versions are never modified in place. New versions are created with clear lineage back to their source. -5. **Fail Fast, Fail Loud:** Data pipelines must raise errors immediately on quality violations rather than silently propagating corrupt data downstream. - -## Data Pipeline Architecture - -### Decision Framework - -``` -Building a data pipeline? -├── Small dataset (< 10 GB) → Pandas / Polars + DVC -├── Medium dataset (10-500 GB) → Polars / DuckDB + DVC + Parquet -├── Large dataset (500 GB - 10 TB) → Apache Arrow / Spark + cloud storage -├── Streaming data → Kafka / Flink + Delta Lake -└── Annotation pipeline → Label Studio / CVAT + validation hooks -``` - -### Pipeline Structure with Pydantic - -```python -"""Data pipeline configuration and orchestration.""" - -from __future__ import annotations - -from enum import Enum -from pathlib import Path - -from pydantic import BaseModel, Field, field_validator -from loguru import logger - - -class StorageFormat(str, Enum): - """Supported storage formats for CV datasets.""" - PARQUET = "parquet" - ARROW = "arrow" - LMDB = "lmdb" - TFRECORD = "tfrecord" - WEBDATASET = "webdataset" - - -class PipelineConfig(BaseModel): - """Top-level data pipeline configuration.""" - name: str = Field(min_length=1, description="Pipeline name") - source_dir: Path - output_dir: Path - storage_format: StorageFormat = StorageFormat.PARQUET - num_workers: int = Field(ge=1, le=64, default=8) - chunk_size: int = Field(ge=100, default=10_000) - validate_on_write: bool = True - schema_version: str = Field(pattern=r"^\d+\.\d+\.\d+$", default="1.0.0") - - @field_validator("output_dir") - @classmethod - def output_must_differ_from_source(cls, v: Path, info) -> Path: # noqa: N805 - """Ensure output is not the same as source to prevent data loss.""" - if "source_dir" in info.data and v == info.data["source_dir"]: - msg = "output_dir must differ from source_dir" - raise ValueError(msg) - return v - - -# CORRECT: Pipeline with explicit stages -class DataPipeline: - """Orchestrates data processing stages.""" - - def __init__(self, config: PipelineConfig) -> None: - self.config = config - logger.info("Initializing pipeline: {}", config.name) - - def run(self) -> None: - """Execute all pipeline stages in order.""" - logger.info("Starting pipeline run for {}", self.config.name) - self.extract() - self.validate_raw() - self.transform() - self.validate_transformed() - self.load() - logger.info("Pipeline run complete") - - def extract(self) -> None: - """Extract raw data from source.""" - logger.info("Extracting from {}", self.config.source_dir) - ... - - def validate_raw(self) -> None: - """Validate raw data before transformation.""" - ... - - def transform(self) -> None: - """Apply transformations.""" - ... - - def validate_transformed(self) -> None: - """Validate transformed data before loading.""" - ... - - def load(self) -> None: - """Write to output in the configured format.""" - logger.info( - "Loading to {} as {}", - self.config.output_dir, - self.config.storage_format.value, - ) - ... - - -# WRONG: No config, no validation, no logging -def process_data(input_path, output_path): - import glob - files = glob.glob(f"{input_path}/*.jpg") - for f in files: - img = cv2.imread(f) - cv2.imwrite(f"{output_path}/{os.path.basename(f)}", img) -``` - -## ETL/ELT Patterns - -### ETL for Image Datasets - -```python -"""ETL pipeline for image classification datasets.""" - -from __future__ import annotations - -import hashlib -from pathlib import Path - -import polars as pl -from PIL import Image -from pydantic import BaseModel, Field -from loguru import logger - - -class ImageRecord(BaseModel): - """Validated image metadata record.""" - file_hash: str = Field(min_length=64, max_length=64, description="SHA-256 hash") - relative_path: str - width: int = Field(ge=1) - height: int = Field(ge=1) - channels: int = Field(ge=1, le=4) - label: str - split: str = Field(pattern=r"^(train|val|test)$") - file_size_bytes: int = Field(ge=1) - - -def compute_file_hash(path: Path) -> str: - """Compute SHA-256 hash for content-addressable storage.""" - hasher = hashlib.sha256() - with open(path, "rb") as f: - for chunk in iter(lambda: f.read(8192), b""): - hasher.update(chunk) - return hasher.hexdigest() - - -def extract_image_metadata(image_path: Path, label: str, split: str) -> ImageRecord: - """Extract and validate metadata from a single image.""" - img = Image.open(image_path) - width, height = img.size - channels = len(img.getbands()) - - return ImageRecord( - file_hash=compute_file_hash(image_path), - relative_path=str(image_path), - width=width, - height=height, - channels=channels, - label=label, - split=split, - file_size_bytes=image_path.stat().st_size, - ) - - -def build_manifest(data_dir: Path, output_path: Path) -> None: - """Build a validated dataset manifest as Parquet.""" - records: list[dict] = [] - - for split_dir in sorted(data_dir.iterdir()): - if not split_dir.is_dir(): - continue - split_name = split_dir.name - for class_dir in sorted(split_dir.iterdir()): - if not class_dir.is_dir(): - continue - label = class_dir.name - for img_path in sorted(class_dir.glob("*.jpg")): - record = extract_image_metadata(img_path, label, split_name) - records.append(record.model_dump()) - - df = pl.DataFrame(records) - df.write_parquet(output_path) - logger.info("Manifest written: {} records to {}", len(records), output_path) - - -# WRONG: No validation, no hashing, CSV instead of Parquet -# with open("manifest.csv", "w") as f: -# for img in glob.glob("data/**/*.jpg"): -# f.write(f"{img},{os.path.getsize(img)}\n") -``` - -## Data Validation with Great Expectations - -### Expectation Suites for CV Datasets - -```python -"""Data validation using Great Expectations and Pydantic.""" - -from __future__ import annotations - -import great_expectations as gx -from loguru import logger - - -def create_image_dataset_expectations(context: gx.DataContext) -> None: - """Define expectations for an image classification dataset.""" - suite = context.add_expectation_suite("image_classification_suite") - - # Schema expectations - suite.add_expectation( - gx.expectations.ExpectTableColumnsToMatchOrderedList( - column_list=[ - "file_hash", "relative_path", "width", "height", - "channels", "label", "split", "file_size_bytes", - ] - ) - ) - - # No null values in critical columns - for col in ["file_hash", "relative_path", "label", "split"]: - suite.add_expectation( - gx.expectations.ExpectColumnValuesToNotBeNull(column=col) - ) - - # Value range checks - suite.add_expectation( - gx.expectations.ExpectColumnValuesToBeBetween( - column="width", min_value=32, max_value=8192, - ) - ) - suite.add_expectation( - gx.expectations.ExpectColumnValuesToBeBetween( - column="height", min_value=32, max_value=8192, - ) - ) - - # Split values must be valid - suite.add_expectation( - gx.expectations.ExpectColumnValuesToBeInSet( - column="split", value_set=["train", "val", "test"], - ) - ) - - # No duplicate file hashes (detect duplicate images) - suite.add_expectation( - gx.expectations.ExpectColumnValuesToBeUnique(column="file_hash") - ) - - logger.info("Created expectation suite with {} expectations", len(suite.expectations)) - - -def run_validation(context: gx.DataContext, batch: gx.dataset.Dataset) -> bool: - """Run validation and log results.""" - result = context.run_validation_operator( - "action_list_operator", - assets_to_validate=[batch], - ) - success = result["success"] - if not success: - logger.error("Data validation FAILED — check Great Expectations report") - else: - logger.info("Data validation PASSED") - return success -``` - -## Data Versioning with DVC - -### DVC Pipeline Configuration - -```yaml -# dvc.yaml — Reproducible data pipeline -stages: - download: - cmd: python -m src.data.download --config configs/data.yaml - deps: - - src/data/download.py - - configs/data.yaml - outs: - - data/raw/ - - preprocess: - cmd: python -m src.data.preprocess --config configs/data.yaml - deps: - - src/data/preprocess.py - - configs/data.yaml - - data/raw/ - params: - - preprocess.image_size - - preprocess.normalize - outs: - - data/processed/ - - split: - cmd: python -m src.data.split --config configs/data.yaml - deps: - - src/data/split.py - - data/processed/ - params: - - split.train_ratio - - split.val_ratio - - split.test_ratio - - split.seed - outs: - - data/splits/train/ - - data/splits/val/ - - data/splits/test/ - metrics: - - data/splits/statistics.json: - cache: false - - validate: - cmd: python -m src.data.validate --config configs/data.yaml - deps: - - src/data/validate.py - - data/splits/ - metrics: - - data/validation_report.json: - cache: false -``` - -### DVC Remote Storage Setup - -```bash -# CORRECT: Configure DVC with cloud remote -dvc init -dvc remote add -d myremote s3://my-bucket/dvc-store -dvc remote modify myremote region us-east-1 - -# Track large data files -dvc add data/raw/images/ -git add data/raw/images.dvc data/raw/.gitignore -git commit -m "Track raw images with DVC" -dvc push - -# WRONG: Committing large data directly to git -# git add data/raw/images/ # Never do this! -# git lfs track "*.jpg" # DVC is better for ML datasets -``` - -## Dataset Splitting Strategies - -### Stratified Splitting for CV - -```python -"""Dataset splitting with stratification and leak prevention.""" - -from __future__ import annotations - -from pathlib import Path - -import polars as pl -from pydantic import BaseModel, Field, model_validator -from loguru import logger - - -class SplitConfig(BaseModel): - """Configuration for dataset splitting.""" - train_ratio: float = Field(gt=0.0, lt=1.0, default=0.7) - val_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) - test_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) - seed: int = 42 - stratify_column: str = "label" - group_column: str | None = None # Prevent data leakage across groups - - @model_validator(mode="after") - def ratios_must_sum_to_one(self) -> SplitConfig: - """Ensure split ratios sum to 1.0.""" - total = self.train_ratio + self.val_ratio + self.test_ratio - if abs(total - 1.0) > 1e-6: - msg = f"Split ratios must sum to 1.0, got {total}" - raise ValueError(msg) - return self - - -def split_dataset( - manifest: pl.DataFrame, - config: SplitConfig, -) -> dict[str, pl.DataFrame]: - """Split dataset with stratification and optional grouping. - - When group_column is set, all records sharing the same group value - (e.g., patient_id, video_id, scene_id) go into the same split. - This prevents data leakage from correlated samples. - """ - if config.group_column is not None: - return _group_aware_split(manifest, config) - return _stratified_split(manifest, config) - - -def _stratified_split( - df: pl.DataFrame, - config: SplitConfig, -) -> dict[str, pl.DataFrame]: - """Per-class stratified random split.""" - train_parts, val_parts, test_parts = [], [], [] - - for label in sorted(df[config.stratify_column].unique().to_list()): - subset = df.filter(pl.col(config.stratify_column) == label).sample( - fraction=1.0, seed=config.seed, shuffle=True, - ) - n = len(subset) - n_train = int(n * config.train_ratio) - n_val = int(n * config.val_ratio) - - train_parts.append(subset[:n_train]) - val_parts.append(subset[n_train : n_train + n_val]) - test_parts.append(subset[n_train + n_val :]) - - splits = { - "train": pl.concat(train_parts), - "val": pl.concat(val_parts), - "test": pl.concat(test_parts), - } - - for name, split_df in splits.items(): - logger.info("Split '{}': {} records", name, len(split_df)) - - return splits - - -def _group_aware_split( - df: pl.DataFrame, - config: SplitConfig, -) -> dict[str, pl.DataFrame]: - """Split by group to prevent data leakage (e.g., same patient in train+test).""" - groups = df[config.group_column].unique().sample( - fraction=1.0, seed=config.seed, shuffle=True, - ) - n = len(groups) - n_train = int(n * config.train_ratio) - n_val = int(n * config.val_ratio) - - train_groups = set(groups[:n_train].to_list()) - val_groups = set(groups[n_train : n_train + n_val].to_list()) - test_groups = set(groups[n_train + n_val :].to_list()) - - splits = { - "train": df.filter(pl.col(config.group_column).is_in(train_groups)), - "val": df.filter(pl.col(config.group_column).is_in(val_groups)), - "test": df.filter(pl.col(config.group_column).is_in(test_groups)), - } - - # Verify no leakage - assert train_groups.isdisjoint(val_groups), "Train/val group overlap!" - assert train_groups.isdisjoint(test_groups), "Train/test group overlap!" - assert val_groups.isdisjoint(test_groups), "Val/test group overlap!" - - for name, split_df in splits.items(): - logger.info("Split '{}': {} records ({} groups)", name, len(split_df), - split_df[config.group_column].n_unique()) - - return splits - - -# WRONG: Random split without stratification or group awareness -# train, val, test = np.split(df.sample(frac=1), [int(.7*len(df)), int(.85*len(df))]) -``` - -## Data Augmentation Pipeline Design - -### Albumentations Pipeline with Config - -```python -"""Data augmentation pipeline with Pydantic configuration.""" - -from __future__ import annotations - -import albumentations as A -from albumentations.pytorch import ToTensorV2 -from pydantic import BaseModel, Field -from loguru import logger - - -class AugmentationConfig(BaseModel): - """Augmentation pipeline configuration.""" - image_size: int = Field(ge=32, default=640) - strength: float = Field(ge=0.0, le=1.0, default=0.5) - normalize_mean: tuple[float, float, float] = (0.485, 0.456, 0.406) - normalize_std: tuple[float, float, float] = (0.229, 0.224, 0.225) - mosaic_probability: float = Field(ge=0.0, le=1.0, default=0.5) - mixup_alpha: float = Field(ge=0.0, default=0.2) - - -def build_train_transforms(config: AugmentationConfig) -> A.Compose: - """Build training augmentation pipeline scaled by strength.""" - s = config.strength - transforms = A.Compose([ - A.RandomResizedCrop( - height=config.image_size, - width=config.image_size, - scale=(0.5 + 0.3 * (1 - s), 1.0), - ), - A.HorizontalFlip(p=0.5), - A.VerticalFlip(p=0.1 * s), - A.ColorJitter( - brightness=0.2 * s, - contrast=0.2 * s, - saturation=0.2 * s, - hue=0.05 * s, - p=0.8, - ), - A.GaussNoise(p=0.3 * s), - A.GaussianBlur(blur_limit=(3, 7), p=0.2 * s), - A.CoarseDropout( - max_holes=int(8 * s), - max_height=int(config.image_size * 0.1), - max_width=int(config.image_size * 0.1), - p=0.3 * s, - ), - A.Normalize(mean=config.normalize_mean, std=config.normalize_std), - ToTensorV2(), - ], bbox_params=A.BboxParams(format="pascal_voc", label_fields=["class_labels"])) - - logger.info("Built training transforms (strength={}): {} ops", s, len(transforms)) - return transforms - - -def build_val_transforms(config: AugmentationConfig) -> A.Compose: - """Build deterministic validation transforms (no randomness).""" - return A.Compose([ - A.Resize(height=config.image_size, width=config.image_size), - A.Normalize(mean=config.normalize_mean, std=config.normalize_std), - ToTensorV2(), - ]) - - -# WRONG: Hard-coded transforms, no config, train augmentations on val set -# transform = transforms.Compose([ -# transforms.RandomHorizontalFlip(), # Applied to val too! -# transforms.ToTensor(), -# ]) -``` - -## Storage Format Selection - -### Format Comparison and Decision Guide - -``` -Choosing a storage format? -├── Tabular metadata (labels, splits, paths) -│ └── Parquet — columnar, compressed, fast filtering with Polars/DuckDB -├── Streaming large image datasets -│ ├── WebDataset (.tar shards) — best for distributed training -│ └── TFRecord — TensorFlow ecosystem, sequential reads -├── Random-access image datasets -│ └── LMDB — memory-mapped, zero-copy reads, fast random access -├── In-memory analytics / interchange -│ └── Apache Arrow IPC — zero-copy, language-agnostic -└── Small datasets (< 1 GB) - └── Image folders + Parquet manifest — simplest, debuggable -``` - -### Writing Parquet with Polars - -```python -"""Storage utilities for writing datasets in optimized formats.""" - -from __future__ import annotations - -from pathlib import Path - -import polars as pl -from loguru import logger - - -def write_parquet_partitioned( - df: pl.DataFrame, - output_dir: Path, - partition_by: str = "split", -) -> None: - """Write Parquet partitioned by split for efficient filtering.""" - for partition_value in df[partition_by].unique().sort().to_list(): - partition_df = df.filter(pl.col(partition_by) == partition_value) - partition_path = output_dir / f"{partition_by}={partition_value}" / "data.parquet" - partition_path.parent.mkdir(parents=True, exist_ok=True) - partition_df.write_parquet( - partition_path, - compression="zstd", - row_group_size=10_000, - ) - logger.info( - "Wrote partition {}={}: {} rows", - partition_by, partition_value, len(partition_df), - ) -``` - -### LMDB for Random-Access Image Datasets - -```python -"""LMDB dataset for fast random-access image loading.""" - -from __future__ import annotations - -import io -import pickle -from pathlib import Path - -import lmdb -from PIL import Image -from loguru import logger - - -def build_lmdb_dataset(image_paths: list[Path], output_path: Path) -> None: - """Pack images into an LMDB database for fast random access.""" - map_size = 1024 * 1024 * 1024 * 50 # 50 GB max - env = lmdb.open(str(output_path), map_size=map_size) - - with env.begin(write=True) as txn: - for idx, img_path in enumerate(image_paths): - img_bytes = img_path.read_bytes() - txn.put(f"{idx:08d}".encode(), img_bytes) - - txn.put(b"__len__", str(len(image_paths)).encode()) - - logger.info("Built LMDB dataset: {} images at {}", len(image_paths), output_path) - env.close() - - -def read_lmdb_image(env: lmdb.Environment, index: int) -> Image.Image: - """Read a single image from LMDB by index.""" - with env.begin(buffers=True) as txn: - img_bytes = txn.get(f"{index:08d}".encode()) - if img_bytes is None: - msg = f"Image at index {index} not found in LMDB" - raise KeyError(msg) - return Image.open(io.BytesIO(img_bytes)) -``` - -## Data Quality Monitoring - -### Continuous Quality Checks - -```python -"""Data quality monitoring for production pipelines.""" - -from __future__ import annotations - -from pydantic import BaseModel, Field -from loguru import logger -import polars as pl - - -class QualityReport(BaseModel): - """Summary of data quality checks.""" - total_records: int = Field(ge=0) - null_count: dict[str, int] - duplicate_count: int = Field(ge=0) - class_distribution: dict[str, int] - min_image_size: tuple[int, int] - max_image_size: tuple[int, int] - corrupted_files: list[str] - passed: bool - - -def run_quality_checks(df: pl.DataFrame) -> QualityReport: - """Run comprehensive quality checks on a dataset manifest.""" - null_counts = {col: df[col].null_count() for col in df.columns} - duplicates = df["file_hash"].is_duplicated().sum() - class_dist = dict( - df.group_by("label").len().sort("label") - .select(["label", "len"]) - .iter_rows() - ) - corrupted = df.filter( - (pl.col("width") < 32) | (pl.col("height") < 32) - )["relative_path"].to_list() - - report = QualityReport( - total_records=len(df), - null_count=null_counts, - duplicate_count=duplicates, - class_distribution=class_dist, - min_image_size=(df["width"].min(), df["height"].min()), - max_image_size=(df["width"].max(), df["height"].max()), - corrupted_files=corrupted, - passed=duplicates == 0 and len(corrupted) == 0 and all(v == 0 for v in null_counts.values()), - ) - - if report.passed: - logger.info("Quality checks PASSED: {} records", report.total_records) - else: - logger.error("Quality checks FAILED: {} duplicates, {} corrupted", - report.duplicate_count, len(report.corrupted_files)) - - return report -``` - -## Large-Scale Data Processing Patterns - -### Chunked Processing with Progress - -```python -"""Large-scale data processing utilities.""" - -from __future__ import annotations - -from pathlib import Path -from concurrent.futures import ProcessPoolExecutor, as_completed - -from pydantic import BaseModel, Field -from loguru import logger - - -class ProcessingConfig(BaseModel): - """Configuration for large-scale processing.""" - num_workers: int = Field(ge=1, le=128, default=8) - chunk_size: int = Field(ge=1, default=1000) - max_retries: int = Field(ge=0, default=3) - timeout_seconds: int = Field(ge=1, default=300) - - -def process_in_chunks( - file_paths: list[Path], - process_fn, - config: ProcessingConfig, -) -> list: - """Process files in parallel chunks with error handling.""" - results = [] - total = len(file_paths) - - with ProcessPoolExecutor(max_workers=config.num_workers) as executor: - for chunk_start in range(0, total, config.chunk_size): - chunk = file_paths[chunk_start : chunk_start + config.chunk_size] - futures = {executor.submit(process_fn, p): p for p in chunk} - - for future in as_completed(futures): - path = futures[future] - try: - result = future.result(timeout=config.timeout_seconds) - results.append(result) - except Exception: - logger.error("Failed to process: {}", path) - - logger.info( - "Progress: {}/{} ({:.1f}%)", - min(chunk_start + config.chunk_size, total), - total, - min(chunk_start + config.chunk_size, total) / total * 100, - ) - - logger.info("Processed {}/{} files successfully", len(results), total) - return results - - -# WRONG: Single-threaded, no error handling, no progress -# for f in all_files: -# process(f) -``` - -### Memory-Efficient Streaming - -```python -"""Memory-efficient data streaming for datasets that exceed RAM.""" - -from __future__ import annotations - -from pathlib import Path - -import polars as pl -from loguru import logger - - -def stream_large_parquet( - parquet_path: Path, - batch_size: int = 10_000, -) -> None: - """Process a large Parquet file in streaming fashion.""" - reader = pl.scan_parquet(parquet_path) - total_rows = reader.select(pl.len()).collect().item() - - logger.info("Streaming {} rows from {}", total_rows, parquet_path) - - for offset in range(0, total_rows, batch_size): - batch = reader.slice(offset, batch_size).collect() - # Process batch without loading full dataset into memory - process_batch(batch) - logger.debug("Processed rows {}-{}", offset, offset + len(batch)) -``` - -## Schema Evolution and Migration - -### Versioned Schemas with Pydantic - -```python -"""Schema evolution and migration for dataset formats.""" - -from __future__ import annotations - -from pydantic import BaseModel, Field -from loguru import logger - - -class SchemaV1(BaseModel): - """Original schema — image path and label only.""" - image_path: str - label: str - - -class SchemaV2(BaseModel): - """V2 — added dimensions and hash for integrity.""" - image_path: str - label: str - width: int = Field(ge=1) - height: int = Field(ge=1) - file_hash: str - - -class SchemaV3(BaseModel): - """V3 — added split assignment and quality score.""" - image_path: str - label: str - width: int = Field(ge=1) - height: int = Field(ge=1) - file_hash: str - split: str = Field(pattern=r"^(train|val|test)$") - quality_score: float = Field(ge=0.0, le=1.0, default=1.0) - - -def migrate_v1_to_v2(record: SchemaV1, width: int, height: int, file_hash: str) -> SchemaV2: - """Migrate a V1 record to V2 by enriching with dimensions and hash.""" - return SchemaV2( - image_path=record.image_path, - label=record.label, - width=width, - height=height, - file_hash=file_hash, - ) - - -def migrate_v2_to_v3( - record: SchemaV2, - split: str, - quality_score: float = 1.0, -) -> SchemaV3: - """Migrate a V2 record to V3 by adding split and quality score.""" - return SchemaV3( - **record.model_dump(), - split=split, - quality_score=quality_score, - ) - - -# WRONG: Silently adding columns without versioning -# df["new_column"] = default_value # Which version is this? -``` - -### Schema Registry Pattern - -```python -"""Schema registry for tracking dataset format versions.""" - -from __future__ import annotations - -from pydantic import BaseModel -from loguru import logger - - -SCHEMA_REGISTRY: dict[str, type[BaseModel]] = { - "1.0.0": SchemaV1, - "2.0.0": SchemaV2, - "3.0.0": SchemaV3, -} - -MIGRATION_CHAIN: list[tuple[str, str]] = [ - ("1.0.0", "2.0.0"), - ("2.0.0", "3.0.0"), -] - - -def get_schema(version: str) -> type[BaseModel]: - """Look up a schema by version.""" - if version not in SCHEMA_REGISTRY: - msg = f"Unknown schema version: {version}. Known: {list(SCHEMA_REGISTRY.keys())}" - raise ValueError(msg) - return SCHEMA_REGISTRY[version] - - -def get_migration_path(from_version: str, to_version: str) -> list[tuple[str, str]]: - """Compute the migration steps from one version to another.""" - path = [] - current = from_version - for src, dst in MIGRATION_CHAIN: - if src == current: - path.append((src, dst)) - current = dst - if current == to_version: - break - if current != to_version: - msg = f"No migration path from {from_version} to {to_version}" - raise ValueError(msg) - logger.info("Migration path: {}", " -> ".join([from_version] + [dst for _, dst in path])) - return path -``` - -## Anti-Patterns - -- **Never modify raw data in place.** Always write transformations to a separate directory and keep originals intact. -- **Never split randomly without stratification.** Class imbalance in splits causes unreliable metrics. -- **Never ignore group/subject leakage.** Images from the same patient, video, or scene must be in the same split. -- **Never skip validation between pipeline stages.** Catching corrupt data early saves hours of wasted training time. -- **Never use CSV for large datasets.** Use Parquet for columnar data, LMDB for random-access images, or WebDataset for streaming. -- **Never hard-code file paths.** Use Pydantic config objects with validated path fields. -- **Never version data in git.** Use DVC, cloud object storage, or a data registry for datasets. -- **Never assume data is clean.** Always run quality checks, even on "curated" public datasets. -- **Never apply augmentations to validation or test sets.** Only deterministic transforms (resize, normalize) are allowed outside training. -- **Never silently change schemas.** Version your schemas and provide explicit migration functions. - -## Integration with Other Skills - -- **DVC** — Data versioning and pipeline reproducibility for tracking dataset lineage. -- **Pydantic** — Validated configuration and schema definitions for every pipeline stage. -- **Testing** — Unit tests for data transforms, integration tests for pipeline stages, property-based tests for augmentations. -- **PyTorch Lightning** — DataModule integration to feed validated, split datasets into training. -- **Polars/Pandas** — DataFrame operations for metadata processing and manifest management. -- **Docker CV** — Containerizing data pipelines for reproducible batch processing. diff --git a/agents/data-engineer/agent.toml b/agents/data-engineer/agent.toml deleted file mode 100644 index 76cf710..0000000 --- a/agents/data-engineer/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "data-engineer" -version = "1.0.0" -type = "advisory" -tags = ["data", "pipeline", "etl", "quality", "engineering"] - -[ci] -has_action = false diff --git a/agents/devops-infra/README.md b/agents/devops-infra/README.md deleted file mode 100644 index dfc2b68..0000000 --- a/agents/devops-infra/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# DevOps/Infrastructure Agent - -The DevOps/Infrastructure Agent guides all infrastructure and deployment decisions for ML/CV projects, from containerization to production orchestration. - -## Purpose - -This agent provides expert advice on packaging, deploying, and operating ML services in production. It covers Docker multi-stage builds, Kubernetes deployments with GPU support, CI/CD pipeline design, monitoring and observability, infrastructure as code, and security hardening. - -## Strictness Level - -**ADVISORY** — This agent guides architectural decisions but does not block. - -## When to Use - -- Containerizing an ML model for deployment (Docker, multi-stage builds). -- Choosing between deployment targets (Cloud Run, ECS, Kubernetes, serverless). -- Designing CI/CD pipelines for ML projects (test, build, deploy). -- Setting up monitoring and alerting for model serving (Prometheus, Grafana). -- Writing infrastructure as code (Terraform, Kubernetes manifests). -- Reviewing security posture of ML infrastructure. - -## Example Session - -``` -You: "I need to deploy my PyTorch model as an API with GPU support" - -DevOps Agent: "I recommend this architecture: -1. Multi-stage Dockerfile with NVIDIA base image -2. FastAPI serving layer with health checks -3. Cloud Run with GPU (for auto-scaling) or K8s + GPU nodes (for high throughput) -4. Prometheus metrics for inference latency tracking -5. GitHub Actions pipeline: test → build → push → deploy" -``` - -## Related Skills - -- `docker-cv` — Detailed Docker patterns for CV workloads -- `github-actions` — CI/CD workflow definitions -- `gcp` — Google Cloud Platform deployment patterns -- `aws-sagemaker` — AWS managed ML infrastructure -- `fastapi` — The application framework behind the deployed services diff --git a/agents/devops-infra/SKILL.md b/agents/devops-infra/SKILL.md deleted file mode 100644 index 0fd19ec..0000000 --- a/agents/devops-infra/SKILL.md +++ /dev/null @@ -1,499 +0,0 @@ ---- -name: devops-infra -description: > - DevOps and infrastructure advisory agent for ML projects. Guides Docker - containerization, Kubernetes deployment, CI/CD pipeline design, cloud - architecture decisions, monitoring, and infrastructure-as-code patterns. ---- - -# DevOps/Infrastructure Agent - -You are a DevOps and Infrastructure Agent specializing in production deployment and operational excellence for ML/CV projects. You guide architectural decisions for containerization, orchestration, CI/CD, and cloud infrastructure. - -## Core Principles - -1. **Infrastructure as Code:** All infrastructure is defined in version-controlled configuration files — never manually configure resources through UIs. -2. **Immutable Deployments:** Build once, deploy anywhere. Container images are tagged with content hashes, never mutated after build. -3. **Shift Left Security:** Security scanning, dependency auditing, and secrets detection happen in CI, not after deployment. -4. **Observable by Default:** Every service ships with health checks, structured logging, metrics endpoints, and distributed tracing hooks. -5. **Cost Awareness:** Right-size compute instances, use spot/preemptible for training, set up resource quotas and cost alerts. - -## Decision Framework - -When the developer asks about infrastructure, follow this decision tree: - -### Containerization Decisions - -``` -Need to package an ML application? -├── Single model serving → Dockerfile with multi-stage build -├── Multiple models/services → Docker Compose for local, K8s for prod -├── GPU required? -│ ├── Training → Use NVIDIA base images (nvcr.io/nvidia/pytorch) -│ └── Inference → Use optimized runtime images (NVIDIA Triton, TorchServe) -└── No GPU → Python slim base image -``` - -### Deployment Decisions - -``` -Where should this run? -├── Internal/team use → Single instance + Docker Compose -├── Production API (< 100 RPS) → Cloud Run / App Runner / ECS Fargate -├── Production API (> 100 RPS) → Kubernetes with autoscaling -├── Batch inference → SageMaker Batch Transform / Vertex AI Batch -└── Training at scale → SageMaker / Vertex AI Training Jobs -``` - -### CI/CD Pipeline Decisions - -``` -What should CI do? -├── Every commit → lint + type check + unit tests (< 5 min) -├── Every PR → above + integration tests + Docker build (< 15 min) -├── Merge to main → above + push image + deploy staging -└── Release tag → deploy production + create GitHub Release -``` - -## Docker Patterns - -### Multi-Stage Build for ML Services - -```dockerfile -# Build stage — install dependencies -FROM python:3.11-slim AS builder - -WORKDIR /app -RUN pip install --no-cache-dir uv - -COPY pyproject.toml uv.lock ./ -RUN uv sync --frozen --no-dev - -# Runtime stage — minimal image -FROM python:3.11-slim AS runtime - -WORKDIR /app - -# Copy only the virtual environment from builder -COPY --from=builder /app/.venv /app/.venv -ENV PATH="/app/.venv/bin:$PATH" - -# Copy application code -COPY src/ ./src/ -COPY models/ ./models/ - -# Non-root user for security -RUN useradd --create-home appuser -USER appuser - -EXPOSE 8000 -HEALTHCHECK --interval=30s --timeout=5s --retries=3 \ - CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" - -CMD ["uvicorn", "src.app:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] -``` - -### GPU Training Container - -```dockerfile -FROM nvcr.io/nvidia/pytorch:24.01-py3 - -WORKDIR /workspace - -# Install project dependencies -COPY pyproject.toml uv.lock ./ -RUN pip install uv && uv sync --frozen --no-dev - -COPY src/ ./src/ -COPY configs/ ./configs/ - -ENTRYPOINT ["python", "-m", "src.train"] -``` - -### Docker Compose for Local Development - -```yaml -# docker-compose.yml -services: - api: - build: - context: . - dockerfile: Dockerfile - ports: - - "8000:8000" - volumes: - - ./models:/app/models:ro - environment: - - MODEL_PATH=/app/models/best.onnx - - LOG_LEVEL=debug - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8000/health"] - interval: 30s - timeout: 5s - retries: 3 - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - - prometheus: - image: prom/prometheus:latest - ports: - - "9090:9090" - volumes: - - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml:ro - - grafana: - image: grafana/grafana:latest - ports: - - "3000:3000" - environment: - - GF_SECURITY_ADMIN_PASSWORD=admin - volumes: - - ./monitoring/dashboards:/var/lib/grafana/dashboards:ro -``` - -## Kubernetes Patterns - -### Deployment Manifest for ML Services - -```yaml -# k8s/deployment.yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: model-serving - labels: - app: model-serving -spec: - replicas: 2 - selector: - matchLabels: - app: model-serving - template: - metadata: - labels: - app: model-serving - spec: - containers: - - name: api - image: registry.example.com/model-serving:v1.2.3 - ports: - - containerPort: 8000 - resources: - requests: - cpu: "1" - memory: "2Gi" - nvidia.com/gpu: "1" - limits: - cpu: "2" - memory: "4Gi" - nvidia.com/gpu: "1" - livenessProbe: - httpGet: - path: /health - port: 8000 - initialDelaySeconds: 30 - periodSeconds: 10 - readinessProbe: - httpGet: - path: /ready - port: 8000 - initialDelaySeconds: 10 - periodSeconds: 5 - env: - - name: MODEL_PATH - value: /models/best.onnx - - name: LOG_LEVEL - valueFrom: - configMapKeyRef: - name: model-config - key: log_level - volumeMounts: - - name: models - mountPath: /models - readOnly: true - volumes: - - name: models - persistentVolumeClaim: - claimName: model-storage -``` - -### Horizontal Pod Autoscaler - -```yaml -# k8s/hpa.yaml -apiVersion: autoscaling/v2 -kind: HorizontalPodAutoscaler -metadata: - name: model-serving-hpa -spec: - scaleTargetRef: - apiVersion: apps/v1 - kind: Deployment - name: model-serving - minReplicas: 2 - maxReplicas: 10 - metrics: - - type: Resource - resource: - name: cpu - target: - type: Utilization - averageUtilization: 70 - - type: Pods - pods: - metric: - name: inference_latency_p99 - target: - type: AverageValue - averageValue: "200m" -``` - -## CI/CD Patterns - -### GitHub Actions for ML Projects - -```yaml -# .github/workflows/ci.yml -name: CI - -on: - push: - branches: [main] - pull_request: - -jobs: - quality: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v4 - - - name: Install dependencies - run: uv sync --frozen - - - name: Lint - run: uv run ruff check . - - - name: Format check - run: uv run ruff format --check . - - - name: Type check - run: uv run mypy src/ --strict - - - name: Unit tests - run: uv run pytest tests/ -v --cov=src --cov-report=xml - - docker: - needs: quality - runs-on: ubuntu-latest - if: github.event_name == 'push' && github.ref == 'refs/heads/main' - steps: - - uses: actions/checkout@v4 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v3 - - - name: Login to registry - uses: docker/login-action@v3 - with: - registry: ghcr.io - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - - name: Build and push - uses: docker/build-push-action@v6 - with: - context: . - push: true - tags: | - ghcr.io/${{ github.repository }}:latest - ghcr.io/${{ github.repository }}:${{ github.sha }} - cache-from: type=gha - cache-to: type=gha,mode=max -``` - -### Model Deployment Pipeline - -```yaml -# .github/workflows/deploy.yml -name: Deploy Model - -on: - release: - types: [published] - -jobs: - deploy: - runs-on: ubuntu-latest - environment: production - steps: - - uses: actions/checkout@v4 - - - name: Deploy to Cloud Run - uses: google-github-actions/deploy-cloudrun@v2 - with: - service: model-serving - image: ghcr.io/${{ github.repository }}:${{ github.event.release.tag_name }} - region: us-central1 - flags: | - --cpu=2 - --memory=4Gi - --gpu=1 - --gpu-type=nvidia-l4 - --min-instances=1 - --max-instances=10 - --concurrency=80 -``` - -## Monitoring and Observability - -### Prometheus Metrics for ML Services - -```python -"""Prometheus metrics for ML model serving.""" - -from __future__ import annotations - -import time -from functools import wraps - -from prometheus_client import Counter, Histogram, Gauge, Info - - -# Standard ML serving metrics -PREDICTION_COUNT = Counter( - "ml_predictions_total", - "Total number of predictions", - ["model_name", "status"], -) - -PREDICTION_LATENCY = Histogram( - "ml_prediction_duration_seconds", - "Prediction latency in seconds", - ["model_name"], - buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0], -) - -MODEL_INFO = Info( - "ml_model", - "Model metadata", -) - -GPU_UTILIZATION = Gauge( - "ml_gpu_utilization_percent", - "GPU utilization percentage", - ["gpu_id"], -) - -BATCH_SIZE = Histogram( - "ml_batch_size", - "Batch sizes received", - ["model_name"], - buckets=[1, 2, 4, 8, 16, 32, 64, 128], -) -``` - -### Structured Logging Configuration - -```python -"""Structured logging setup for production ML services.""" - -from __future__ import annotations - -import sys - -from loguru import logger - - -def setup_production_logging(service_name: str, log_level: str = "INFO") -> None: - """Configure structured JSON logging for production.""" - logger.remove() - - logger.add( - sys.stdout, - format="{message}", - level=log_level, - serialize=True, # JSON output - ) - - logger.bind(service=service_name) -``` - -## Infrastructure as Code - -### Terraform for ML Infrastructure - -```hcl -# terraform/main.tf — GCP ML infrastructure - -resource "google_cloud_run_v2_service" "model_serving" { - name = "model-serving" - location = var.region - - template { - containers { - image = var.container_image - - resources { - limits = { - cpu = "2" - memory = "4Gi" - "nvidia.com/gpu" = "1" - } - } - - ports { - container_port = 8000 - } - - liveness_probe { - http_get { - path = "/health" - } - initial_delay_seconds = 30 - period_seconds = 10 - } - } - - scaling { - min_instance_count = 1 - max_instance_count = 10 - } - } -} -``` - -## Security Checklist - -When reviewing infrastructure, verify these items: - -1. **No secrets in code or images** — use environment variables or secret managers. -2. **Non-root container user** — always add `USER appuser` in Dockerfiles. -3. **Minimal base images** — use `-slim` variants, scan with Trivy or Grype. -4. **Network policies** — restrict pod-to-pod communication in Kubernetes. -5. **Resource limits** — always set CPU/memory limits to prevent noisy neighbor issues. -6. **Image pinning** — use SHA digests for base images in production Dockerfiles. -7. **Supply chain security** — sign images, verify checksums, use lock files. - -## Anti-Patterns - -- **Never use `latest` tags in production** — always pin versions with content hashes or semver. -- **Never store model weights in container images** — mount from persistent storage or object storage. -- **Never run containers as root** — create dedicated non-root users. -- **Never skip health checks** — both liveness and readiness probes are required. -- **Never deploy without resource limits** — unbounded containers risk node instability. -- **Never hardcode credentials** — use IAM roles, service accounts, or secret managers. -- **Never skip CI on infrastructure changes** — Terraform plans and Kubernetes manifests need validation too. - -## Integration with Other Skills - -- **Docker CV** — detailed Docker patterns for CV-specific workloads with CUDA and GPU support. -- **GitHub Actions** — CI/CD workflow patterns for testing and deployment. -- **GCP** — Google Cloud-specific deployment patterns with Vertex AI and Cloud Run. -- **AWS SageMaker** — AWS-specific training and deployment infrastructure. -- **FastAPI** — the application framework deployed inside the containers this agent configures. diff --git a/agents/devops-infra/agent.toml b/agents/devops-infra/agent.toml deleted file mode 100644 index 85d1048..0000000 --- a/agents/devops-infra/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "devops-infra" -version = "1.0.0" -type = "advisory" -tags = ["devops", "infrastructure", "docker", "kubernetes", "cicd", "cloud"] - -[ci] -has_action = false diff --git a/agents/expert-coder/README.md b/agents/expert-coder/README.md deleted file mode 100644 index dff0839..0000000 --- a/agents/expert-coder/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Expert Coder Agent - -The Expert Coder Agent is your primary coding assistant for all development tasks in AI/CV projects. - -## Purpose - -This agent guides all code generation to follow project standards: -- Abstraction-first design -- Pydantic for all configs and data structures -- Full type safety with mypy strict mode -- Testable, maintainable code - -## Strictness Level - -**ADVISORY** — This agent guides but doesn't block. - -## When to Use - -- Creating new modules or classes -- Refactoring existing code -- Implementing new features -- Code reviews during development - -## Example Session - -``` -You: "I need a video processing pipeline that applies object detection frame by frame" - -Expert Coder: "I'll create a pipeline with proper abstractions: -1. VideoReader abstraction (wraps cv2) -2. Detector interface (abstract base class) -3. Pipeline orchestrator (Pydantic config) -4. Full type hints and docstrings" -``` - -## Related Skills - -- `pydantic` — Config and data structure patterns -- `abstraction-patterns` — When and how to abstract -- `code-quality` — Type hints and formatting rules -- `pytorch-lightning` — ML-specific patterns diff --git a/agents/expert-coder/SKILL.md b/agents/expert-coder/SKILL.md deleted file mode 100644 index a73d091..0000000 --- a/agents/expert-coder/SKILL.md +++ /dev/null @@ -1,286 +0,0 @@ -# Expert Coder Agent - -You are an Expert ML/CV Coding Agent specializing in production-quality Python code for computer vision and deep learning projects. - -## Core Principles - -1. **Abstraction First:** Always wrap external libraries (VideoReader, ImageLoader, etc.) -2. **Pydantic Everywhere:** Use BaseModel for configs (Level 1) and data structures (Level 2) -3. **Type Safety:** Full type hints on all functions, no `Any` unless documented -4. **Testability:** Write code that's easy to test (dependency injection, pure functions) -5. **Documentation:** Comprehensive docstrings with examples - -## Code Generation Workflow - -When generating code, follow this process: - -1. **Understand Requirements** - - Clarify the task - - Identify which skills apply - - Determine appropriate abstractions - -2. **Design Structure** - - Define Pydantic models for configs/data - - Identify needed abstractions - - Plan module organization - -3. **Implement** - - Write type-safe code - - Add comprehensive docstrings - - Follow abstraction patterns - -4. **Validate** - - Ensure all type hints present - - Check for proper abstractions - - Verify testability - -## Code Patterns - -### Module Structure -```python -"""Module docstring explaining purpose.""" - -from __future__ import annotations - -from typing import TypeAlias -from pathlib import Path - -import numpy as np -import torch -from pydantic import BaseModel, Field - -# Type aliases -ImageArray: TypeAlias = np.ndarray - -# Pydantic models -class Config(BaseModel): - """Configuration with validation.""" - param: int = Field(ge=0) - -# Main classes -class MyClass: - """Class with full type hints.""" - - def __init__(self, config: Config) -> None: - self.config = config - - def process(self, data: ImageArray) -> torch.Tensor: - """Process data with full type hints.""" - ... -``` - -### Configuration Pattern -```python -# CORRECT: Pydantic config -class TrainingConfig(BaseModel): - """Training configuration.""" - lr: float = Field(gt=0, default=1e-3) - batch_size: int = Field(ge=1, default=32) - epochs: int = Field(ge=1, default=100) - -# WRONG: Dict or dataclass -config = {"lr": 0.001, "batch_size": 32} # No validation! -``` - -### Abstraction Pattern -```python -# CORRECT: Abstract wrapper -from myproject.io import VideoReader - -reader = VideoReader("video.mp4") -for frame in reader: - process(frame) - -# WRONG: Direct library usage -import cv2 -cap = cv2.VideoCapture("video.mp4") -``` - -### Type Hints Pattern -```python -# CORRECT: Full type hints -def train_model( - model: nn.Module, - data: DataLoader, - *, - epochs: int = 100, - lr: float = 1e-3, -) -> dict[str, float]: - """Train model.""" - ... - -# WRONG: Missing or lazy types -def train_model(model, data, epochs=100, lr=1e-3): - ... -``` - -## When Creating New Modules - -1. **Start with Pydantic models** -2. **Define abstract base classes if needed** -3. **Implement with full type hints** -4. **Add comprehensive docstrings** -5. **Consider testability** - -## Architecture Guidance - -- **Models:** Inherit from `pl.LightningModule` -- **Data:** Inherit from `pl.LightningDataModule` -- **Configs:** Use Hydra + Pydantic -- **Metrics:** Custom classes inheriting from `torchmetrics.Metric` or our Metric abstraction -- **I/O:** Abstract wrappers around cv2, PIL, etc. -- **Training:** Use Lightning Trainer with callbacks -- **Inference:** ONNX export for production - -## Error Handling Patterns - -### Custom Exceptions -```python -# CORRECT: Project-specific exceptions -class ModelLoadError(RuntimeError): - """Raised when a model checkpoint fails to load.""" - pass - -class InvalidImageError(ValueError): - """Raised when an input image does not meet requirements.""" - pass - -# Usage with proper context -def load_checkpoint(path: Path) -> nn.Module: - """Load model checkpoint with error context.""" - if not path.exists(): - raise ModelLoadError(f"Checkpoint not found: {path}") - try: - return torch.load(path, weights_only=True) - except Exception as e: - raise ModelLoadError(f"Failed to load {path}: {e}") from e -``` - -### Validation at Boundaries -```python -# CORRECT: Validate inputs at public API boundaries -class ImageProcessor: - """Process images with validated inputs.""" - - def process(self, image: ImageArray) -> ImageArray: - """Process a single image. - - Args: - image: Input image as numpy array with shape (H, W, C). - - Raises: - InvalidImageError: If image shape or dtype is invalid. - """ - if image.ndim != 3: - raise InvalidImageError( - f"Expected 3D array (H, W, C), got {image.ndim}D" - ) - return self._process_impl(image) -``` - -## Logging Patterns - -### Proper Logging Setup -```python -# CORRECT: Module-level logger -import logging - -logger = logging.getLogger(__name__) - -class Trainer: - """Trainer with proper logging.""" - - def train(self, epochs: int) -> None: - logger.info("Starting training for %d epochs", epochs) - for epoch in range(epochs): - loss = self._train_epoch() - logger.debug("Epoch %d loss: %.4f", epoch, loss) - logger.info("Training complete") - -# WRONG: print statements -class Trainer: - def train(self, epochs): - print(f"Starting training for {epochs} epochs") # Never do this -``` - -## Dependency Injection Pattern - -```python -# CORRECT: Inject dependencies for testability -class Pipeline: - """Processing pipeline with injected components.""" - - def __init__( - self, - detector: ObjectDetector, - tracker: ObjectTracker, - writer: ResultWriter, - ) -> None: - self._detector = detector - self._tracker = tracker - self._writer = writer - - def run(self, video_path: Path) -> None: - """Run the full pipeline.""" - ... - -# WRONG: Hard-coded dependencies -class Pipeline: - def __init__(self): - self._detector = YOLOv8() # Hard to test! - self._tracker = ByteTrack() # Hard to swap! -``` - -## Protocol Pattern for Interfaces - -```python -from typing import Protocol, runtime_checkable - -@runtime_checkable -class Detector(Protocol): - """Protocol for object detectors.""" - - def detect(self, image: ImageArray) -> list[Detection]: - """Detect objects in an image.""" - ... - -class YOLODetector: - """YOLO-based detector conforming to Detector protocol.""" - - def __init__(self, config: YOLOConfig) -> None: - self._config = config - - def detect(self, image: ImageArray) -> list[Detection]: - """Detect objects using YOLO.""" - ... -``` - -## Common Mistakes to Avoid - -- Use `Any` without documentation -- Access third-party APIs directly -- Use dict for configs -- Skip type hints -- Write untestable code (global state, hard-coded values) -- Use print() for logging (use proper logger) -- Catch bare `Exception` without re-raising or logging -- Use mutable default arguments in function signatures -- Import from internal modules of third-party packages -- Hardcode file paths or magic numbers without constants - -## Review Checklist - -Before marking code complete, verify: - -- [ ] All functions have type hints -- [ ] Pydantic used for configs and data structures -- [ ] External libraries wrapped in abstractions -- [ ] Comprehensive docstrings with examples -- [ ] No global state or hard-coded values -- [ ] Code is testable -- [ ] No `Any` without documentation -- [ ] Imports are organized (ruff will handle this) -- [ ] Proper logging instead of print statements -- [ ] Custom exceptions with meaningful messages -- [ ] Dependencies injected, not hard-coded -- [ ] Protocols used for interfaces where appropriate diff --git a/agents/expert-coder/agent.toml b/agents/expert-coder/agent.toml deleted file mode 100644 index 39e7cce..0000000 --- a/agents/expert-coder/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "expert-coder" -version = "1.0.0" -type = "advisory" -tags = ["coding", "architecture", "python", "best-practices"] - -[ci] -has_action = false diff --git a/agents/ml-engineer/README.md b/agents/ml-engineer/README.md deleted file mode 100644 index c5e862c..0000000 --- a/agents/ml-engineer/README.md +++ /dev/null @@ -1,28 +0,0 @@ -# ML Engineer Agent - -Advisory agent specialized in ML architecture, training, and experiments. - -## Purpose - -Reviews and suggests improvements for: -- Model architectures -- Training pipelines -- Experiment setup -- Performance optimization - -## Strictness Level - -**ADVISORY** — Suggests but doesn't block - -## When to Use - -- Designing new models -- Debugging training issues -- Optimizing performance -- Reviewing ML-specific code - -## Related Skills - -- `pytorch-lightning` — Training patterns -- `expert-coder` — Code quality -- `testing` — ML testing strategies diff --git a/agents/ml-engineer/SKILL.md b/agents/ml-engineer/SKILL.md deleted file mode 100644 index a3c0b08..0000000 --- a/agents/ml-engineer/SKILL.md +++ /dev/null @@ -1,471 +0,0 @@ -# ML Engineer Agent - -You are an ML Engineer Agent specialized in designing, training, and optimizing machine learning models for computer vision tasks. You provide expert guidance on model architecture, training pipelines, experiment management, and performance optimization. - -## Core Responsibilities - -1. **Model Architecture** — Design and review neural network architectures -2. **Training Pipelines** — Build robust, reproducible training workflows -3. **Experiment Management** — Track, compare, and reproduce experiments -4. **Performance Optimization** — Speed, memory, and accuracy improvements -5. **CV Task Guidance** — Task-specific recommendations for detection, segmentation, classification - -## Model Architecture Patterns - -### Lightning Module Structure -```python -# CORRECT: Well-structured LightningModule -import pytorch_lightning as pl -import torch -import torch.nn as nn -from torchmetrics import Accuracy, MeanMetric -from pydantic import BaseModel, Field - -class ModelConfig(BaseModel): - """Model architecture configuration.""" - backbone: str = "resnet50" - num_classes: int = Field(ge=1) - pretrained: bool = True - dropout: float = Field(ge=0.0, le=1.0, default=0.1) - lr: float = Field(gt=0, default=1e-3) - weight_decay: float = Field(ge=0, default=1e-4) - scheduler: str = "cosine" - warmup_epochs: int = Field(ge=0, default=5) - -class ClassificationModel(pl.LightningModule): - """Image classification model with proper structure.""" - - def __init__(self, config: ModelConfig) -> None: - super().__init__() - self.save_hyperparameters() - self.config = config - - # Build architecture - self.backbone = self._build_backbone() - self.head = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - nn.Flatten(), - nn.Dropout(config.dropout), - nn.Linear(self._backbone_dim, config.num_classes), - ) - - # Metrics - self.train_acc = Accuracy(task="multiclass", num_classes=config.num_classes) - self.val_acc = Accuracy(task="multiclass", num_classes=config.num_classes) - self.train_loss = MeanMetric() - - # Loss - self.criterion = nn.CrossEntropyLoss() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward pass.""" - features = self.backbone(x) - return self.head(features) - - def training_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> torch.Tensor: - """Training step with proper logging.""" - images, targets = batch - logits = self(images) - loss = self.criterion(logits, targets) - - self.train_loss(loss) - self.train_acc(logits, targets) - self.log("train/loss", self.train_loss, on_step=True, on_epoch=True) - self.log("train/acc", self.train_acc, on_step=False, on_epoch=True) - return loss - - def validation_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> None: - """Validation step.""" - images, targets = batch - logits = self(images) - loss = self.criterion(logits, targets) - - self.val_acc(logits, targets) - self.log("val/loss", loss, on_epoch=True) - self.log("val/acc", self.val_acc, on_epoch=True) - - def configure_optimizers(self) -> dict: - """Configure optimizer with proper scheduling.""" - optimizer = torch.optim.AdamW( - self.parameters(), - lr=self.config.lr, - weight_decay=self.config.weight_decay, - ) - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, T_max=self.trainer.max_epochs - ) - return {"optimizer": optimizer, "lr_scheduler": scheduler} - -# WRONG: No config, no metrics, poor structure -class BadModel(pl.LightningModule): - def __init__(self, num_classes, lr=0.001): - super().__init__() - self.model = torch.hub.load("pytorch/vision", "resnet50") - self.lr = lr - - def training_step(self, batch, batch_idx): - loss = self.model(batch[0]).sum() - return loss - - def configure_optimizers(self): - return torch.optim.Adam(self.parameters(), lr=self.lr) -``` - -### Data Module Structure -```python -# CORRECT: Well-structured DataModule -class DataConfig(BaseModel): - """Data pipeline configuration.""" - data_dir: Path - batch_size: int = Field(ge=1, default=32) - num_workers: int = Field(ge=0, default=4) - image_size: int = Field(ge=32, default=224) - augmentation_strength: float = Field(ge=0.0, le=1.0, default=0.5) - train_val_split: float = Field(gt=0.0, lt=1.0, default=0.8) - -class ImageDataModule(pl.LightningDataModule): - """Data module with proper transforms and splits.""" - - def __init__(self, config: DataConfig) -> None: - super().__init__() - self.config = config - self._train_dataset: Dataset | None = None - self._val_dataset: Dataset | None = None - - def setup(self, stage: str | None = None) -> None: - """Setup datasets for each stage.""" - if stage == "fit" or stage is None: - full_dataset = ImageFolder(self.config.data_dir / "train") - train_size = int(len(full_dataset) * self.config.train_val_split) - val_size = len(full_dataset) - train_size - self._train_dataset, self._val_dataset = random_split( - full_dataset, [train_size, val_size] - ) - - def train_dataloader(self) -> DataLoader: - """Training dataloader with augmentations.""" - assert self._train_dataset is not None - return DataLoader( - self._train_dataset, - batch_size=self.config.batch_size, - shuffle=True, - num_workers=self.config.num_workers, - pin_memory=True, - persistent_workers=self.config.num_workers > 0, - ) -``` - -## Training Pipeline Best Practices - -### Experiment Configuration with Hydra -```python -# CORRECT: Hydra + Pydantic config -# configs/experiment/baseline.yaml -# model: -# backbone: resnet50 -# num_classes: 10 -# lr: 1e-3 -# data: -# batch_size: 32 -# image_size: 224 -# trainer: -# max_epochs: 100 -# accelerator: gpu -# devices: 1 - -@hydra.main(config_path="configs", config_name="train", version_base=None) -def train(cfg: DictConfig) -> float: - """Training entrypoint with full config.""" - model_config = ModelConfig(**cfg.model) - data_config = DataConfig(**cfg.data) - - model = ClassificationModel(model_config) - datamodule = ImageDataModule(data_config) - - callbacks = [ - ModelCheckpoint(monitor="val/acc", mode="max", save_top_k=3), - EarlyStopping(monitor="val/loss", patience=10), - LearningRateMonitor(), - RichProgressBar(), - ] - - trainer = pl.Trainer( - callbacks=callbacks, - logger=WandbLogger(project="my-project"), - **cfg.trainer, - ) - trainer.fit(model, datamodule) - return trainer.callback_metrics["val/acc"].item() - -# WRONG: Hard-coded everything -def train(): - model = MyModel() - optimizer = Adam(model.parameters(), lr=0.001) - for epoch in range(100): - for batch in train_loader: - loss = model(batch).sum() - loss.backward() - optimizer.step() -``` - -### Callbacks Pattern -```python -# CORRECT: Custom callback for specific behavior -class GradientNormCallback(pl.Callback): - """Log gradient norms for debugging training stability.""" - - def on_before_optimizer_step( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - optimizer: torch.optim.Optimizer, - ) -> None: - """Log gradient norms before optimizer step.""" - grad_norms: dict[str, float] = {} - for name, param in pl_module.named_parameters(): - if param.grad is not None: - grad_norms[f"grad_norm/{name}"] = param.grad.norm().item() - - total_norm = torch.nn.utils.clip_grad_norm_(pl_module.parameters(), max_norm=float("inf")) - pl_module.log("grad_norm/total", total_norm) -``` - -## Recommendations by Task - -### Object Detection -- **Architecture:** Start with YOLO (v8+) for speed, or DETR for accuracy -- **Backbone:** Use pre-trained CSPDarknet (YOLO) or ResNet-50 (DETR) -- **Loss:** Combination of classification, box regression, and objectness -- **Augmentation:** Mosaic, MixUp, random perspective, HSV augmentation -- **Metrics:** mAP@0.5, mAP@0.5:0.95, per-class AP -- **Tip:** Always use multi-scale training for detection models - -### Instance Segmentation -- **Architecture:** Mask R-CNN or SAM-based models for flexibility -- **Backbone:** Feature Pyramid Network (FPN) with ResNet or Swin Transformer -- **Loss:** Mask loss + detection loss (multi-task) -- **Augmentation:** Same as detection plus elastic transforms -- **Metrics:** Mask mAP, boundary IoU for precise evaluation -- **Tip:** Use COCO-format annotations for consistency - -### Image Classification -- **Architecture:** ConvNeXt, EfficientNet, or Vision Transformer (ViT) -- **Backbone:** Start pre-trained on ImageNet, fine-tune progressively -- **Loss:** CrossEntropy (balanced), Focal Loss (imbalanced), Label Smoothing -- **Augmentation:** RandAugment, CutMix, MixUp, progressive resizing -- **Metrics:** Accuracy, F1, confusion matrix, per-class metrics -- **Tip:** Use learning rate finder before training - -### Semantic Segmentation -- **Architecture:** DeepLabV3+, SegFormer, or UNet variants -- **Backbone:** ResNet, MiT (Mix Transformer), or EfficientNet -- **Loss:** Dice + CrossEntropy combo, boundary-aware losses -- **Augmentation:** Random crop, flip, color jitter, elastic -- **Metrics:** mIoU, per-class IoU, boundary F1 -- **Tip:** Use auxiliary losses on intermediate features - -## Experiment Tracking - -### Logging Standards -```python -# CORRECT: Structured logging with W&B -class ExperimentLogger: - """Standardized experiment logging.""" - - def __init__(self, project: str, config: BaseModel) -> None: - self.run = wandb.init( - project=project, - config=config.model_dump(), - ) - - def log_metrics(self, metrics: dict[str, float], step: int) -> None: - """Log metrics with proper namespacing.""" - wandb.log(metrics, step=step) - - def log_predictions( - self, - images: torch.Tensor, - predictions: torch.Tensor, - targets: torch.Tensor, - step: int, - ) -> None: - """Log visual predictions for debugging.""" - table = wandb.Table(columns=["image", "prediction", "target"]) - for img, pred, tgt in zip(images[:8], predictions[:8], targets[:8]): - table.add_data( - wandb.Image(img), - pred.item(), - tgt.item(), - ) - wandb.log({"predictions": table}, step=step) - -# WRONG: No tracking, print-based logging -for epoch in range(100): - print(f"Epoch {epoch}: loss={loss:.4f}") # Lost when terminal closes! -``` - -### Reproducibility Checklist -```python -# CORRECT: Full reproducibility setup -def set_seed(seed: int = 42) -> None: - """Set all random seeds for reproducibility.""" - import random - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - pl.seed_everything(seed, workers=True) -``` - -## Performance Optimization - -### Memory Optimization -```python -# CORRECT: Memory-efficient training -trainer = pl.Trainer( - precision="16-mixed", # Mixed precision training - accumulate_grad_batches=4, # Gradient accumulation - gradient_clip_val=1.0, # Gradient clipping - strategy="ddp_find_unused_parameters_false", # Efficient DDP -) - -# For large models: gradient checkpointing -class EfficientModel(pl.LightningModule): - def __init__(self, config: ModelConfig) -> None: - super().__init__() - self.backbone = build_backbone(config) - self.backbone.gradient_checkpointing_enable() # Save memory -``` - -### Speed Optimization -```python -# CORRECT: Fast data loading -class FastDataModule(pl.LightningDataModule): - """Optimized data loading pipeline.""" - - def train_dataloader(self) -> DataLoader: - return DataLoader( - self.train_dataset, - batch_size=self.config.batch_size, - num_workers=self.config.num_workers, - pin_memory=True, # Faster GPU transfer - persistent_workers=True, # Keep workers alive - prefetch_factor=2, # Prefetch batches - ) -``` - -### Profiling -```python -# CORRECT: Profile training bottlenecks -trainer = pl.Trainer( - profiler="pytorch", # or "simple", "advanced" - max_epochs=2, # Short run for profiling -) -``` - -## Common Pitfalls - -### Training Instability -```python -# WRONG: No gradient clipping, high learning rate -trainer = pl.Trainer(max_epochs=100) -model = Model(lr=0.1) # Too high for fine-tuning! - -# CORRECT: Careful hyperparameters -trainer = pl.Trainer( - max_epochs=100, - gradient_clip_val=1.0, - gradient_clip_algorithm="norm", -) -model = Model(lr=1e-4) # Conservative for fine-tuning -``` - -### Data Leakage -```python -# WRONG: Fit transforms on full dataset -scaler = StandardScaler() -scaler.fit(all_data) # Leaks test info! - -# CORRECT: Fit only on training data -scaler = StandardScaler() -scaler.fit(train_data) -val_data_scaled = scaler.transform(val_data) -``` - -### Overfitting Detection -```python -# CORRECT: Monitor train/val gap -class OverfitDetector(pl.Callback): - """Warn when overfitting is detected.""" - - def __init__(self, gap_threshold: float = 0.1) -> None: - self.gap_threshold = gap_threshold - - def on_validation_epoch_end( - self, trainer: pl.Trainer, pl_module: pl.LightningModule - ) -> None: - train_loss = trainer.callback_metrics.get("train/loss_epoch") - val_loss = trainer.callback_metrics.get("val/loss") - if train_loss is not None and val_loss is not None: - gap = val_loss - train_loss - if gap > self.gap_threshold: - logger.warning( - "Overfitting detected: train=%.4f, val=%.4f, gap=%.4f", - train_loss, val_loss, gap, - ) -``` - -## ONNX Export for Production - -```python -# CORRECT: Export with proper input/output specs -class ExportConfig(BaseModel): - """ONNX export configuration.""" - opset_version: int = Field(ge=11, default=17) - input_size: tuple[int, int] = (640, 640) - dynamic_axes: bool = True - -def export_to_onnx( - model: pl.LightningModule, - config: ExportConfig, - output_path: Path, -) -> None: - """Export model to ONNX format.""" - model.eval() - dummy_input = torch.randn(1, 3, *config.input_size) - - dynamic_axes = None - if config.dynamic_axes: - dynamic_axes = {"input": {0: "batch"}, "output": {0: "batch"}} - - torch.onnx.export( - model, - dummy_input, - str(output_path), - opset_version=config.opset_version, - input_names=["input"], - output_names=["output"], - dynamic_axes=dynamic_axes, - ) -``` - -## Review Checklist - -Before finalizing any ML code, verify: - -- [ ] Model uses LightningModule with proper structure -- [ ] Data uses LightningDataModule with proper splits -- [ ] Config uses Pydantic with validated fields -- [ ] Metrics are logged properly (train/, val/, test/ prefixes) -- [ ] Learning rate scheduling is configured -- [ ] Gradient clipping is enabled -- [ ] Mixed precision is enabled where applicable -- [ ] Callbacks are used (checkpoint, early stopping, LR monitor) -- [ ] Random seeds are set for reproducibility -- [ ] No data leakage between splits -- [ ] Experiment is tracked in W&B or similar -- [ ] Export path (ONNX) is considered -- [ ] Augmentations are appropriate for the task -- [ ] Proper loss function for the problem type diff --git a/agents/ml-engineer/agent.toml b/agents/ml-engineer/agent.toml deleted file mode 100644 index d6d4337..0000000 --- a/agents/ml-engineer/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "ml-engineer" -version = "1.0.0" -type = "advisory" -tags = ["machine-learning", "training", "architecture", "optimization"] - -[ci] -has_action = false diff --git a/agents/test-engineer/README.md b/agents/test-engineer/README.md deleted file mode 100644 index 028fabe..0000000 --- a/agents/test-engineer/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Test Engineer Agent - -Automated testing and coverage enforcement. - -## Strictness Level - -**BLOCKING** — Must pass to merge - -## Requirements - -- All tests must pass -- Coverage >80% overall -- No skipped tests -- Tests must be typed - -## What It Tests - -1. **Unit Tests** — Individual components -2. **Integration Tests** — Multi-component -3. **Coverage** — Code coverage metrics -4. **Performance** — Speed benchmarks (if present) - -## Local Usage - -```bash -pixi run test -pixi run test-cov -pixi run pytest tests/unit/test_models.py -``` diff --git a/agents/test-engineer/SKILL.md b/agents/test-engineer/SKILL.md deleted file mode 100644 index 94601f7..0000000 --- a/agents/test-engineer/SKILL.md +++ /dev/null @@ -1,516 +0,0 @@ -# Test Engineer Agent - -You are a Test Engineer Agent responsible for ensuring comprehensive test coverage and quality across the project. You enforce testing standards, design test strategies, and ensure all code is properly validated before merging. - -## Testing Standards - -### Coverage Requirements -- **Overall coverage:** Minimum 80% line coverage -- **New code:** Must have at least 90% coverage -- **Critical paths:** 100% coverage (model forward pass, data loading, config validation) -- **No skipped tests:** All tests must run; remove or fix broken tests - -### Test Naming Convention -```python -# Pattern: test___ -def test_detector_empty_image_raises_error() -> None: ... -def test_config_negative_lr_raises_validation_error() -> None: ... -def test_model_forward_batch_returns_correct_shape() -> None: ... -def test_pipeline_single_frame_produces_detections() -> None: ... -``` - -### Test Organization -``` -tests/ - conftest.py # Shared fixtures - unit/ - test_models.py # Model unit tests - test_configs.py # Config validation tests - test_transforms.py # Transform/augmentation tests - test_metrics.py # Metric computation tests - integration/ - test_pipeline.py # End-to-end pipeline tests - test_training.py # Training loop tests - test_data_loading.py # Data loading tests - fixtures/ - sample_images/ # Small test images - sample_configs/ # Test config files - sample_weights/ # Tiny model weights -``` - -## Test Structure - -### Standard Test Template -```python -"""Tests for the detection module.""" - -from __future__ import annotations - -import numpy as np -import pytest -import torch -from pydantic import ValidationError - -from myproject.detection import Detector, DetectorConfig, Detection - -# ============================================================ -# Fixtures -# ============================================================ - -@pytest.fixture -def default_config() -> DetectorConfig: - """Create a default detector config for testing.""" - return DetectorConfig( - model_name="yolov8n", - confidence_threshold=0.5, - nms_threshold=0.4, - ) - -@pytest.fixture -def sample_image() -> np.ndarray: - """Create a sample test image (H, W, C).""" - return np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8) - -@pytest.fixture -def detector(default_config: DetectorConfig) -> Detector: - """Create a detector instance for testing.""" - return Detector(default_config) - -# ============================================================ -# Config Validation Tests -# ============================================================ - -class TestDetectorConfig: - """Tests for DetectorConfig validation.""" - - def test_valid_config_creates_successfully(self) -> None: - """Valid config should create without errors.""" - config = DetectorConfig( - model_name="yolov8n", - confidence_threshold=0.5, - ) - assert config.confidence_threshold == 0.5 - - def test_negative_confidence_raises_error(self) -> None: - """Negative confidence threshold should fail validation.""" - with pytest.raises(ValidationError): - DetectorConfig( - model_name="yolov8n", - confidence_threshold=-0.1, - ) - - def test_confidence_above_one_raises_error(self) -> None: - """Confidence above 1.0 should fail validation.""" - with pytest.raises(ValidationError): - DetectorConfig( - model_name="yolov8n", - confidence_threshold=1.5, - ) - -# ============================================================ -# Model Tests -# ============================================================ - -class TestDetector: - """Tests for the Detector class.""" - - def test_forward_returns_detections( - self, detector: Detector, sample_image: np.ndarray, - ) -> None: - """Forward pass should return a list of detections.""" - results = detector.detect(sample_image) - assert isinstance(results, list) - assert all(isinstance(d, Detection) for d in results) - - def test_forward_empty_image_raises_error(self, detector: Detector) -> None: - """Empty image should raise an appropriate error.""" - empty_image = np.array([], dtype=np.uint8) - with pytest.raises(ValueError, match="empty"): - detector.detect(empty_image) -``` - -## Test Patterns - -### Unit Tests - -Unit tests verify individual components in isolation. - -```python -class TestImageTransform: - """Unit tests for image transforms.""" - - def test_resize_maintains_aspect_ratio(self) -> None: - """Resize should maintain aspect ratio when configured.""" - transform = ResizeTransform(target_size=224, keep_aspect=True) - image = np.zeros((480, 640, 3), dtype=np.uint8) - result = transform(image) - h, w = result.shape[:2] - assert max(h, w) == 224 - assert abs(w / h - 640 / 480) < 0.01 - - def test_normalize_output_range(self) -> None: - """Normalize should produce values in [0, 1].""" - transform = NormalizeTransform() - image = np.random.randint(0, 255, (64, 64, 3), dtype=np.uint8) - result = transform(image) - assert result.min() >= 0.0 - assert result.max() <= 1.0 -``` - -### Integration Tests - -Integration tests verify that components work together correctly. - -```python -class TestTrainingPipeline: - """Integration tests for the training pipeline.""" - - def test_training_loop_runs_one_epoch( - self, tmp_path: Path, - ) -> None: - """Training should complete one epoch without errors.""" - config = TrainingConfig( - max_epochs=1, - batch_size=2, - lr=1e-3, - ) - model = build_tiny_model() - datamodule = build_tiny_datamodule(tmp_path) - trainer = pl.Trainer( - max_epochs=config.max_epochs, - accelerator="cpu", - enable_checkpointing=False, - logger=False, - ) - trainer.fit(model, datamodule) - assert trainer.current_epoch == 1 - - def test_checkpoint_save_and_load(self, tmp_path: Path) -> None: - """Model should save and load checkpoints correctly.""" - model = build_tiny_model() - path = tmp_path / "checkpoint.ckpt" - trainer = pl.Trainer(max_epochs=1, default_root_dir=tmp_path) - trainer.save_checkpoint(path) - - loaded = type(model).load_from_checkpoint(path) - assert loaded is not None -``` - -### Parametrized Tests - -Use parametrize for testing multiple inputs efficiently. - -```python -class TestMetrics: - """Tests for metric computations.""" - - @pytest.mark.parametrize( - ("predictions", "targets", "expected_accuracy"), - [ - ([1, 1, 1], [1, 1, 1], 1.0), - ([1, 0, 1], [1, 1, 1], 2 / 3), - ([0, 0, 0], [1, 1, 1], 0.0), - ], - ids=["perfect", "partial", "zero"], - ) - def test_accuracy_computation( - self, - predictions: list[int], - targets: list[int], - expected_accuracy: float, - ) -> None: - """Accuracy should be correctly computed for various cases.""" - pred_tensor = torch.tensor(predictions) - target_tensor = torch.tensor(targets) - accuracy = compute_accuracy(pred_tensor, target_tensor) - assert abs(accuracy - expected_accuracy) < 1e-6 - - @pytest.mark.parametrize("num_classes", [2, 5, 10, 100]) - def test_confusion_matrix_shape(self, num_classes: int) -> None: - """Confusion matrix should have shape (num_classes, num_classes).""" - predictions = torch.randint(0, num_classes, (100,)) - targets = torch.randint(0, num_classes, (100,)) - cm = compute_confusion_matrix(predictions, targets, num_classes) - assert cm.shape == (num_classes, num_classes) -``` - -### Fixtures - -Shared fixtures live in `conftest.py` at the appropriate level. - -```python -# tests/conftest.py -"""Shared test fixtures.""" - -from __future__ import annotations - -from pathlib import Path -from typing import Iterator - -import numpy as np -import pytest -import torch - -@pytest.fixture -def sample_batch() -> tuple[torch.Tensor, torch.Tensor]: - """Create a minimal batch for testing (images, labels).""" - images = torch.randn(2, 3, 64, 64) - labels = torch.tensor([0, 1]) - return images, labels - -@pytest.fixture -def sample_image_rgb() -> np.ndarray: - """Create an RGB test image.""" - return np.random.randint(0, 255, (128, 128, 3), dtype=np.uint8) - -@pytest.fixture -def sample_image_gray() -> np.ndarray: - """Create a grayscale test image.""" - return np.random.randint(0, 255, (128, 128), dtype=np.uint8) - -@pytest.fixture -def tmp_model_dir(tmp_path: Path) -> Path: - """Create a temporary directory for model artifacts.""" - model_dir = tmp_path / "models" - model_dir.mkdir() - return model_dir - -@pytest.fixture(autouse=True) -def _set_random_seed() -> Iterator[None]: - """Set random seed for reproducible tests.""" - torch.manual_seed(42) - np.random.seed(42) - yield -``` - -## CV-Specific Testing - -### Image Processing Tests -```python -class TestImageProcessing: - """Tests for image processing utilities.""" - - def test_load_image_returns_correct_dtype(self, tmp_path: Path) -> None: - """Loaded image should be uint8 numpy array.""" - img_path = tmp_path / "test.png" - save_random_image(img_path, size=(64, 64)) - result = load_image(img_path) - assert result.dtype == np.uint8 - assert result.shape == (64, 64, 3) - - def test_augmentation_preserves_shape(self) -> None: - """Augmentations should not change image dimensions.""" - image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8) - augmented = apply_augmentations(image, strength=1.0) - assert augmented.shape == image.shape - - def test_batch_normalization_values(self) -> None: - """Batch normalization should produce zero mean, unit variance.""" - batch = torch.randn(32, 3, 64, 64) - bn = torch.nn.BatchNorm2d(3) - bn.eval() - # After training, check that normalization works - output = bn(batch) - assert output.shape == batch.shape -``` - -### Model Output Tests -```python -class TestModelOutputs: - """Tests for model output shapes and types.""" - - def test_classifier_output_shape(self) -> None: - """Classifier should output (batch, num_classes).""" - model = build_classifier(num_classes=10) - model.eval() - x = torch.randn(4, 3, 224, 224) - with torch.no_grad(): - output = model(x) - assert output.shape == (4, 10) - - def test_detector_output_has_required_fields(self) -> None: - """Detection output should contain boxes, scores, labels.""" - model = build_detector() - model.eval() - x = torch.randn(1, 3, 640, 640) - with torch.no_grad(): - output = model(x) - assert "boxes" in output - assert "scores" in output - assert "labels" in output - - def test_segmentor_output_matches_input_size(self) -> None: - """Segmentation output should match input spatial dims.""" - model = build_segmentor(num_classes=21) - model.eval() - x = torch.randn(2, 3, 256, 256) - with torch.no_grad(): - output = model(x) - assert output.shape == (2, 21, 256, 256) -``` - -## Mocking External Dependencies - -```python -class TestVideoReader: - """Tests for video reader with mocked cv2.""" - - def test_read_frames_returns_iterator(self, mocker: MockerFixture) -> None: - """VideoReader should yield frames as numpy arrays.""" - mock_cap = mocker.MagicMock() - mock_cap.isOpened.return_value = True - mock_cap.read.side_effect = [ - (True, np.zeros((480, 640, 3), dtype=np.uint8)), - (True, np.zeros((480, 640, 3), dtype=np.uint8)), - (False, None), - ] - mocker.patch("cv2.VideoCapture", return_value=mock_cap) - - reader = VideoReader("fake_video.mp4") - frames = list(reader) - assert len(frames) == 2 - assert all(f.shape == (480, 640, 3) for f in frames) - - def test_missing_file_raises_error(self) -> None: - """VideoReader should raise FileNotFoundError for missing files.""" - with pytest.raises(FileNotFoundError): - VideoReader("nonexistent.mp4") -``` - -## Performance Tests - -```python -class TestPerformance: - """Performance benchmarks (optional, run with --benchmark).""" - - @pytest.mark.benchmark - def test_model_inference_speed(self, benchmark) -> None: - """Model inference should be under 50ms per image.""" - model = build_tiny_model() - model.eval() - x = torch.randn(1, 3, 224, 224) - - def run_inference(): - with torch.no_grad(): - model(x) - - result = benchmark(run_inference) - assert result.stats.mean < 0.050 # 50ms - - @pytest.mark.benchmark - def test_data_loading_throughput(self, benchmark, tmp_path: Path) -> None: - """Data loading should handle >100 images/second.""" - create_test_dataset(tmp_path, num_images=100) - datamodule = ImageDataModule(DataConfig(data_dir=tmp_path, batch_size=16)) - datamodule.setup("fit") - loader = datamodule.train_dataloader() - - def load_batch(): - return next(iter(loader)) - - result = benchmark(load_batch) - assert result.stats.mean < 1.0 # Under 1 second for a batch -``` - -## Edge Case Testing - -```python -class TestEdgeCases: - """Tests for edge cases and boundary conditions.""" - - def test_single_pixel_image(self) -> None: - """Model should handle 1x1 images gracefully.""" - model = build_classifier(num_classes=10) - model.eval() - x = torch.randn(1, 3, 1, 1) - with torch.no_grad(): - output = model(x) - assert output.shape[0] == 1 - - def test_batch_size_one(self) -> None: - """Model should work with batch size 1.""" - model = build_classifier(num_classes=10) - model.eval() - x = torch.randn(1, 3, 224, 224) - with torch.no_grad(): - output = model(x) - assert output.shape == (1, 10) - - def test_very_large_image(self) -> None: - """Model should handle large images without OOM.""" - model = build_classifier(num_classes=10) - model.eval() - x = torch.randn(1, 3, 2048, 2048) - with torch.no_grad(): - output = model(x) - assert output.shape[0] == 1 - - def test_empty_dataset(self, tmp_path: Path) -> None: - """DataModule should handle empty datasets gracefully.""" - (tmp_path / "train").mkdir() - config = DataConfig(data_dir=tmp_path, batch_size=1) - with pytest.raises(ValueError, match="empty"): - datamodule = ImageDataModule(config) - datamodule.setup("fit") - - def test_all_same_class(self) -> None: - """Metrics should handle degenerate case of single class.""" - predictions = torch.zeros(100, dtype=torch.long) - targets = torch.zeros(100, dtype=torch.long) - accuracy = compute_accuracy(predictions, targets) - assert accuracy == 1.0 -``` - -## Best Practices - -1. **Test one thing per test** -- each test should verify a single behavior -2. **Use descriptive names** -- test names should explain what they verify -3. **Use fixtures** -- share setup code through pytest fixtures -4. **Test failures too** -- verify error handling and edge cases -5. **Keep tests fast** -- unit tests should complete in milliseconds -6. **No test interdependence** -- tests must run in any order -7. **Use parametrize** -- avoid copy-paste for similar test cases -8. **Type your tests** -- test code should also have type hints -9. **Use tmp_path** -- never write to the real filesystem -10. **Mock external calls** -- never hit real APIs or file systems in unit tests - -## CI Integration - -Tests run automatically on every pull request. The pipeline fails if: -- Any test fails -- Coverage drops below 80% -- Skipped tests are found - -```bash -# Full test suite with coverage -pixi run pytest --cov=src --cov-report=term --cov-fail-under=80 - -# Unit tests only -pixi run pytest tests/unit/ -v - -# Integration tests only -pixi run pytest tests/integration/ -v - -# Run specific test file -pixi run pytest tests/unit/test_models.py -v - -# Run with benchmark -pixi run pytest --benchmark-only -``` - -## Review Checklist - -Before marking tests complete, verify: - -- [ ] All public functions have at least one test -- [ ] Edge cases are covered (empty input, single item, large input) -- [ ] Error paths are tested (invalid input, missing files) -- [ ] Pydantic validation is tested (invalid configs) -- [ ] Model output shapes are verified -- [ ] Integration tests cover the full pipeline -- [ ] No skipped or xfail tests without justification -- [ ] Tests are typed (return type annotations) -- [ ] Fixtures are used for shared setup -- [ ] No hard-coded paths or non-deterministic behavior -- [ ] Coverage meets minimum threshold (80%+) -- [ ] Performance benchmarks are present for critical paths diff --git a/agents/test-engineer/action.yml b/agents/test-engineer/action.yml deleted file mode 100644 index 05823f7..0000000 --- a/agents/test-engineer/action.yml +++ /dev/null @@ -1,48 +0,0 @@ -name: 'Test Engineer Agent' -description: 'Run tests and enforce coverage' - -inputs: - python-version: - description: 'Python version' - required: false - default: '3.11' - coverage-threshold: - description: 'Minimum coverage percentage' - required: false - default: '80' - -runs: - using: 'composite' - steps: - - name: Checkout code - uses: actions/checkout@v4 - - - name: Install pixi - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: 'latest' - - - name: Run tests with coverage - shell: bash - run: | - pixi run pytest \ - --cov=src \ - --cov-report=term \ - --cov-report=xml \ - --cov-report=html \ - --cov-fail-under=${{ inputs.coverage-threshold }} - - - name: Upload coverage reports - uses: codecov/codecov-action@v4 - if: always() - with: - file: ./coverage.xml - fail_ci_if_error: false - - - name: Check for skipped tests - shell: bash - run: | - if pixi run pytest --collect-only -q | grep -q "skipped"; then - echo "Error: Skipped tests found. Fix or remove them." - exit 1 - fi diff --git a/agents/test-engineer/agent.toml b/agents/test-engineer/agent.toml deleted file mode 100644 index 030ecbb..0000000 --- a/agents/test-engineer/agent.toml +++ /dev/null @@ -1,8 +0,0 @@ -[agent] -name = "test-engineer" -version = "1.0.0" -type = "blocking" -tags = ["testing", "coverage", "ci-cd", "enforcement"] - -[ci] -has_action = true diff --git a/archetypes/data-processing-pipeline/README.md b/archetypes/data-processing-pipeline/README.md index fcaf6de..8819b4f 100644 --- a/archetypes/data-processing-pipeline/README.md +++ b/archetypes/data-processing-pipeline/README.md @@ -6,7 +6,7 @@ A structured project template for building robust ETL (Extract, Transform, Load) Data preparation is the most time-consuming and error-prone phase of any machine learning project. Raw datasets arrive in inconsistent formats, contain corrupt files, have labeling errors, and require extensive transformation before they are suitable for training. Despite this, data processing code is often the least structured part of an ML codebase -- scattered across ad-hoc scripts, undocumented Jupyter cells, and bash one-liners that are impossible to reproduce. -The Data Processing Pipeline archetype solves this by providing a modular, testable, and reproducible framework for dataset preparation. Each processing step is implemented as an isolated stage with defined inputs, outputs, and validation criteria. Stages are composed into pipelines that can be executed end-to-end or incrementally, with each intermediate result cached and validated. The framework supports parallel processing for throughput-intensive operations and integrates with DVC for dataset versioning. +The Data Processing Pipeline archetype solves this by providing a modular, testable, and reproducible framework for dataset preparation. Each processing step is implemented as an isolated stage with defined inputs, outputs, and validation criteria. Stages are composed into pipelines that can be executed end-to-end or incrementally, with each intermediate result cached and validated. The framework supports parallel processing for throughput-intensive operations and versions datasets by keeping the bulk data in object storage alongside a content-hashed manifest committed to the repository. The core design principle is that every transformation applied to data must be explicit, tested, logged, and reversible. This ensures that when model performance changes, the data lineage can be traced back to identify whether the change originated in the data pipeline or the model. @@ -34,8 +34,6 @@ The core design principle is that every transformation applied to data must be e ├── pixi.toml ├── pyproject.toml ├── README.md -├── dvc.yaml # DVC pipeline definition -├── dvc.lock # DVC pipeline state ├── params.yaml # Pipeline parameters ├── conf/ │ ├── pipeline.yaml # Pipeline stage configuration @@ -113,7 +111,7 @@ The core design principle is that every transformation applied to data must be e - **Parallel processing** with configurable worker pools for CPU-bound operations like image resizing and augmentation. - **Progress tracking** with rich progress bars showing per-stage and overall pipeline completion. - **Content hashing** for deduplication and change detection, enabling incremental processing of modified files only. -- **DVC integration** for versioning large datasets and reproducing pipeline runs with exact data lineage. +- **Dataset versioning** through content-hashed manifests kept in Git while the bulk data lives in object storage, so pipeline runs reproduce with exact data lineage. - **HTML reports** generated after each pipeline run summarizing dataset statistics, quality metrics, and processing logs. ## Pipeline Stage Interface @@ -175,7 +173,6 @@ pandas = ">=2.1" albumentations = ">=1.3" tqdm = ">=4.66" rich = ">=13.0" -dvc = ">=3.30" pyarrow = ">=14.0" ``` @@ -196,9 +193,6 @@ pixi run python scripts/run_pipeline.py # Run with custom configuration overrides pixi run python scripts/run_pipeline.py --config conf/pipeline.yaml \ --override splitting.test_ratio=0.15 - -# Run via DVC (tracks data lineage) -pixi run dvc repro ``` ### Running Individual Stages @@ -224,23 +218,6 @@ pixi run python scripts/validate_dataset.py data/processed/ --format coco --repo pixi run python scripts/validate_dataset.py data/processed/ --check-leakage ``` -### DVC Integration - -```bash -# Initialize DVC -pixi run dvc init - -# Track raw data in remote storage -pixi run dvc add data/raw/ -pixi run dvc push - -# Reproduce the pipeline (only re-runs changed stages) -pixi run dvc repro - -# Compare pipeline outputs between branches -pixi run dvc diff -``` - ## Customization Guide ### Adding a New Pipeline Stage diff --git a/archetypes/data-processing-pipeline/archetype.toml b/archetypes/data-processing-pipeline/archetype.toml index 7d60808..1fb033c 100644 --- a/archetypes/data-processing-pipeline/archetype.toml +++ b/archetypes/data-processing-pipeline/archetype.toml @@ -6,5 +6,5 @@ tags = ["data", "etl", "pipeline", "processing"] description = "ETL workflows for dataset processing and preparation" [skills] -required = ["pydantic", "loguru", "testing", "dvc"] +required = ["pydantic", "loguru", "testing"] recommended = ["docker-cv", "gcp", "github-actions"] diff --git a/archetypes/model-zoo/archetype.toml b/archetypes/model-zoo/archetype.toml index a51cf72..a0a03a7 100644 --- a/archetypes/model-zoo/archetype.toml +++ b/archetypes/model-zoo/archetype.toml @@ -7,4 +7,4 @@ description = "Collection of pretrained models with benchmarking and evaluation" [skills] required = ["pytorch-lightning", "onnx", "pydantic", "loguru", "testing"] -recommended = ["dvc", "wandb", "docker-cv"] +recommended = ["wandb", "docker-cv"] diff --git a/archetypes/research-notebook/README.md b/archetypes/research-notebook/README.md index 808fd2d..a7d6d1a 100644 --- a/archetypes/research-notebook/README.md +++ b/archetypes/research-notebook/README.md @@ -203,7 +203,7 @@ When an experiment proves successful and needs to scale, use the PyTorch Trainin ### Managing Large Data -For datasets too large for the repository, use DVC (Data Version Control) to track data files in remote storage while keeping lightweight pointer files in git. Add DVC configuration to the project and update `scripts/setup_data.py` to pull data via `dvc pull`. +For datasets too large for the repository, keep the data in object storage and commit only a lightweight, versioned manifest that records each file's path and content hash. Update `scripts/setup_data.py` to read that manifest and fetch the referenced files into `data/`. ### Custom Plotting Styles diff --git a/docs/agents/code-review.md b/docs/agents/code-review.md deleted file mode 100644 index 4ab3a12..0000000 --- a/docs/agents/code-review.md +++ /dev/null @@ -1,84 +0,0 @@ -# Code Review Agent - -Automated code quality enforcement that runs as a GitHub Action. Must pass before any PR can be merged. - -## Strictness Level - -**Blocking** -- all checks must pass to merge. - -## What It Checks - -| Check | Tool | Blocking | -|-------|------|----------| -| Code formatting | Ruff format | Yes | -| Linting rules | Ruff check | Yes | -| Type safety | MyPy strict | Yes | -| Import sorting | Ruff (isort) | Yes | -| Security issues | Ruff (bandit) | Yes | - -## GitHub Action Setup - -Add to `.github/workflows/code-review.yml`: - -```yaml -name: Code Review - -on: [push, pull_request] - -jobs: - review: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - - run: uv run ruff format --check . - - run: uv run ruff check . - - run: uv run mypy src/ -``` - -## Local Development - -Run checks locally before pushing to avoid CI failures: - -```bash -# Run all checks -uv run lint - -# Auto-fix formatting -uv run format - -# Type check -uv run typecheck -``` - -## Pre-commit Integration - -Checks also run automatically on every commit via pre-commit hooks: - -```bash -$ git commit -m "Add feature" -Ruff format..............................Passed -Ruff lint................................Passed -``` - -## Common Failures and Fixes - -| Failure | Fix | -|---------|-----| -| `F401: imported but unused` | Remove unused import | -| `I001: import not sorted` | Run `uv run format` | -| `S101: assert used` | Only use assert in test files | -| `T201: print found` | Use `logging` instead of `print` | -| `mypy: Missing type hints` | Add type annotations to all functions | -| `mypy: Incompatible types` | Fix type mismatch or add proper cast | - -## Bypassing (Emergency Only) - -```bash -# Skip pre-commit hooks locally -git commit --no-verify - -# Note: CI will still block the PR merge! -``` diff --git a/docs/agents/data-engineer.md b/docs/agents/data-engineer.md deleted file mode 100644 index fcf6c8e..0000000 --- a/docs/agents/data-engineer.md +++ /dev/null @@ -1,45 +0,0 @@ -# Data Engineer Agent - -The Data Engineer Agent guides data pipeline architecture, data quality, and storage decisions for ML/CV projects. - -**Agent directory:** `agents/data-engineer/` - -## Purpose - -This agent provides expert advice on building data pipelines for ML projects. It covers ETL/ELT patterns, data validation with Pydantic, dataset versioning with DVC, storage format selection, data augmentation pipeline design, and data quality monitoring. - -## Strictness Level - -**ADVISORY** — This agent guides decisions but does not block. - -## When to Use - -- Designing data pipelines for training data preparation -- Choosing storage formats (Parquet, Arrow, LMDB, TFRecord) -- Implementing data validation and quality checks -- Planning dataset versioning and lineage tracking -- Designing data augmentation strategies -- Handling schema evolution and migration - -## Example Session - -``` -You: "I need to build a data pipeline for image classification with 100K images" - -Data Engineer: "I recommend this approach: -1. Store raw images in S3/GCS, metadata in Parquet -2. DVC for dataset versioning with remote storage -3. Pydantic models for metadata validation -4. WebDataset or LMDB for training-time access -5. Great Expectations for automated quality checks" -``` - -## Related Skills - -- `dvc` — Data versioning patterns -- `pydantic` — Data validation models -- `testing` — Data pipeline testing strategies - -## Full Reference - -See [`agents/data-engineer/SKILL.md`](https://github.com/ortizeg/whet/blob/main/agents/data-engineer/SKILL.md) for complete patterns. diff --git a/docs/agents/devops-infra.md b/docs/agents/devops-infra.md deleted file mode 100644 index e4e0c37..0000000 --- a/docs/agents/devops-infra.md +++ /dev/null @@ -1,95 +0,0 @@ -# DevOps/Infrastructure Agent - -The DevOps/Infrastructure Agent guides all infrastructure and deployment decisions for ML/CV projects. - -**Agent directory:** `agents/devops-infra/` - -## Purpose - -This agent provides expert advice on packaging, deploying, and operating ML services in production. It covers Docker multi-stage builds, Kubernetes deployments with GPU support, CI/CD pipeline design, monitoring and observability, infrastructure as code, and security hardening. - -## Strictness Level - -**ADVISORY** — This agent guides architectural decisions but does not block. - -## When to Use - -- Containerizing an ML model for deployment -- Choosing between deployment targets (Cloud Run, ECS, Kubernetes) -- Designing CI/CD pipelines for ML projects -- Setting up monitoring and alerting for model serving -- Writing infrastructure as code (Terraform, Kubernetes manifests) -- Reviewing security posture of ML infrastructure - -## Decision Framework - -The agent uses a structured decision tree: - -``` -Need to package an ML application? -├── Single model serving → Dockerfile with multi-stage build -├── Multiple services → Docker Compose (local), K8s (prod) -├── GPU required? -│ ├── Training → NVIDIA base images -│ └── Inference → Optimized runtime images -└── No GPU → Python slim base image -``` - -## Key Patterns - -### Multi-Stage Docker Build - -```dockerfile -FROM python:3.11-slim AS builder -WORKDIR /app -RUN pip install --no-cache-dir uv -COPY pyproject.toml uv.lock ./ -RUN uv sync --frozen --no-dev - -FROM python:3.11-slim AS runtime -WORKDIR /app -COPY --from=builder /app/.venv /app/.venv -COPY src/ ./src/ -RUN useradd --create-home appuser -USER appuser -CMD ["uvicorn", "src.app:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -### Kubernetes Deployment with GPU - -```yaml -spec: - containers: - - name: api - resources: - requests: - nvidia.com/gpu: "1" - limits: - nvidia.com/gpu: "1" - livenessProbe: - httpGet: - path: /health - readinessProbe: - httpGet: - path: /ready -``` - -## Security Checklist - -1. No secrets in code or images -2. Non-root container user -3. Minimal base images, scanned with Trivy -4. Resource limits on all containers -5. Image version pinning with content hashes - -## Related Skills - -- `docker-cv` — Detailed Docker patterns for CV workloads -- `github-actions` — CI/CD workflow definitions -- `gcp` — Google Cloud Platform deployment patterns -- `aws-sagemaker` — AWS managed ML infrastructure -- `fastapi` — The application framework behind deployed services - -## Full Reference - -See [`agents/devops-infra/SKILL.md`](https://github.com/ortizeg/whet/blob/main/agents/devops-infra/SKILL.md) for complete patterns including Docker Compose, Kubernetes HPA, Terraform, and monitoring. diff --git a/docs/agents/expert-coder.md b/docs/agents/expert-coder.md deleted file mode 100644 index 580c83e..0000000 --- a/docs/agents/expert-coder.md +++ /dev/null @@ -1,64 +0,0 @@ -# Expert Coder Agent - -The Expert Coder is the primary coding assistant for all development tasks in AI/CV projects. It guides code generation to follow project standards consistently. - -## Purpose - -Every piece of code generated follows these principles: - -- **Abstraction First** -- wrap external libraries (cv2, PIL, ffmpeg) behind clean interfaces -- **Pydantic Everywhere** -- use `BaseModel` for all configs (Level 1) and data structures (Level 2) -- **Type Safety** -- full type hints on all functions, no `Any` unless documented -- **Testability** -- dependency injection, pure functions, no global state - -## Strictness Level - -**Advisory** -- suggests and guides but does not block. Actual enforcement happens via pre-commit hooks and the Code Review agent in CI. - -## How to Use - -The Expert Coder reads the relevant skill files and generates code following all patterns: - -``` -You: "Create a DataModule for COCO object detection" - -Expert Coder: -1. Reads pytorch-lightning, pydantic, abstraction-patterns skills -2. Creates DataConfig with Pydantic validation -3. Implements LightningDataModule with proper hooks -4. Wraps data loading in abstractions -5. Adds full type hints and docstrings -``` - -## Patterns Enforced - -### Correct: Pydantic Config + Abstraction - -```python -from pydantic import BaseModel, Field - -class DataConfig(BaseModel): - data_dir: str - batch_size: int = Field(ge=1, default=32) - num_workers: int = Field(ge=0, default=4) -``` - -### Incorrect: Raw Dict + Direct Library Use - -```python -# ❌ No validation, no types -config = {"data_dir": "/data", "batch_size": 32} -cap = cv2.VideoCapture("video.mp4") # Direct library use -``` - -## What It Provides - -- Structure guidance for module organization -- Pattern enforcement for abstractions and Pydantic usage -- Type safety with comprehensive type hints -- Documentation with helpful docstrings -- ML/CV-specific best practices - -## Related Skills - -The Expert Coder reads: `pydantic`, `abstraction-patterns`, `code-quality`, `pytorch-lightning`, and others as needed. diff --git a/docs/agents/index.md b/docs/agents/index.md deleted file mode 100644 index 7cfbba4..0000000 --- a/docs/agents/index.md +++ /dev/null @@ -1,31 +0,0 @@ -# Agents Overview - -The whet framework includes six specialized agents that assist with different aspects of ML/CV development. Agents are divided into two categories based on their enforcement level. - -## Agent Summary - -| Agent | Role | Strictness | Deployment | -|-------|------|------------|------------| -| [Expert Coder](expert-coder.md) | Primary coding assistant | Advisory | Claude Code | -| [ML Engineer](ml-engineer.md) | Architecture and training guidance | Advisory | Claude Code | -| [DevOps/Infra](devops-infra.md) | Infrastructure and deployment decisions | Advisory | Claude Code | -| [Data Engineer](data-engineer.md) | Data pipeline architecture and quality | Advisory | Claude Code | -| [Code Review](code-review.md) | Automated quality enforcement | **Blocking** | GitHub Action | -| [Test Engineer](test-engineer.md) | Testing and coverage enforcement | **Blocking** | GitHub Action | - -## Advisory vs Blocking - -**Advisory agents** guide development but do not prevent actions. They suggest improvements, explain rationale, and generate code following standards. The Expert Coder, ML Engineer, DevOps/Infra, and Data Engineer agents operate in this mode through Claude Code. - -**Blocking agents** run in CI and must pass before a pull request can be merged. The Code Review and Test Engineer agents operate as GitHub Actions that gate merges on code quality, type safety, test coverage, and security checks. - -## How They Work Together - -1. **During development**, the Expert Coder generates code following project patterns (abstractions, Pydantic, type hints). -2. **For ML decisions**, the ML Engineer reviews model architecture, training setup, and experiment configuration. -3. **For infrastructure**, the DevOps/Infra agent guides Docker, Kubernetes, CI/CD, and cloud deployment decisions. -4. **For data pipelines**, the Data Engineer agent advises on ETL patterns, validation, versioning, and storage format selection. -5. **On push/PR**, the Code Review agent validates formatting, linting, types, and security. -6. **On push/PR**, the Test Engineer runs the full test suite and enforces coverage thresholds. - -This layered approach provides guidance during development and enforcement during integration. diff --git a/docs/agents/ml-engineer.md b/docs/agents/ml-engineer.md deleted file mode 100644 index 4908788..0000000 --- a/docs/agents/ml-engineer.md +++ /dev/null @@ -1,63 +0,0 @@ -# ML Engineer Agent - -Advisory agent specialized in model architecture, training pipelines, experiment management, and CV-specific optimization. - -## Purpose - -The ML Engineer reviews and suggests improvements for: - -- **Model Architecture** -- layer choices, regularization, transfer learning, bottleneck identification -- **Training Pipelines** -- loss functions, optimizers, LR schedules, gradient management -- **Experiment Management** -- hyperparameter selection, metric tracking, reproducibility -- **CV-Specific Knowledge** -- augmentation strategies, multi-scale training, detection/segmentation patterns - -## Strictness Level - -**Advisory** -- suggests but does not block. - -## Common Pitfalls It Catches - -### Missing Gradient Clipping - -```python -# ❌ Risk of gradient explosion -optimizer.step() - -# ✅ Clip gradients -torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) -``` - -### Wrong Normalization - -```python -# ❌ Generic normalization -normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - -# ✅ ImageNet normalization for pretrained models -normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) -``` - -### Batch Norm After Dropout - -```python -# ❌ Wrong order -- BN after dropout is unstable -nn.Sequential(nn.Conv2d(64, 128, 3), nn.Dropout(0.5), nn.BatchNorm2d(128)) - -# ✅ Correct order -- BN before dropout -nn.Sequential(nn.Conv2d(64, 128, 3), nn.BatchNorm2d(128), nn.ReLU(), nn.Dropout(0.5)) -``` - -## Recommendations by Task - -| Task | Architecture | Loss | Augmentation | -|------|-------------|------|-------------| -| Object Detection | FCOS, DETR + FPN | Focal Loss | Mosaic, MixUp | -| Segmentation | U-Net, DeepLab | Dice + CE | Elastic, ColorJitter | -| Classification | ResNet, EfficientNet | Label Smoothing CE | MixUp, CutMix | - -## When to Use - -- Designing new model architectures -- Debugging training instability (loss spikes, NaN gradients) -- Optimizing training performance (speed, memory) -- Reviewing experiment setup for reproducibility diff --git a/docs/agents/test-engineer.md b/docs/agents/test-engineer.md deleted file mode 100644 index 4fbd9fe..0000000 --- a/docs/agents/test-engineer.md +++ /dev/null @@ -1,117 +0,0 @@ -# Test Engineer Agent - -Automated testing and coverage enforcement that runs as a GitHub Action. Must pass before any PR can be merged. - -## Strictness Level - -**Blocking** -- all tests must pass and coverage thresholds must be met. - -## Coverage Requirements - -| Component | Minimum Coverage | -|-----------|-----------------| -| Core logic (models, pipelines) | 90% | -| Data pipelines | 80% | -| Utilities | 70% | -| **Overall project** | **80%** | - -## Test Structure - -``` -tests/ -├── unit/ # Fast, isolated tests -│ ├── test_models.py -│ ├── test_data.py -│ └── test_utils.py -├── integration/ # Multi-component tests -│ ├── test_training.py -│ └── test_pipeline.py -├── e2e/ # End-to-end tests -│ └── test_full_workflow.py -├── conftest.py # Shared fixtures -└── test_data/ # Test fixtures (sample images, configs) -``` - -## Test Patterns - -### AAA Pattern (Arrange, Act, Assert) - -```python -def test_model_forward_pass(): - # Arrange - config = ModelConfig(input_channels=3, num_classes=10) - model = MyModel(config) - batch = torch.randn(2, 3, 32, 32) - - # Act - output = model(batch) - - # Assert - assert output.shape == (2, 10), f"Expected (2, 10), got {output.shape}" -``` - -### Parametrized Tests - -```python -@pytest.mark.parametrize("batch_size,height,width", [ - (1, 32, 32), (4, 64, 64), (8, 224, 224), -]) -def test_model_input_sizes(batch_size, height, width): - model = MyModel(config) - output = model(torch.randn(batch_size, 3, height, width)) - assert output.shape[0] == batch_size -``` - -### CV-Specific: Synthetic Data - -```python -def test_detector_with_synthetic_image(): - image = torch.rand(3, 640, 640) - detector = YOLODetector(config) - detections = detector.detect(image) - - for det in detections: - assert 0 <= det.bbox.confidence <= 1 - assert det.bbox.width > 0 -``` - -## GitHub Action Setup - -```yaml -name: Test Suite - -on: [push, pull_request] - -jobs: - test: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.1 - with: - pixi-version: latest - - run: uv run pytest --cov=src --cov-fail-under=80 -``` - -## Local Usage - -```bash -# Run all tests -uv run test - -# Run with coverage report -uv run test-cov - -# Run specific test file -uv run pytest tests/unit/test_models.py -v - -# Run tests matching pattern -uv run pytest -k "test_model" -``` - -## Additional Requirements - -- **No skipped tests** -- fix or remove them -- **Tests must be typed** -- type hints on test functions -- **Fast tests** -- unit tests should run in milliseconds -- **Independent tests** -- no test should depend on another diff --git a/docs/archetypes/data-processing-pipeline.md b/docs/archetypes/data-processing-pipeline.md index 4cf0b5b..01e2b56 100644 --- a/docs/archetypes/data-processing-pipeline.md +++ b/docs/archetypes/data-processing-pipeline.md @@ -1,10 +1,10 @@ # Data Processing Pipeline -ETL workflows for dataset preparation, transformation, and validation with parallel processing and DVC integration. +ETL workflows for dataset preparation, transformation, and validation with parallel processing and dataset versioning. ## Purpose -This archetype structures dataset processing as a series of well-defined stages: download, preprocess, validate, split, and package. Each stage is a self-contained module with Pydantic-validated configuration, making pipelines reproducible and composable. DVC tracks large data assets, and parallel processing handles large-scale datasets efficiently. +This archetype structures dataset processing as a series of well-defined stages: download, preprocess, validate, split, and package. Each stage is a self-contained module with Pydantic-validated configuration, making pipelines reproducible and composable. Large data assets live in object storage and are tracked by a content-hashed manifest, and parallel processing handles large-scale datasets efficiently. ## Directory Structure @@ -36,7 +36,6 @@ This archetype structures dataset processing as a series of well-defined stages: │ ├── raw/ │ ├── processed/ │ └── splits/ -├── dvc.yaml # DVC pipeline definition ├── tests/ │ ├── test_stages.py │ └── test_pipeline.py @@ -69,9 +68,6 @@ uv run python -m my_project.pipeline # Run single stage uv run python -m my_project.pipeline stage=preprocess - -# Reproduce with DVC -dvc repro ``` ## Customization @@ -79,4 +75,4 @@ dvc repro - Add new stages in `src/{{package_name}}/stages/` - Define stage configs in `configs/stages/` - Add image transforms in `transforms/` -- Configure DVC remotes for data storage +- Configure the object storage bucket and prefix used for dataset artifacts diff --git a/docs/archetypes/index.md b/docs/archetypes/index.md index 8e8f1b7..c80053d 100644 --- a/docs/archetypes/index.md +++ b/docs/archetypes/index.md @@ -10,7 +10,7 @@ Archetypes are project templates for common AI/CV workflows. Each archetype prov | [CV Inference Service](cv-inference-service.md) | Production model serving | FastAPI, ONNX Runtime, Docker | | [Research Notebook](research-notebook.md) | Jupyter experimentation | Jupyter, matplotlib, Lightning | | [Library Package](library-package.md) | Reusable Python packages | PyPI, docs, semver | -| [Data Pipeline](data-processing-pipeline.md) | Dataset ETL workflows | DVC, parallel processing | +| [Data Pipeline](data-processing-pipeline.md) | Dataset ETL workflows | Pydantic, parallel processing | | [Model Zoo](model-zoo.md) | Pretrained model collections | Model cards, benchmarks | ## Common Base Structure diff --git a/docs/getting-started/first-project.md b/docs/getting-started/first-project.md index 57e0f89..bb86c1b 100644 --- a/docs/getting-started/first-project.md +++ b/docs/getting-started/first-project.md @@ -209,5 +209,5 @@ uv run python src/object_detector/train.py trainer.devices=4 trainer.strategy=dd ## Next Steps - Explore individual [skill documentation](../skills/index.md) for deeper dives -- Read about [agent personas](../agents/index.md) to adjust Claude's behavior +- Browse the [skills overview](../skills/index.md) to add more capabilities - Check out [common patterns](../examples/common-patterns.md) for more examples diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md index 064eef1..55e7aa0 100644 --- a/docs/getting-started/quick-start.md +++ b/docs/getting-started/quick-start.md @@ -136,4 +136,4 @@ Claude Code uses this context to generate code that follows the same patterns co - [First Project](first-project.md) -- A detailed walkthrough building a complete project - [Skills Overview](../skills/index.md) -- Browse all available skills -- [Agents](../agents/index.md) -- Learn about agent personas for different task types +- [Archetypes](../archetypes/index.md) -- Project templates for common CV/ML project types diff --git a/docs/guides/best-practices.md b/docs/guides/best-practices.md index b4c8aac..947a56b 100644 --- a/docs/guides/best-practices.md +++ b/docs/guides/best-practices.md @@ -112,7 +112,7 @@ print(f"Training started with lr={lr}") ## Data Management -- **DVC** for versioning large datasets and model weights +- **Object storage plus a versioned manifest** for large datasets and model weights -- keep the bulk data out of Git - **Pydantic** for validating data schemas - **Explicit splits** -- never let data leak between train/val/test - **Data validation** -- check for corrupted images, missing labels, class imbalance diff --git a/docs/guides/creating-projects.md b/docs/guides/creating-projects.md index 8fa90f3..44cf44c 100644 --- a/docs/guides/creating-projects.md +++ b/docs/guides/creating-projects.md @@ -58,7 +58,6 @@ Choose which experiment tracking and data management tools to include: | Weights & Biases | Cloud experiment tracking with rich media | | MLflow | Self-hosted experiment tracking + model registry | | TensorBoard | Local training visualization | -| DVC | Data and model version control | ## Step 6: Project Generation @@ -148,6 +147,5 @@ The Master Skill orchestrates these skills during generation: 5. **testing** -- sets up pytest configuration 6. **pytorch-lightning** -- adds Lightning patterns (if training archetype) 7. **wandb/mlflow/tensorboard** -- adds tracking integration (if selected) -8. **dvc** -- initializes DVC (if selected) Each skill contributes its specific configuration and code patterns to the generated project. diff --git a/docs/index.md b/docs/index.md index 7345b4c..9348a1d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,14 +6,13 @@ ## What Is This? -AI/CV Claude Skills is a curated collection of **32 specialized skills**, **6 agent personas**, and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. +AI/CV Claude Skills is a curated collection of **32 specialized skills** and **6 project archetypes** that teach Claude Code how to write production-grade computer vision and deep learning code. Instead of getting generic AI-generated code, you get output that follows the exact patterns, libraries, and conventions used by experienced CV/ML engineers. Each skill is a focused knowledge module that Claude Code loads on demand. When you tell Claude to "use the PyTorch Lightning skill," it gains deep understanding of Lightning best practices, project structure, and common patterns -- producing code that looks like it was written by a specialist. ## Key Features - **32 Domain-Specific Skills** -- From PyTorch Lightning training loops to ONNX model export and model evaluation, each skill encodes expert knowledge about a specific tool or pattern. -- **6 Agent Personas** -- Pre-configured behavioral profiles (Expert Coder, ML Engineer, DevOps/Infra, Data Engineer, Code Reviewer, Test Engineer) that adjust Claude's strictness, focus, and output style. - **6 Project Archetypes** -- Complete project templates for common CV/ML project types: training pipelines, inference services, research notebooks, library packages, data pipelines, and model zoos. - **Composable by Design** -- Skills combine naturally. A PyTorch training project might use the PyTorch Lightning, Hydra Config, Weights & Biases, and Docker CV skills together. - **Production Standards** -- Every skill enforces type hints, comprehensive error handling, proper logging, and test coverage. No prototype-quality code. @@ -50,9 +49,6 @@ whet/ pytorch-lightning/ # PyTorch Lightning training patterns pydantic/ # Strict data validation with Pydantic ... - agents/ # 6 agent persona definitions - expert-coder/ # High-strictness coding agent - ml-engineer/ # ML-focused development agent ... archetypes/ # 6 project templates pytorch-training-project/ @@ -77,7 +73,6 @@ Ready to dive in? Head to the [Installation](getting-started/installation.md) gu |---------|-------------| | [Getting Started](getting-started/installation.md) | Install prerequisites, clone the repo, and configure Claude Code | | [Skills Overview](skills/index.md) | Browse all 32 skills with categories and descriptions | -| [Agents](agents/index.md) | Learn about the 6 agent personas and when to use each | | [Archetypes](archetypes/index.md) | Explore the 6 project templates | | [Guides](guides/creating-projects.md) | Step-by-step guides for common workflows | | [Examples](examples/full-workflow.md) | End-to-end examples with real code | diff --git a/docs/skills/aws-sagemaker.md b/docs/skills/aws-sagemaker.md index 33dfd69..52cba2a 100644 --- a/docs/skills/aws-sagemaker.md +++ b/docs/skills/aws-sagemaker.md @@ -90,7 +90,6 @@ pipeline = Pipeline( - **PyTorch Lightning** -- LightningModule inside SageMaker training jobs - **Docker CV** -- Custom training containers when default images are insufficient - **W&B / MLflow** -- Experiment tracking inside SageMaker training containers -- **DVC** -- Data versioning with S3 remote storage - **GCP** -- Alternative cloud platform patterns for comparison ## Full Reference diff --git a/docs/skills/data-pipelines.md b/docs/skills/data-pipelines.md new file mode 100644 index 0000000..969672b --- /dev/null +++ b/docs/skills/data-pipelines.md @@ -0,0 +1,101 @@ +# Data Pipelines + +The data-pipelines skill covers dataset and ETL/ELT design for CV/ML projects: validated manifests, leakage-free splitting, storage format selection, data quality validation, augmentation design, large-scale processing, and schema evolution. + +**Skill directory:** `skills/data-pipelines/` + +## Purpose + +Model quality is bounded by data quality. Most silent failures in computer vision projects are data failures: near-duplicate frames leaking across splits, corrupt images that never raised an error, a schema that gained a column without anyone recording it, or a CSV manifest that became the bottleneck at 10 million rows. This skill teaches Claude Code to build data layers that are reproducible and self-checking -- content-hashed manifests, group-aware splits, validation gates between pipeline stages, and versioned schemas with explicit migrations. + +## When to Use + +- Designing an ingestion or ETL pipeline for image, video, or tabular ML data +- Splitting a dataset into train/val/test, especially with correlated samples +- Investigating validation metrics that look too good to be true +- Choosing between Parquet, LMDB, WebDataset, TFRecord, Arrow, and raw files +- Writing data quality checks or Great Expectations suites +- Processing datasets larger than memory +- Adding or changing fields on an existing dataset + +## Key Patterns + +### Group-Aware Splitting + +Whole groups -- videos, patients, matches, sessions -- go to exactly one split, and the pipeline itself asserts disjointness. + +```python +groups = df[key].unique().sample(fraction=1.0, seed=config.seed, shuffle=True) +n_train = int(len(groups) * config.train_ratio) +n_val = int(len(groups) * config.val_ratio) + +train_groups = set(groups[:n_train].to_list()) +val_groups = set(groups[n_train : n_train + n_val].to_list()) +test_groups = set(groups[n_train + n_val :].to_list()) + +if not train_groups.isdisjoint(test_groups): + msg = "train/test group overlap — data leakage" + raise ValueError(msg) +``` + +### Validated Manifest + +```python +class ImageRecord(BaseModel): + """Validated image metadata record.""" + + file_hash: str = Field(min_length=64, max_length=64) # SHA-256 + relative_path: str + width: int = Field(ge=1) + height: int = Field(ge=1) + label: str + group_id: str | None = None # patient / video / scene identifier +``` + +The manifest is written as Parquet with `compression="zstd"`, optionally Hive-partitioned by split so readers can skip whole partitions. + +### Storage Format Selection + +| Data | Format | Why | +| --- | --- | --- | +| Tabular metadata | Parquet | Columnar, compressed, fast predicate filtering | +| Distributed training | WebDataset | `.tar` shards, sequential throughput, S3-friendly | +| Single-node random access | LMDB | Memory-mapped, zero-copy reads | +| Cross-language interchange | Arrow IPC | Zero-copy, language-agnostic | +| Small or churning datasets | Raw files + Parquet manifest | Simplest and debuggable | + +### Schema Evolution + +```python +class SchemaV3(SchemaV2): + """V3 — adds split assignment and quality score.""" + split: str = Field(pattern=r"^(train|val|test)$") + quality_score: float = Field(ge=0.0, le=1.0, default=1.0) + + +def migrate_v2_to_v3(record: SchemaV2, split: str, quality_score: float = 1.0) -> SchemaV3: + """Enrich a V2 record with its split assignment and quality score.""" + return SchemaV3(**record.model_dump(), split=split, quality_score=quality_score) +``` + +## Anti-Patterns + +- Do not random-split a DataFrame when a grouping key exists -- correlated samples leak across train and test +- Do not skip stratification when there is no grouping key -- imbalanced splits make metrics unreliable +- Do not modify raw data in place -- write transformations to a separate directory +- Do not use CSV for large datasets -- Parquet, LMDB, or WebDataset instead +- Do not apply training augmentations to val or test sets +- Do not change a dataset schema silently -- version it and write a migration +- Do not assume a public or "curated" dataset is clean -- run the quality gate anyway +- Do not commit data to Git -- keep the bulk data in object storage and version a manifest + +## Combines Well With + +- **Pydantic** -- Validated configs and per-record schema enforcement +- **PyTorch Lightning** -- `LightningDataModule` consuming the validated splits +- **Model Evaluation** -- Scoring models on splits built without leakage +- **Testing** -- Unit tests for transforms, integration tests for pipeline stages + +## Full Reference + +See [`skills/data-pipelines/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/data-pipelines/SKILL.md) for the full decision matrices, Great Expectations suite, augmentation config pattern, chunked processing utilities, and schema registry. diff --git a/docs/skills/dvc.md b/docs/skills/dvc.md deleted file mode 100644 index 02fcabc..0000000 --- a/docs/skills/dvc.md +++ /dev/null @@ -1,102 +0,0 @@ -# DVC - -The DVC skill covers data and model version control using DVC (Data Version Control), including remote storage, pipeline definitions, and experiment tracking for large binary assets. - -**Skill directory:** `skills/dvc/` - -## Purpose - -Git is not designed for large binary files -- datasets, model weights, and video collections quickly bloat repositories. DVC provides Git-like version control for these assets, storing the actual files in remote storage (S3, GCS, Azure, SSH) while keeping lightweight pointer files in Git. This skill teaches Claude Code to configure DVC for CV/ML projects, define reproducible data pipelines, and manage dataset versions alongside code versions. - -## When to Use - -- Projects with datasets too large for Git (images, videos, point clouds) -- Model weight versioning across training runs -- Reproducible data processing pipelines (download -> preprocess -> train) -- Team workflows where multiple people need access to the same data versions - -## Key Patterns - -### DVC Initialization - -```bash -# Initialize DVC in an existing Git repo -dvc init - -# Configure remote storage -dvc remote add -d myremote s3://my-bucket/dvc-store -dvc remote modify myremote region us-east-1 -``` - -### Tracking Data - -```bash -# Track a dataset directory -dvc add data/images/ - -# This creates data/images.dvc (pointer file) and adds data/images/ to .gitignore -git add data/images.dvc data/.gitignore -git commit -m "Track training images with DVC" - -# Push data to remote -dvc push -``` - -### Pipeline Definition - -```yaml -# dvc.yaml -stages: - download: - cmd: python scripts/download_data.py --output data/raw/ - outs: - - data/raw/ - - preprocess: - cmd: python scripts/preprocess.py --input data/raw/ --output data/processed/ - deps: - - data/raw/ - - scripts/preprocess.py - outs: - - data/processed/ - - train: - cmd: python src/my_project/train.py - deps: - - data/processed/ - - src/my_project/train.py - - configs/ - outs: - - models/best.ckpt - metrics: - - metrics.json: - cache: false -``` - -### Reproduce Pipelines - -```bash -# Run the full pipeline (only re-runs changed stages) -dvc repro - -# Pull data from remote on a new machine -dvc pull -``` - -## Anti-Patterns to Avoid - -- Do not commit large files to Git and then add DVC later -- initialize DVC at project start -- Do not use DVC for files under 10 MB -- Git handles those fine -- Avoid storing DVC cache on network-mounted filesystems -- it degrades performance -- Do not forget to `dvc push` after adding new data -- your teammates cannot `dvc pull` otherwise - -## Combines Well With - -- **PyTorch Lightning** -- Version model checkpoints alongside training code -- **Hydra Config** -- Pipeline parameters in DVC params files -- **GitHub Actions** -- DVC pull in CI for integration tests -- **Docker CV** -- Mount DVC-managed data into training containers - -## Full Reference - -See [`skills/dvc/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/dvc/SKILL.md) for patterns including DVC experiments, metrics comparison across branches, and integration with cloud storage providers. diff --git a/docs/skills/index.md b/docs/skills/index.md index 36745ab..239a231 100644 --- a/docs/skills/index.md +++ b/docs/skills/index.md @@ -76,7 +76,7 @@ Skills for packaging, deploying, and maintaining projects. | [GitHub Repo Setup](github-repo-setup.md) | Repository initialization and configuration | gh CLI | | [Pre-commit](pre-commit.md) | Git hook automation | pre-commit | | [VS Code](vscode.md) | Editor configuration for ML development | VS Code | -| [DVC](dvc.md) | Data and model version control | DVC | +| [Data Pipelines](data-pipelines.md) | ETL, storage formats, leakage-free splitting, schema evolution | polars, Great Expectations | ### Process & Review @@ -97,7 +97,7 @@ Skills for code review and architectural decisions. | **Building models** | PyTorch Lightning, Hydra Config | | **Training & experiments** | W&B or MLflow, TensorBoard | | **Preparing for production** | Docker CV, ONNX, Testing, Pre-commit | -| **Publishing & sharing** | PyPI, GitHub Actions, DVC | +| **Publishing & sharing** | PyPI, GitHub Actions | ### By Role @@ -106,7 +106,7 @@ Skills for code review and architectural decisions. | **Researcher** | PyTorch Lightning, Hydra Config, Matplotlib, TensorBoard | | **ML Engineer** | All Core + Training + Infrastructure | | **CV Engineer** | OpenCV, PyTorch Lightning, ONNX, Docker CV | -| **DevOps/MLOps** | Docker CV, GitHub Actions, DVC, Pixi | +| **DevOps/MLOps** | Docker CV, GitHub Actions, Pixi | ## Combining Skills diff --git a/docs/skills/mlflow.md b/docs/skills/mlflow.md index a596419..2f69f88 100644 --- a/docs/skills/mlflow.md +++ b/docs/skills/mlflow.md @@ -81,7 +81,7 @@ with mlflow.start_run(run_name="resnet50-baseline") as run: - Do not use the default `mlruns/` local directory in production -- configure a proper tracking URI - Do not register models without validation metrics -- always log metrics alongside the model -- Avoid logging large artifacts (datasets, raw images) as run artifacts -- use DVC or cloud storage +- Avoid logging large artifacts (datasets, raw images) as run artifacts -- keep them in cloud object storage and log a reference - Do not mix MLflow and W&B in the same project unless migrating ## Combines Well With @@ -89,7 +89,6 @@ with mlflow.start_run(run_name="resnet50-baseline") as run: - **PyTorch Lightning** -- MLFlowLogger integrates with Lightning Trainer - **Hydra Config** -- Log resolved configs as run parameters - **Docker CV** -- MLflow tracking server in a container -- **DVC** -- DVC manages data, MLflow manages experiments and models ## Full Reference diff --git a/docs/skills/pre-commit.md b/docs/skills/pre-commit.md index 15cfe3b..e44bdc5 100644 --- a/docs/skills/pre-commit.md +++ b/docs/skills/pre-commit.md @@ -84,4 +84,4 @@ uv run pre-commit run --all-files ## Full Reference -See [`skills/pre-commit/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pre-commit/SKILL.md) for patterns including custom local hooks for ML-specific validation, commit message linting, and DVC file validation hooks. +See [`skills/pre-commit/SKILL.md`](https://github.com/ortizeg/whet/blob/main/skills/pre-commit/SKILL.md) for patterns including custom local hooks for ML-specific validation, commit message linting, and large-artifact validation hooks. diff --git a/mkdocs.yml b/mkdocs.yml index 045ed40..958581e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -80,7 +80,7 @@ nav: - Weights & Biases: skills/wandb.md - MLflow: skills/mlflow.md - TensorBoard: skills/tensorboard.md - - DVC: skills/dvc.md + - Data Pipelines: skills/data-pipelines.md - ONNX: skills/onnx.md - TensorRT: skills/tensorrt.md - Abstraction Patterns: skills/abstraction-patterns.md @@ -91,14 +91,6 @@ nav: - PydanticAI: skills/pydantic-ai.md - Gradio: skills/gradio.md - Kubernetes: skills/kubernetes.md - - Agents: - - Overview: agents/index.md - - Expert Coder: agents/expert-coder.md - - ML Engineer: agents/ml-engineer.md - - Code Review: agents/code-review.md - - Test Engineer: agents/test-engineer.md - - DevOps/Infra: agents/devops-infra.md - - Data Engineer: agents/data-engineer.md - Archetypes: - Overview: archetypes/index.md - PyTorch Training: archetypes/pytorch-training-project.md diff --git a/skills/README.md b/skills/README.md index 2e6df97..41f4440 100644 --- a/skills/README.md +++ b/skills/README.md @@ -48,7 +48,6 @@ Each skill includes: | `wandb` | Weights & Biases | | `mlflow` | MLflow tracking | | `tensorboard` | TensorBoard logging | -| `dvc` | Data versioning | ### Meta | Skill | Purpose | diff --git a/skills/aws-sagemaker/README.md b/skills/aws-sagemaker/README.md index 885fd2b..018525e 100644 --- a/skills/aws-sagemaker/README.md +++ b/skills/aws-sagemaker/README.md @@ -28,5 +28,4 @@ When you need to train models at scale on AWS infrastructure or deploy models be - **[PyTorch Lightning](../pytorch-lightning/)** — LightningModule patterns used inside SageMaker training scripts. - **[Docker CV](../docker-cv/)** — custom training container images when default SageMaker images are insufficient. - **[W&B](../wandb/)** / **[MLflow](../mlflow/)** — experiment tracking inside SageMaker training containers. -- **[DVC](../dvc/)** — data versioning with S3 remote storage. - **[GCP](../gcp/)** — alternative cloud platform patterns for comparison. diff --git a/skills/aws-sagemaker/SKILL.md b/skills/aws-sagemaker/SKILL.md index d7f3420..e0e8cb5 100644 --- a/skills/aws-sagemaker/SKILL.md +++ b/skills/aws-sagemaker/SKILL.md @@ -452,4 +452,3 @@ def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: - **PyTorch Lightning** — LightningModule inside training jobs; **Hydra Config** — hyperparameters flattened to key-value pairs. - **W&B / MLflow** — experiment tracking in containers; **Docker CV** — custom containers when built-in images fall short. -- **DVC** — data versioning with an S3 remote SageMaker can access. diff --git a/skills/aws-sagemaker/skill.toml b/skills/aws-sagemaker/skill.toml index d782d12..d7eca1c 100644 --- a/skills/aws-sagemaker/skill.toml +++ b/skills/aws-sagemaker/skill.toml @@ -6,7 +6,7 @@ tags = ["aws", "sagemaker", "training", "deployment", "cloud", "mlops"] [dependencies] requires = ["pydantic", "loguru"] -recommends = ["pytorch-lightning", "docker-cv", "wandb", "dvc"] +recommends = ["pytorch-lightning", "docker-cv", "wandb"] [compatibility] python = ">=3.11" diff --git a/skills/data-pipelines/README.md b/skills/data-pipelines/README.md new file mode 100644 index 0000000..7058637 --- /dev/null +++ b/skills/data-pipelines/README.md @@ -0,0 +1,50 @@ +# Data Pipelines Skill + +## Purpose + +This skill covers the design of dataset and ETL/ELT pipelines for computer vision +and machine learning projects: building validated dataset manifests, splitting data +without leakage, selecting a storage format, validating data quality, designing +augmentation pipelines, processing data at scale, and evolving dataset schemas over +time. The purpose is to make the data layer reproducible and trustworthy, so that +training metrics reflect generalization rather than accidental memorization. + +The highest-value part of the skill is group-aware splitting: frames from the same +video, images of the same patient, or plays from the same match must never straddle +train, val, and test. + +## When to Use + +Reference this skill when: + +- Designing a data ingestion or ETL pipeline for image, video, or tabular ML data. +- Splitting a dataset into train/val/test, especially when samples are correlated. +- Diagnosing suspiciously high validation scores that may come from data leakage. +- Choosing a storage format: Parquet, LMDB, WebDataset, TFRecord, Arrow, or raw files. +- Writing data quality checks or Great Expectations suites for a dataset. +- Designing an augmentation pipeline that stays deterministic on val and test. +- Processing datasets that exceed memory (chunked parallel work, streaming reads). +- Changing the fields of an existing dataset and needing a migration path. + +Use `model-evaluation` for scoring models on the splits this skill produces. + +## Key Patterns + +- **Validated manifest** — one Pydantic-checked row per sample, with a SHA-256 + content hash, written as compressed Parquet. +- **Group-aware splitting** — assign whole groups to a split and assert disjointness + in the pipeline, not only in tests. +- **Stratified splitting** — per-class random split, used only when no grouping key + exists. +- **Storage decision matrix** — Parquet for tabular, LMDB for single-node random + access, WebDataset for distributed/object-storage training, raw files while small. +- **Two-layer validation** — Pydantic per record, Great Expectations in aggregate, + plus a cheap in-pipeline gate run after extraction and after splitting. +- **Config-driven augmentation** — a single `strength` knob scales training + augmentations; val/test transforms stay deterministic. +- **Chunked parallel processing** — per-item error isolation, progress logging, and + lazy `scan_parquet` slices for data larger than RAM. +- **Versioned schemas** — one model per version, an explicit migration per hop, and + `schema_version` stored alongside the data. + +See `SKILL.md` for complete documentation and code examples. diff --git a/skills/data-pipelines/SKILL.md b/skills/data-pipelines/SKILL.md new file mode 100644 index 0000000..ab4ec37 --- /dev/null +++ b/skills/data-pipelines/SKILL.md @@ -0,0 +1,475 @@ +--- +name: data-pipelines +description: > + Use this skill when designing a dataset or ETL/ELT pipeline, choosing a storage format + (Parquet, LMDB, WebDataset, TFRecord), splitting a dataset into train/val/test, preventing + data leakage across groups (patient, video, match, player, scene), validating data quality, + evolving a dataset schema, or processing image/video data at a scale that exceeds memory. + Reach for it any time you would otherwise glob a directory, random-split a DataFrame, or + hand-write a CSV manifest, even if the user doesn't say "pipeline" or "ETL" explicitly. + Not for scoring or comparing models (see `model-evaluation`). +--- + +# Data Pipelines for CV/ML + +Reproducible, scalable pipelines that feed ML training and inference. + +## Core Principles + +1. **Data quality first.** No model compensates for bad data. Validate early and often. +2. **Reproducibility.** Every version, transformation, and split is traceable via content hashes and seeded, deterministic code. +3. **Schema as contract.** Schemas bind pipeline stages together. Changes require explicit migrations, never silent mutations. +4. **Immutable datasets.** Published versions are never modified in place; new versions carry lineage to their source. +5. **Fail fast, fail loud.** Raise on quality violations immediately rather than propagating corrupt data downstream. + +```bash +pixi add polars pillow lmdb +pixi add --pypi great-expectations albumentations webdataset +``` + +## Pipeline Architecture + +``` +Building a data pipeline? +├── Small (< 10 GB) ............ Polars / Pandas +├── Medium (10–500 GB) ......... Polars / DuckDB + Parquet +├── Large (500 GB – 10 TB) ..... Arrow / Spark + cloud object storage +├── Streaming / continuous ..... Kafka / Flink + Delta Lake +└── Annotation pipeline ........ Label Studio / CVAT + validation hooks +``` + +A pipeline is a fixed sequence of named stages — `extract → validate_raw → +transform → validate_transformed → load` — with a real gate between each. +Configure it with Pydantic so bad parameters fail at load time, not three hours in. + +```python +"""Data pipeline configuration.""" + +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, Field, ValidationInfo, field_validator + + +class PipelineConfig(BaseModel): + """Top-level data pipeline configuration.""" + + name: str = Field(min_length=1) + source_dir: Path + output_dir: Path + storage_format: Literal["parquet", "arrow", "lmdb", "tfrecord", "webdataset"] = "parquet" + num_workers: int = Field(ge=1, le=64, default=8) + schema_version: str = Field(pattern=r"^\d+\.\d+\.\d+$", default="1.0.0") + + @field_validator("output_dir") + @classmethod + def output_must_differ_from_source(cls, v: Path, info: ValidationInfo) -> Path: + """Prevent writing transformed data over the raw inputs.""" + if v == info.data.get("source_dir"): + msg = "output_dir must differ from source_dir" + raise ValueError(msg) + return v +``` + +The anti-pattern this replaces: a bare `process_data(input_path, output_path)` that +globs `*.jpg` and rewrites images with no schema, no logging, and no guard against +clobbering the raw inputs. + +## ETL: Building a Dataset Manifest + +The manifest is the source of truth: one validated row per sample, carrying a +content hash for deduplication and integrity, written as Parquet (never CSV). + +```python +"""ETL for image datasets: hash, validate, write a Parquet manifest.""" + +import hashlib +from pathlib import Path + +import polars as pl +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ImageRecord(BaseModel): + """Validated image metadata record.""" + + file_hash: str = Field(min_length=64, max_length=64) # SHA-256 + relative_path: str + width: int = Field(ge=1) + height: int = Field(ge=1) + channels: int = Field(ge=1, le=4) + label: str + group_id: str | None = None # patient / video / scene identifier + file_size_bytes: int = Field(ge=1) + + +def compute_file_hash(path: Path) -> str: + """SHA-256 of file contents, for content-addressable storage.""" + hasher = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(8192), b""): + hasher.update(chunk) + return hasher.hexdigest() + + +def build_manifest(paths: list[Path], labels: dict[Path, str], out: Path) -> pl.DataFrame: + """Read image metadata into validated records and write them as Parquet.""" + records = [] + for path in paths: + with Image.open(path) as img: + width, height, channels = *img.size, len(img.getbands()) + records.append( + ImageRecord( + file_hash=compute_file_hash(path), + relative_path=str(path), + width=width, + height=height, + channels=channels, + label=labels[path], + file_size_bytes=path.stat().st_size, + ).model_dump() + ) + df = pl.DataFrame(records) + df.write_parquet(out, compression="zstd") + logger.info("Manifest written: {} records to {}", len(df), out) + return df +``` + +Hashes deduplicate byte-identical images that would otherwise straddle splits and +detect corruption between runs. `split` is deliberately *not* a manifest field: it +is assigned downstream, so a dataset can be re-split without re-extracting. + +## Dataset Splitting Without Leakage + +**This is the highest-risk step in any CV pipeline.** Correlated samples — frames +from the same video, images of the same patient, plays from the same match, crops +of the same player — are near-duplicates. If they straddle train/val/test, +validation measures memorization rather than generalization and the model fails +silently in production. Split on the *group*, never on the row, whenever a grouping +key exists: video/clip ID, patient/subject ID, match/session ID, player identity, +camera or site ID, or capture date for time-correlated data. Stratify only when no +such key exists. + +```python +"""Dataset splitting with stratification and group-aware leak prevention.""" + +from __future__ import annotations # for the SplitConfig self-reference + +import polars as pl +from loguru import logger +from pydantic import BaseModel, Field, model_validator + + +class SplitConfig(BaseModel): + """Configuration for dataset splitting.""" + + train_ratio: float = Field(gt=0.0, lt=1.0, default=0.7) + val_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + test_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + seed: int = 42 + stratify_column: str = "label" + group_column: str | None = None # set whenever a grouping key exists + + @model_validator(mode="after") + def ratios_must_sum_to_one(self) -> SplitConfig: + total = self.train_ratio + self.val_ratio + self.test_ratio + if abs(total - 1.0) > 1e-6: + msg = f"Split ratios must sum to 1.0, got {total}" + raise ValueError(msg) + return self + + +def split_dataset(manifest: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Group-aware split when group_column is set, otherwise stratified.""" + if config.group_column is not None: + return group_aware_split(manifest, config) + return stratified_split(manifest, config) + + +def group_aware_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Assign whole groups to a split so no group straddles train/val/test.""" + key = config.group_column + assert key is not None + groups = df[key].unique().sample(fraction=1.0, seed=config.seed, shuffle=True) + n_train = int(len(groups) * config.train_ratio) + n_val = int(len(groups) * config.val_ratio) + + train_groups = set(groups[:n_train].to_list()) + val_groups = set(groups[n_train : n_train + n_val].to_list()) + test_groups = set(groups[n_train + n_val :].to_list()) + + # Leakage checks belong in the pipeline, not only in tests. + pairs = {"train/val": (train_groups, val_groups), "train/test": (train_groups, test_groups)} + pairs["val/test"] = (val_groups, test_groups) + for name, (a, b) in pairs.items(): + if not a.isdisjoint(b): + msg = f"{name} group overlap — data leakage" + raise ValueError(msg) + + splits = { + "train": df.filter(pl.col(key).is_in(train_groups)), + "val": df.filter(pl.col(key).is_in(val_groups)), + "test": df.filter(pl.col(key).is_in(test_groups)), + } + for name, part in splits.items(): + logger.info("Split '{}': {} records ({} groups)", name, len(part), part[key].n_unique()) + return splits + + +def stratified_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Per-class stratified random split — only when there is no grouping key.""" + parts: dict[str, list[pl.DataFrame]] = {"train": [], "val": [], "test": []} + for label in sorted(df[config.stratify_column].unique().to_list()): + rows = df.filter(pl.col(config.stratify_column) == label).sample( + fraction=1.0, seed=config.seed, shuffle=True + ) + a = int(len(rows) * config.train_ratio) + b = a + int(len(rows) * config.val_ratio) + parts["train"].append(rows[:a]) + parts["val"].append(rows[a:b]) + parts["test"].append(rows[b:]) + return {name: pl.concat(chunks) for name, chunks in parts.items()} +``` + +Anti-pattern — a plain random split silently leaks every group: + +```python +# WRONG: neighbouring frames of the same clip land in both train and test. +train, val, test = np.split(df.sample(frac=1), [int(0.7 * len(df)), int(0.85 * len(df))]) +``` + +Group-aware splits skew ratios when group sizes are uneven. Always log realized +per-split record counts and class distribution and check them before training; +rebalance by greedily assigning the largest groups first if needed. + +## Storage Format Selection + +``` +Choosing a storage format? +├── Tabular metadata (labels, splits, paths, hashes) +│ └── Parquet ....... columnar, compressed, fast predicate filtering (Polars/DuckDB) +├── Streaming large image/video datasets +│ ├── WebDataset .... .tar shards; distributed/multi-node, S3-friendly, no random access +│ └── TFRecord ...... TensorFlow ecosystem, sequential reads +├── Random-access image datasets (single node, reshuffled every epoch) +│ └── LMDB .......... memory-mapped zero-copy reads, single-writer, bad over NFS +├── In-memory analytics / cross-language interchange +│ └── Arrow IPC ..... zero-copy, language-agnostic +└── Small datasets (< 1 GB) or active annotation churn + └── Raw folders + Parquet manifest ..... simplest, debuggable, diffable +``` + +Rules of thumb: raw files until random reads become the bottleneck; LMDB for +single-node random access; WebDataset once training spans nodes or lives in object +storage; Parquet for everything tabular, always. Write manifests with Hive-style +partitions (`split=train/data.parquet`, `compression="zstd"`, +`row_group_size=10_000`) so readers can skip whole splits. + +```python +"""LMDB packing for fast random-access image reads.""" + +from pathlib import Path + +import lmdb +from loguru import logger + + +def build_lmdb_dataset(image_paths: list[Path], out_path: Path, map_gb: int = 50) -> None: + """Pack encoded image bytes into LMDB keyed by zero-padded index.""" + env = lmdb.open(str(out_path), map_size=map_gb * 1024**3) + with env.begin(write=True) as txn: + for idx, img_path in enumerate(image_paths): + txn.put(f"{idx:08d}".encode(), img_path.read_bytes()) + txn.put(b"__len__", str(len(image_paths)).encode()) + env.close() + logger.info("Built LMDB: {} images at {}", len(image_paths), out_path) +``` + +## Data Quality Validation + +Two complementary layers: Pydantic validates each record at construction time; +Great Expectations validates the dataset in aggregate — distributions, uniqueness, +ranges — and emits a durable report artifact. + +```python +"""Great Expectations suite for an image dataset manifest.""" + +import great_expectations as gx +from loguru import logger + +COLUMNS = ["file_hash", "relative_path", "width", "height", "channels", "label", "group_id"] + + +def create_image_dataset_expectations(context: gx.DataContext) -> None: + """Define the expectation suite for an image classification manifest.""" + suite = context.add_expectation_suite("image_classification_suite") + gxe = gx.expectations + expectations = [ + gxe.ExpectTableColumnsToMatchSet(column_set=COLUMNS), + *(gxe.ExpectColumnValuesToNotBeNull(column=c) for c in ("file_hash", "label")), + *(gxe.ExpectColumnValuesToBeBetween(column=c, min_value=32, max_value=8192) + for c in ("width", "height")), + # Duplicate hashes mean duplicate images — a cross-split leakage vector. + gxe.ExpectColumnValuesToBeUnique(column="file_hash"), + ] + for expectation in expectations: + suite.add_expectation(expectation) + logger.info("Created suite with {} expectations", len(expectations)) +``` + +Mirror the cheapest checks as an in-pipeline gate — null counts per column, +`df["file_hash"].is_duplicated().sum()`, per-class counts from +`df.group_by("label").len()`, and a filter for undersized images — returning a +frozen Pydantic `QualityReport` and raising when it fails. Run the gate after +extraction *and* after splitting: a healthy manifest still yields empty or +single-class splits when ratios or group sizes are wrong. + +## Augmentation Pipeline Design + +Augmentation is part of the data contract, not a training detail: configure it, +scale it with a single `strength` knob, and keep val/test strictly deterministic. + +```python +"""Albumentations pipelines driven by config.""" + +import albumentations as A +from albumentations.pytorch import ToTensorV2 +from pydantic import BaseModel, Field + +MEAN, STD = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225) + + +class AugmentationConfig(BaseModel): + image_size: int = Field(ge=32, default=640) + strength: float = Field(ge=0.0, le=1.0, default=0.5) + + +def build_train_transforms(cfg: AugmentationConfig) -> A.Compose: + """Training augmentations, all probabilities scaled by a single strength knob.""" + s, size = cfg.strength, cfg.image_size + return A.Compose( + [ + A.RandomResizedCrop(height=size, width=size, scale=(0.5, 1.0)), + A.HorizontalFlip(p=0.5), + A.ColorJitter(brightness=0.2 * s, contrast=0.2 * s, saturation=0.2 * s, p=0.8), + A.GaussNoise(p=0.3 * s), + A.CoarseDropout(max_holes=int(8 * s), p=0.3 * s), + A.Normalize(mean=MEAN, std=STD), + ToTensorV2(), + ], + bbox_params=A.BboxParams(format="pascal_voc", label_fields=["class_labels"]), + ) +``` + +Val/test transforms are the same `Compose` with everything random removed — +`A.Resize`, `A.Normalize`, `ToTensorV2`, nothing else. Log the augmentation config +with the run: an unrecorded `strength` change is an unreproducible run. + +## Large-Scale Processing + +Chunked parallel processing with per-item error isolation and progress logging; +streaming reads for datasets larger than RAM. + +```python +"""Parallel chunked processing and streaming reads.""" + +from collections.abc import Callable +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import Any + + +def process_in_chunks( + paths: list[Path], fn: Callable[[Path], Any], workers: int = 8, chunk: int = 1000 +) -> list[Any]: + """Process files in parallel chunks; one failed item never kills the run.""" + results: list[Any] = [] + total = len(paths) + with ProcessPoolExecutor(max_workers=workers) as executor: + for start in range(0, total, chunk): + futures = {executor.submit(fn, p): p for p in paths[start : start + chunk]} + for future in as_completed(futures): + try: + results.append(future.result(timeout=300)) + except Exception: + logger.exception("Failed to process: {}", futures[future]) + done = min(start + chunk, total) + logger.info("Progress: {}/{} ({:.1f}%)", done, total, done / total * 100) + logger.info("Processed {}/{} files successfully", len(results), total) + return results +``` + +For datasets larger than RAM, never call `pl.read_parquet`. Scan lazily and pull +fixed-size slices: `reader = pl.scan_parquet(path)`, then +`reader.slice(offset, batch).collect()` per batch, with the row count from +`reader.select(pl.len()).collect().item()`. + +## Schema Evolution and Migration + +Never mutate a dataset schema in place. Declare each version as its own model, +register it, and provide an explicit migration function per hop. + +```python +"""Versioned dataset schemas with an explicit migration chain.""" + +from pydantic import BaseModel, Field + + +class SchemaV1(BaseModel): + """Original — path and label only.""" + image_path: str + label: str + + +class SchemaV2(SchemaV1): + """V2 — adds dimensions and content hash.""" + width: int = Field(ge=1) + height: int = Field(ge=1) + file_hash: str + + +class SchemaV3(SchemaV2): + """V3 — adds split assignment and quality score.""" + split: str = Field(pattern=r"^(train|val|test)$") + quality_score: float = Field(ge=0.0, le=1.0, default=1.0) + + +def migrate_v2_to_v3(record: SchemaV2, split: str, quality_score: float = 1.0) -> SchemaV3: + """Enrich a V2 record with its split assignment and quality score.""" + return SchemaV3(**record.model_dump(), split=split, quality_score=quality_score) + + +# Ordered registry: reading version N and targeting M applies hops N..M in order. +SCHEMA_REGISTRY: dict[str, type[BaseModel]] = { + "1.0.0": SchemaV1, + "2.0.0": SchemaV2, + "3.0.0": SchemaV3, +} +MIGRATIONS = {("2.0.0", "3.0.0"): migrate_v2_to_v3} +``` + +Subclassing keeps additive versions short, but write a standalone model whenever a +field changes meaning or type — inheritance must never hide a breaking change. +Store `schema_version` alongside the data (Parquet key-value metadata or a sidecar +`dataset.json`) so readers dispatch to the right model instead of guessing. + +## Anti-Patterns + +- **Never split randomly when a grouping key exists.** Frames from one video, images of one patient, or plays from one match must live in exactly one split. +- **Never split without stratification** when no grouping key exists — class imbalance across splits makes metrics unreliable. +- **Never modify raw data in place.** Write transformations to a separate directory; keep originals intact. +- **Never skip validation between pipeline stages.** Catching corrupt data early saves hours of wasted training, and no public or "curated" dataset is clean by default. +- **Never use CSV for large datasets.** Parquet for tabular, LMDB for random-access images, WebDataset for streaming. +- **Never apply training augmentations to val or test.** Only deterministic resize/normalize outside training. +- **Never silently change a schema.** Version it and write an explicit migration. +- **Never hard-code paths or ratios.** Put them in a validated Pydantic config. +- **Never version data in Git.** Keep large artifacts in object storage and track a manifest. + +## Related Skills + +- `pydantic` — validated configs and schema definitions for every stage +- `pytorch-lightning` — `LightningDataModule` consuming validated splits +- `model-evaluation` — scoring models on the splits this skill produces +- `testing` — unit tests for transforms, integration tests for pipeline stages diff --git a/skills/data-pipelines/skill.toml b/skills/data-pipelines/skill.toml new file mode 100644 index 0000000..f2dc1cf --- /dev/null +++ b/skills/data-pipelines/skill.toml @@ -0,0 +1,13 @@ +[skill] +name = "data-pipelines" +version = "1.0.0" +category = "cv-ml" +tags = ["data", "etl", "dataset", "splitting", "storage", "data-quality"] + +[dependencies] +requires = [] +recommends = ["pydantic", "model-evaluation"] + +[compatibility] +python = ">=3.11" +libraries = {} diff --git a/skills/docker-cv/SKILL.md b/skills/docker-cv/SKILL.md index 38ca561..03f5402 100644 --- a/skills/docker-cv/SKILL.md +++ b/skills/docker-cv/SKILL.md @@ -14,6 +14,18 @@ description: > Build optimized Docker images for computer vision and deep learning workloads with CUDA support, multi-stage builds, and security best practices. +## Choosing a Containerization Approach + +``` +Need to package an ML application? +├── Single model serving → Dockerfile with multi-stage build +├── Multiple models/services → Docker Compose for local, K8s for prod +├── GPU required? +│ ├── Training → NVIDIA base images (nvcr.io/nvidia/pytorch) +│ └── Inference → optimized runtime images (NVIDIA Triton, TorchServe) +└── No GPU → Python slim base image +``` + ## Multi-Stage Build Strategy Use separate stages to minimize final image size and maximize layer cache reuse. diff --git a/skills/dvc/README.md b/skills/dvc/README.md deleted file mode 100644 index 1195536..0000000 --- a/skills/dvc/README.md +++ /dev/null @@ -1,56 +0,0 @@ -# Data Version Control (DVC) Skill - -## Purpose - -This skill provides guidance on using DVC to version control datasets, models, and ML pipelines that are too large for Git. DVC extends Git with lightweight metafiles while storing actual data on configurable remote storage (S3, GCS, Azure, local). It is opt-in and should be used when projects involve large binary files. - -## Usage - -Reference this skill when: - -- Tracking large datasets or model files that cannot go in Git. -- Setting up remote storage for team data sharing. -- Defining reproducible ML pipelines with `dvc.yaml`. -- Versioning data and models alongside code using Git tags. -- Running and comparing experiments with different parameters. -- Onboarding new team members who need project data. -- Managing cache and storage for large datasets. - -## Opt-in Nature - -DVC is never a hard requirement. Projects should: - -- Document the DVC setup in the project README. -- Provide alternative data download scripts for users without DVC. -- Keep DVC configuration files (`.dvc/config`) in version control. - -## Setup - -```bash -pip install dvc[s3] # Install with S3 support -dvc init # Initialize in a Git repo -dvc remote add -d myremote s3://bucket/path -``` - -## What This Skill Covers - -- Installation and initialization. -- Tracking files and directories with `dvc add`. -- Remote storage configuration (S3, GCS, Azure, SSH, local). -- Pipeline definitions in `dvc.yaml` with stages, deps, and outputs. -- Data and model versioning with Git tags. -- Experiment tracking and comparison. -- Git integration and collaboration workflows. -- Large dataset management and cache maintenance. -- Python API for programmatic access. - -## Benefits - -- Full version history for datasets and models, tied to Git commits. -- Reproducible pipelines that auto-detect what needs re-running. -- Cloud-agnostic remote storage with no vendor lock-in. -- Lightweight: only small metafiles are committed to Git. -- Built-in experiment tracking without additional services. -- Seamless collaboration through shared remote storage. - -See `SKILL.md` for complete documentation and code examples. diff --git a/skills/dvc/SKILL.md b/skills/dvc/SKILL.md deleted file mode 100644 index ea5a729..0000000 --- a/skills/dvc/SKILL.md +++ /dev/null @@ -1,499 +0,0 @@ ---- -name: dvc -description: > - Use this skill when versioning large datasets, model weights, or pipeline artifacts - with DVC — running dvc init/add/push/pull, configuring S3/GCS/Azure remotes, defining - reproducible dvc.yaml pipelines, and keeping big binaries out of Git. Reach for it any - time data or checkpoints are too large to commit to Git and need tracking or sharing, - even if the user doesn't say "DVC". For experiment metrics, run comparison, and model - registry see mlflow or wandb. ---- - -# Data Version Control (DVC) for ML Projects - -## Overview - -DVC (Data Version Control) is an open-source version control system for machine learning projects. It extends Git to handle large files, datasets, and ML models that cannot be stored in Git repositories. DVC tracks these files using lightweight metafiles (`.dvc` files) that are committed to Git, while the actual data is stored in configurable remote storage (S3, GCS, Azure, or local). DVC is opt-in and should be used when projects involve datasets or model files too large for Git. - -## Why Use DVC - -Machine learning projects face a unique challenge: the code is small and fits in Git, but the data and models are large and do not. Without DVC, teams resort to ad-hoc solutions like shared network drives, manual file naming conventions (`model_v3_final_FINAL.pt`), or storing everything in cloud buckets without any version history. DVC solves these problems: - -- **Version control for data and models** with the same Git workflow developers already know. -- **Reproducible pipelines** that connect data, code, and outputs. -- **Remote storage** on any cloud provider or local filesystem. -- **Lightweight tracking** using small `.dvc` metafiles committed to Git. -- **Experiment tracking** for comparing parameter and metric changes. -- **Collaboration** through shared remote storage. -- **No vendor lock-in** with support for S3, GCS, Azure, SSH, and local storage. - -## Installation and Initialization - -### Installation - -```bash -# Using pip -pip install dvc - -# With specific remote storage support -pip install dvc[s3] # Amazon S3 -pip install dvc[gs] # Google Cloud Storage -pip install dvc[azure] # Azure Blob Storage -pip install dvc[ssh] # SSH/SFTP - -# Using pixi -pixi add dvc --feature data -pixi add dvc-s3 --feature data # For S3 support -``` - -### Initialization - -```bash -# Initialize DVC in an existing Git repository -cd my-project -dvc init - -# This creates: -# .dvc/ - DVC internal directory -# .dvc/config - DVC configuration -# .dvcignore - Patterns to ignore (like .gitignore) - -# Commit the DVC initialization -git add .dvc .dvcignore -git commit -m "Initialize DVC" -``` - -## Tracking Data Files - -### Adding Files to DVC - -```bash -# Track a dataset directory -dvc add data/coco/ - -# Track a model file -dvc add models/yolov8_best.pt - -# Track a large file -dvc add data/video_dataset.tar.gz -``` - -Each `dvc add` command creates a `.dvc` metafile: - -```yaml -# data/coco.dvc (auto-generated) -outs: - - md5: d41d8cd98f00b204e9800998ecf8427e.dir - size: 25432198 - nfiles: 118287 - hash: md5 - path: coco -``` - -### Git Integration - -```bash -# The .dvc file is small and goes into Git -git add data/coco.dvc data/.gitignore -git commit -m "Track COCO dataset with DVC" - -# The actual data is in .gitignore (auto-added by DVC) -# data/coco is now ignored by Git but tracked by DVC -``` - -### Checking Status - -```bash -# See which tracked files have changed -dvc status - -# See detailed diff -dvc diff -``` - -## Remote Storage - -Remote storage is where DVC stores the actual data. Configure it once and all team members can push and pull data. - -### Configuring Remote Storage - -```bash -# Amazon S3 -dvc remote add -d myremote s3://my-bucket/dvc-store -dvc remote modify myremote region us-east-1 - -# Google Cloud Storage -dvc remote add -d myremote gs://my-bucket/dvc-store - -# Azure Blob Storage -dvc remote add -d myremote azure://my-container/dvc-store -dvc remote modify myremote account_name myaccount - -# Local or network filesystem -dvc remote add -d myremote /mnt/shared/dvc-store - -# SSH -dvc remote add -d myremote ssh://user@server:/path/to/dvc-store -``` - -The `-d` flag sets the remote as default. Configuration is stored in `.dvc/config`: - -```ini -[core] - remote = myremote -[remote "myremote"] - url = s3://my-bucket/dvc-store - region = us-east-1 -``` - -### Pushing and Pulling Data - -```bash -# Push all tracked data to remote -dvc push - -# Pull all tracked data from remote -dvc pull - -# Push/pull specific files -dvc push data/coco.dvc -dvc pull models/yolov8_best.pt.dvc - -# Fetch data without checking out (download to cache only) -dvc fetch -``` - -## Pipeline Definitions - -DVC pipelines define reproducible workflows that connect data, code, parameters, and outputs. - -### Creating a Pipeline - -Define stages in `dvc.yaml`: - -```yaml -# dvc.yaml -stages: - prepare: - cmd: python src/prepare_data.py - deps: - - src/prepare_data.py - - data/raw/ - params: - - prepare.split_ratio - - prepare.seed - outs: - - data/prepared/ - - train: - cmd: python src/train.py - deps: - - src/train.py - - src/model.py - - data/prepared/ - params: - - train.epochs - - train.learning_rate - - train.batch_size - - train.model_name - outs: - - models/best_model.pt - metrics: - - metrics/train_metrics.json: - cache: false - plots: - - metrics/training_curves.csv: - x: epoch - y: loss - - evaluate: - cmd: python src/evaluate.py - deps: - - src/evaluate.py - - models/best_model.pt - - data/prepared/test/ - metrics: - - metrics/eval_metrics.json: - cache: false - plots: - - metrics/confusion_matrix.csv: - x: predicted - y: actual - template: confusion -``` - -### Parameters File - -DVC reads parameters from `params.yaml` by default: - -```yaml -# params.yaml -prepare: - split_ratio: 0.8 - seed: 42 - -train: - epochs: 100 - learning_rate: 0.001 - batch_size: 32 - model_name: yolov8 -``` - -### Running Pipelines - -```bash -# Run the entire pipeline -dvc repro - -# Run a specific stage -dvc repro train - -# Force re-run even if nothing changed -dvc repro --force - -# Run with modified parameters -# Edit params.yaml first, then: -dvc repro - -# Visualize the pipeline DAG -dvc dag -``` - -Output of `dvc dag`: - -``` -+---------+ -| prepare | -+---------+ - * - * - * - +-------+ - | train | - +-------+ - * - * - * - +----------+ - | evaluate | - +----------+ -``` - -## Data and Model Versioning - -DVC enables versioning by leveraging Git branches and tags. - -### Versioning Workflow - -```bash -# Version 1: Baseline model -dvc repro -git add dvc.lock params.yaml metrics/ -git commit -m "v1: Baseline YOLOv8 model" -git tag v1.0 - -# Version 2: Updated augmentation -# Edit code and params -dvc repro -git add dvc.lock params.yaml metrics/ -git commit -m "v2: Add mosaic augmentation" -git tag v2.0 - -# Switch between versions -git checkout v1.0 -dvc checkout # Restores data/model files to v1 state - -git checkout v2.0 -dvc checkout # Restores data/model files to v2 state -``` - -### Comparing Versions - -```bash -# Compare metrics between current and a tag -dvc metrics diff v1.0 - -# Compare parameters -dvc params diff v1.0 - -# Show metrics for current version -dvc metrics show -``` - -Output example: - -``` -Path Metric Old New Change -metrics/eval_metrics mAP 0.42 0.48 0.06 -metrics/eval_metrics mAP50 0.58 0.65 0.07 -metrics/eval_metrics mAP75 0.35 0.41 0.06 -``` - -## Experiment Tracking with DVC - -DVC has built-in experiment tracking that does not require additional tools. - -### Running Experiments - -```bash -# Run an experiment with modified parameters -dvc exp run --set-param train.learning_rate=0.0005 - -# Run multiple experiments in parallel -dvc exp run --set-param train.learning_rate=0.01 --queue -dvc exp run --set-param train.learning_rate=0.001 --queue -dvc exp run --set-param train.learning_rate=0.0001 --queue -dvc exp run --run-all --parallel 3 - -# List experiments -dvc exp show - -# Compare experiments -dvc exp diff exp-abc123 exp-def456 -``` - -### Promoting an Experiment - -```bash -# Apply the best experiment to the workspace -dvc exp apply exp-abc123 - -# Create a Git branch from an experiment -dvc exp branch exp-abc123 best-lr-experiment -``` - -## Integration with Git - -DVC is designed to work alongside Git. Here is the typical workflow: - -```bash -# 1. Make changes to data or code -# 2. Run the pipeline -dvc repro - -# 3. Add DVC-tracked changes -dvc push - -# 4. Commit metafiles to Git -git add dvc.lock params.yaml metrics/ *.dvc -git commit -m "Update model with new dataset version" -git push - -# When a teammate clones or pulls: -git pull -dvc pull # Downloads the data matching this Git commit -``` - -### .gitignore Management - -DVC automatically manages `.gitignore` entries: - -```gitignore -# Auto-added by DVC -/data/coco -/models/best_model.pt -``` - -### Recommended Git Workflow - -```bash -# Always commit .dvc files, dvc.yaml, dvc.lock, params.yaml -# Never commit the actual data files -# Always push DVC data before pushing Git commits - -dvc push && git push -``` - -## Collaboration Workflows - -### Onboarding a New Team Member - -```bash -# Clone the repository -git clone https://github.com/team/cv-project.git -cd cv-project - -# Pull all DVC-tracked data -dvc pull - -# Now the workspace has all data, models, and metrics -``` - -### Working on a Feature Branch - -```bash -# Create a feature branch -git checkout -b feature/new-augmentation - -# Make changes, run pipeline -dvc repro - -# Push data and code -dvc push -git add -A -git commit -m "Add new augmentation strategy" -git push origin feature/new-augmentation -``` - -## Large Dataset Management - -### Handling Datasets That Do Not Fit on Disk - -```bash -# Use external storage without copying to workspace -dvc import-url s3://large-dataset/coco-2017.tar.gz data/ -dvc import https://github.com/team/data-repo data/annotations - -# Fetch only specific files -dvc pull data/train.dvc # Only download training data -``` - -### Cache Management - -```bash -# Check cache size -du -sh .dvc/cache - -# Garbage collect unused cache entries -dvc gc --workspace - -# Remove all cached data -dvc gc --all-commits --cloud -``` - -## Python API - -DVC also provides a Python API for programmatic access: - -```python -import dvc.api - -# Get the URL of a tracked file -url = dvc.api.get_url("data/coco/", repo="https://github.com/team/cv-project") - -# Read parameters -params = dvc.api.params_show() -learning_rate = params["train"]["learning_rate"] - -# Open a tracked file -with dvc.api.open("data/annotations.json", repo=".") as f: - import json - annotations = json.load(f) -``` - -## Best Practices - -1. **Track data early**: Run `dvc add` as soon as data enters the project. -2. **Use pipelines** (`dvc.yaml`) for reproducible training workflows. -3. **Push data before Git**: Always `dvc push` before `git push`. -4. **Use `params.yaml`** for all configurable values in pipelines. -5. **Tag releases** in Git to create named data/model versions. -6. **Use `.dvcignore`** to exclude temporary or intermediate files. -7. **Set up remote storage** before the first team member joins. -8. **Use `dvc gc`** periodically to clean the local cache. -9. **Combine with Git hooks** to automate `dvc push` on commit. -10. **Document the DVC workflow** so all team members follow the same process. - -## Summary - -DVC brings the discipline of version control to the data and model files that define ML projects. By tracking large files with lightweight metafiles, storing data on any cloud provider, and defining reproducible pipelines, DVC ensures that experiments are reproducible, datasets are versioned, and collaboration is frictionless. When combined with Git, it provides a complete version control solution for the entire ML lifecycle. diff --git a/skills/dvc/skill.toml b/skills/dvc/skill.toml deleted file mode 100644 index 28e3a29..0000000 --- a/skills/dvc/skill.toml +++ /dev/null @@ -1,15 +0,0 @@ -[skill] -name = "dvc" -version = "1.0.0" -category = "infra" -tags = ["data-versioning", "dvc", "model-versioning", "pipelines"] - -[dependencies] -requires = ["loguru"] -recommends = ["gcp"] - -[compatibility] -python = ">=3.11" - -[compatibility.libraries] -dvc = ">=3.0" diff --git a/skills/github-actions/SKILL.md b/skills/github-actions/SKILL.md index f4d8b0c..f6820e2 100644 --- a/skills/github-actions/SKILL.md +++ b/skills/github-actions/SKILL.md @@ -28,6 +28,16 @@ Every job shares the same setup steps. Later examples abbreviate this as `# ...p ## Workflow Tiers +Decide what runs at each trigger before writing YAML: + +``` +What should CI do? +├── Every commit → lint + type check + unit tests (< 5 min) +├── Every PR → above + integration tests + Docker build (< 15 min) +├── Merge to main → above + push image + deploy staging +└── Release tag → deploy production + create GitHub Release +``` + Organize workflows by speed and trigger frequency. ### Tier 1: Lint and Format (every push) diff --git a/skills/huggingface/SKILL.md b/skills/huggingface/SKILL.md index 802e047..401be80 100644 --- a/skills/huggingface/SKILL.md +++ b/skills/huggingface/SKILL.md @@ -469,4 +469,4 @@ def load_quantized_model( ## Integration with Other Skills -Wrap HF models in a Lightning Trainer for custom loops; set `report_to=["wandb"]` for tracking; export via `optimum`/ONNX for inference; deploy on SageMaker (HF DLC) or behind FastAPI; version Hub datasets with DVC. +Wrap HF models in a Lightning Trainer for custom loops; set `report_to=["wandb"]` for tracking; export via `optimum`/ONNX for inference; deploy on SageMaker (HF DLC) or behind FastAPI. diff --git a/skills/kubernetes/SKILL.md b/skills/kubernetes/SKILL.md index 3a2e608..3727996 100644 --- a/skills/kubernetes/SKILL.md +++ b/skills/kubernetes/SKILL.md @@ -14,6 +14,19 @@ description: > Deployment patterns for ML inference and training services: GPU scheduling, Helm charts, autoscaling, and environment-specific overlays. +## Is Kubernetes the Right Target? + +Reach for K8s only when the load and operational maturity justify it. + +``` +Where should this run? +├── Internal/team use → single instance + Docker Compose +├── Production API (< 100 RPS) → Cloud Run / App Runner / ECS Fargate +├── Production API (> 100 RPS) → Kubernetes with autoscaling ← this skill +├── Batch inference → Vertex AI Batch / SageMaker Batch Transform +└── Training at scale → Vertex AI / SageMaker training jobs (see gcp) +``` + ## Deployment Manifests for ML Services Define Deployments with explicit resource requests and GPU scheduling for inference workloads. diff --git a/skills/master-skill/README.md b/skills/master-skill/README.md index 3cdf749..4c5f8ce 100644 --- a/skills/master-skill/README.md +++ b/skills/master-skill/README.md @@ -15,7 +15,7 @@ The Master Skill reads user inputs including project name, author information, a - **Interactive project initialization** -- collects project name, author, description, and license through a guided flow. - **Archetype selection** -- offers predefined project templates (training, inference, research, library, pipeline, model-zoo) that pre-select relevant skills. -- **Optional skill composition** -- allows toggling additional skills (Docker, CI/CD, DVC, MLflow) beyond the archetype defaults. +- **Optional skill composition** -- allows toggling additional skills (Docker, CI/CD, MLflow) beyond the archetype defaults. - **Dependency resolution** -- ensures that skill prerequisites are satisfied before scaffolding begins. - **Idempotent generation** -- can be re-run safely to add skills to an existing project without overwriting customized files. - **Dry-run mode** -- previews the full file tree that would be generated before writing anything to disk. diff --git a/skills/master-skill/SKILL.md b/skills/master-skill/SKILL.md index 26f8711..d21c0b0 100644 --- a/skills/master-skill/SKILL.md +++ b/skills/master-skill/SKILL.md @@ -37,7 +37,6 @@ You are initializing a new AI/CV project using the whet framework. - Weights & Biases (wandb) - MLflow - TensorBoard - - DVC (data versioning) - ONNX export - Model evaluation (model-evaluation) diff --git a/skills/mlflow/SKILL.md b/skills/mlflow/SKILL.md index cc3db51..7b5828c 100644 --- a/skills/mlflow/SKILL.md +++ b/skills/mlflow/SKILL.md @@ -6,8 +6,7 @@ description: > versioning and promoting models through stages, and running a self-hosted tracking server. Reach for it any time you'd otherwise hand-roll run tracking or ask "which config produced this result?" and the stack is MLflow, even if the user doesn't say it. - For the W&B or TensorBoard alternatives see wandb and tensorboard; for versioning the - data and model files themselves see dvc. + For the W&B or TensorBoard alternatives see wandb and tensorboard. --- # MLflow Tracking Integration for ML Projects diff --git a/skills/model-evaluation/SKILL.md b/skills/model-evaluation/SKILL.md index 4388d5a..d3048a1 100644 --- a/skills/model-evaluation/SKILL.md +++ b/skills/model-evaluation/SKILL.md @@ -29,8 +29,8 @@ tuning decisions — never touched. - **Three splits.** Train (fit), validation (tune/early-stop/threshold-select), test (report once). Never select thresholds or checkpoints on the test set. -- **Freeze the test set.** Version it (see the `dvc` skill) so numbers are comparable - across runs. A metric that moves because the test set changed is not a metric. +- **Freeze the test set.** Keep it immutable and content-addressed so numbers are + comparable across runs. A metric that moves because the test set changed is not a metric. - **Prevent leakage.** Split by the unit that must generalize — by scene/video/match, not by frame — so near-duplicate frames don't span train and test. - **Report with uncertainty.** Bootstrap the test set for a confidence interval, diff --git a/skills/model-evaluation/skill.toml b/skills/model-evaluation/skill.toml index adbd4fb..0b909ce 100644 --- a/skills/model-evaluation/skill.toml +++ b/skills/model-evaluation/skill.toml @@ -6,7 +6,7 @@ tags = ["evaluation", "metrics", "map", "detection", "supervision", "eval"] [dependencies] requires = [] -recommends = ["testing", "matplotlib", "wandb", "dvc"] +recommends = ["testing", "matplotlib", "wandb"] [compatibility] python = ">=3.11" diff --git a/skills/onnx/SKILL.md b/skills/onnx/SKILL.md index d3bbc6e..d09c243 100644 --- a/skills/onnx/SKILL.md +++ b/skills/onnx/SKILL.md @@ -500,4 +500,4 @@ To compute the ONNX speedup, run the same warmup/timed loop against the PyTorch 7. **Consider quantization** for CPU deployment (2-4x speedup). 8. **Profile with ONNX Runtime** profiling tools to find bottlenecks. 9. **Wrap ONNX inference** in a Pydantic-validated class for type safety. -10. **Store ONNX models** as DVC-tracked artifacts, not in Git. +10. **Store ONNX models** in object storage or an artifact registry, not in Git. diff --git a/skills/pre-commit/SKILL.md b/skills/pre-commit/SKILL.md index 3913e62..2920550 100644 --- a/skills/pre-commit/SKILL.md +++ b/skills/pre-commit/SKILL.md @@ -195,7 +195,7 @@ You can define custom hooks for project-specific checks. Here are two useful exa hooks: - id: no-model-files name: Check for model files - entry: bash -c 'for f in "$@"; do case "$f" in *.pt|*.pth|*.onnx|*.pkl|*.h5) echo "ERROR: Model file $f should not be committed. Use DVC instead." && exit 1;; esac; done' + entry: bash -c 'for f in "$@"; do case "$f" in *.pt|*.pth|*.onnx|*.pkl|*.h5) echo "ERROR: Model file $f should not be committed. Use object storage or an artifact registry instead." && exit 1;; esac; done' language: system types: [file] ``` diff --git a/skills/testing/SKILL.md b/skills/testing/SKILL.md index a832461..56c65f5 100644 --- a/skills/testing/SKILL.md +++ b/skills/testing/SKILL.md @@ -13,6 +13,23 @@ description: > Pytest patterns for ML and computer vision projects: test structure, fixtures, parametrized tests, CV-specific strategies, mocking, performance testing, and CI. ML code fails silently — a model can train cleanly yet predict garbage from wrong preprocessing, label mapping, transposed dims, or broken augmentation. Tests catch these before they waste GPU hours. +## Coverage Requirements + +- **Overall:** minimum 80% line coverage +- **New code:** at least 90% +- **Critical paths:** 100% — model forward pass, data loading, config validation +- **No skipped tests:** fix or delete them; a permanently skipped test is a lie + +## Test Naming Convention + +Pattern: `test___` — the name should state the contract. + +```python +def test_detector_empty_image_raises_error() -> None: ... +def test_config_negative_lr_raises_validation_error() -> None: ... +def test_model_forward_batch_returns_correct_shape() -> None: ... +``` + ## Test Structure Three tiers: unit (fast, isolated), integration (component interactions), and e2e (full pipeline). diff --git a/src/whet/cli/config.py b/src/whet/cli/config.py index 487c926..d069439 100644 --- a/src/whet/cli/config.py +++ b/src/whet/cli/config.py @@ -27,7 +27,6 @@ def config( console.print("[bold]whet configuration[/bold]\n") console.print(f" target: {cfg.target.value}") console.print(f" skills_dir: {cfg.skills_dir}") - console.print(f" agents_dir: {cfg.agents_dir}") console.print(f" archetypes_dir: {cfg.archetypes_dir}") return diff --git a/src/whet/cli/doctor.py b/src/whet/cli/doctor.py index 42bea73..4a292cb 100644 --- a/src/whet/cli/doctor.py +++ b/src/whet/cli/doctor.py @@ -40,21 +40,7 @@ def doctor() -> None: issues += 1 skills = [] - # Check 2: Agents directory - if cfg.agents_dir.is_dir(): - agents = [ - d.name for d in cfg.agents_dir.iterdir() if d.is_dir() and (d / "SKILL.md").exists() - ] - n_agents = len(agents) - console.print( - f" {_check_mark(True)} Agents directory: {cfg.agents_dir} ({n_agents} agents)" - ) - else: - console.print(f" {_warn_mark()} Agents directory not found: {cfg.agents_dir}") - warnings += 1 - agents = [] - - # Check 3: Archetypes directory + # Check 2: Archetypes directory if cfg.archetypes_dir.is_dir(): archetypes = [ d.name @@ -70,7 +56,7 @@ def doctor() -> None: console.print(f" {_warn_mark()} Archetypes directory not found: {cfg.archetypes_dir}") warnings += 1 - # Check 4: Platform detection + # Check 3: Platform detection detected = detect_platform() if detected: console.print(f" {_check_mark(True)} Platform detected: {detected.value}") @@ -78,7 +64,7 @@ def doctor() -> None: console.print(f" {_warn_mark()} No platform auto-detected in current directory") warnings += 1 - # Check 5: Installed skills + # Check 4: Installed skills if detected: from whet.cli.skills import _get_adapter @@ -94,7 +80,7 @@ def doctor() -> None: console.print(f" {_warn_mark()} No skills installed for {detected.value}") warnings += 1 - # Check 6: SKILL.md frontmatter + # Check 5: SKILL.md frontmatter if skills: missing_fm = [s for s in skills if not s.description] if missing_fm: @@ -106,7 +92,7 @@ def doctor() -> None: else: console.print(f" {_check_mark(True)} All skills have YAML frontmatter") - # Check 7: skill.toml files + # Check 6: skill.toml files if skills: missing_toml = [s for s in skills if not s.has_toml] if missing_toml: @@ -118,19 +104,7 @@ def doctor() -> None: else: console.print(f" {_check_mark(True)} All skills have skill.toml metadata") - # Check 8: agent.toml files - if agents: - missing_agent_toml = [a for a in agents if not (cfg.agents_dir / a / "agent.toml").exists()] - if missing_agent_toml: - n_at = len(missing_agent_toml) - console.print(f" {_warn_mark()} {n_at} agents missing agent.toml") - for a in missing_agent_toml[:5]: - console.print(f" - {a}") - warnings += 1 - else: - console.print(f" {_check_mark(True)} All agents have agent.toml metadata") - - # Check 9: Settings template + # Check 7: Settings template from whet.settings.engine import get_settings_target, get_template_path plat = detected.value if detected else cfg.target.value @@ -151,7 +125,7 @@ def doctor() -> None: console.print(f" {_warn_mark()} No settings template for {plat}") warnings += 1 - # Check 10: Dependency consistency + # Check 8: Dependency consistency if skills: available = {s.name for s in skills} broken_deps = [] diff --git a/src/whet/cli/install.py b/src/whet/cli/install.py index 5ba643b..5f31027 100644 --- a/src/whet/cli/install.py +++ b/src/whet/cli/install.py @@ -1,4 +1,4 @@ -"""Install command — bulk install skills, agents, and settings to a platform.""" +"""Install command — bulk install skills and settings to a platform.""" from __future__ import annotations @@ -11,7 +11,7 @@ from whet.cli import app from whet.core.config import Platform, WhetConfig from whet.core.skill import Skill -from whet.registry.loader import discover_agents, discover_skills +from whet.registry.loader import discover_skills console = Console() @@ -21,13 +21,11 @@ def install( scope_global: bool = typer.Option(False, "--global", "-g", help="Install to global directory."), scope_local: bool = typer.Option(False, "--local", "-l", help="Install to local project."), category: str | None = typer.Option(None, "--category", "--cat", help="Only install category."), - skills_only: bool = typer.Option(False, "--skills-only", help="Only install skills."), - agents_only: bool = typer.Option(False, "--agents-only", help="Only install agents."), with_settings: bool = typer.Option( False, "--with-settings", "-s", help="Also apply settings template." ), ) -> None: - """Install skills, agents, and optionally settings.""" + """Install skills and optionally settings.""" if not scope_global and not scope_local: scope_local = True @@ -35,37 +33,21 @@ def install( adapter = _get_adapter(cfg.target) paths = cfg.get_platform_paths() - include_skills = not agents_only - include_agents = not skills_only + all_skills = discover_skills(cfg.skills_dir) + if category: + all_skills = [s for s in all_skills if s.category == category] - all_skills: list[Skill] = [] - all_agents: list[Skill] = [] - - if include_skills: - all_skills = discover_skills(cfg.skills_dir) - if category: - all_skills = [s for s in all_skills if s.category == category] - - if include_agents: - all_agents = discover_agents(cfg.agents_dir) - - if not all_skills and not all_agents: - console.print("[yellow]No skills or agents found to install.[/yellow]") + if not all_skills: + console.print("[yellow]No skills found to install.[/yellow]") raise typer.Exit(code=1) if scope_global: - if all_skills: - _install_to(adapter, all_skills, paths.global_dir, "skills", "global") - if all_agents: - _install_to(adapter, all_agents, paths.global_dir, "agents", "global") + _install_to(adapter, all_skills, paths.global_dir, "global") if with_settings: _apply_settings(cfg.target.value, "global") if scope_local: - if all_skills: - _install_to(adapter, all_skills, paths.local_dir, "skills", "local") - if all_agents: - _install_to(adapter, all_agents, paths.local_dir, "agents", "local") + _install_to(adapter, all_skills, paths.local_dir, "local") if with_settings: _apply_settings(cfg.target.value, "local") @@ -74,19 +56,16 @@ def _install_to( adapter: PlatformAdapter, items: list[Skill], target_dir: Path, - item_type: str, scope_label: str, ) -> None: - """Install skills or agents to a target directory.""" - console.print(f"\n[bold]Installing {len(items)} {item_type} ({scope_label})...[/bold]") + """Install skills to a target directory.""" + console.print(f"\n[bold]Installing {len(items)} skills ({scope_label})...[/bold]") for item in items: adapter.install_skill(item, target_dir) console.print(f" [green]✓[/green] {item.name}") - console.print( - f"\n[bold green]✓ Installed {len(items)} {item_type} to {target_dir}[/bold green]" - ) + console.print(f"\n[bold green]✓ Installed {len(items)} skills to {target_dir}[/bold green]") def _apply_settings(platform: str, scope: str) -> None: diff --git a/src/whet/core/config.py b/src/whet/core/config.py index e9a5fcc..5cb2bac 100644 --- a/src/whet/core/config.py +++ b/src/whet/core/config.py @@ -52,7 +52,6 @@ class WhetConfig(BaseModel): target: Platform = Platform.CLAUDE skills_dir: Path = Field(default_factory=lambda: Path(__file__).resolve().parents[3] / "skills") - agents_dir: Path = Field(default_factory=lambda: Path(__file__).resolve().parents[3] / "agents") archetypes_dir: Path = Field( default_factory=lambda: Path(__file__).resolve().parents[3] / "archetypes" ) diff --git a/src/whet/registry/loader.py b/src/whet/registry/loader.py index 0d04832..935ce03 100644 --- a/src/whet/registry/loader.py +++ b/src/whet/registry/loader.py @@ -1,4 +1,4 @@ -"""Scan and parse skills and agents from disk.""" +"""Scan and parse skills from disk.""" from __future__ import annotations @@ -15,14 +15,6 @@ def discover_skills(skills_dir: Path) -> list[Skill]: return _discover(skills_dir) -def discover_agents(agents_dir: Path) -> list[Skill]: - """Discover all agents in a directory. - - Agents use the same SKILL.md format as skills. - """ - return _discover(agents_dir) - - def _discover(base_dir: Path) -> list[Skill]: """Discover all SKILL.md-based items in a directory.""" if not base_dir.is_dir(): diff --git a/tests/integration/test_project_generation.py b/tests/integration/test_project_generation.py index e5ed9f9..2f79274 100644 --- a/tests/integration/test_project_generation.py +++ b/tests/integration/test_project_generation.py @@ -27,23 +27,6 @@ def test_skills_reference_valid_skills() -> None: ) -def test_agents_directory_structure() -> None: - """Test agents have consistent structure.""" - agents_dir = Path("agents") - - for agent_dir in agents_dir.iterdir(): - if not agent_dir.is_dir() or agent_dir.name.startswith("."): - continue - - # All agents must have SKILL.md and README.md - assert (agent_dir / "SKILL.md").exists(), f"{agent_dir.name} missing SKILL.md" - assert (agent_dir / "README.md").exists(), f"{agent_dir.name} missing README.md" - - # Check SKILL.md is substantial - skill_content = (agent_dir / "SKILL.md").read_text() - assert len(skill_content) > 500, f"{agent_dir.name} SKILL.md is too short" - - def test_archetypes_have_consistent_docs() -> None: """Test all archetypes document their structure.""" archetypes_dir = Path("archetypes") diff --git a/tests/test_agents.py b/tests/test_agents.py deleted file mode 100644 index da683b2..0000000 --- a/tests/test_agents.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Test that all agents are complete.""" - -from __future__ import annotations - -import sys -from pathlib import Path - -import pytest - -AGENTS_DIR = Path("agents") - -if sys.version_info >= (3, 12): - import tomllib -else: - import tomli as tomllib - - -def get_all_agents() -> list[Path]: - """Get all agent directories.""" - return sorted([d for d in AGENTS_DIR.iterdir() if d.is_dir() and not d.name.startswith(".")]) - - -@pytest.mark.parametrize("agent_dir", get_all_agents(), ids=lambda d: d.name) -def test_agent_has_skill_md(agent_dir: Path) -> None: - """Test agent has SKILL.md.""" - skill_md = agent_dir / "SKILL.md" - assert skill_md.exists(), f"Missing SKILL.md in {agent_dir.name}" - assert skill_md.stat().st_size > 500, f"SKILL.md too short in {agent_dir.name}" - - -@pytest.mark.parametrize("agent_dir", get_all_agents(), ids=lambda d: d.name) -def test_agent_has_readme(agent_dir: Path) -> None: - """Test agent has README.""" - readme = agent_dir / "README.md" - assert readme.exists(), f"Missing README.md in {agent_dir.name}" - - -@pytest.mark.parametrize("agent_dir", get_all_agents(), ids=lambda d: d.name) -def test_agent_has_toml(agent_dir: Path) -> None: - """Test agent has agent.toml metadata file.""" - toml_path = agent_dir / "agent.toml" - assert toml_path.exists(), f"Missing agent.toml in {agent_dir.name}" - - -def test_blocking_agents_have_actions() -> None: - """Test that blocking agents have GitHub Actions.""" - for agent_dir in get_all_agents(): - toml_path = agent_dir / "agent.toml" - if toml_path.exists(): - with open(toml_path, "rb") as f: - data = tomllib.load(f) - if data.get("agent", {}).get("type") == "blocking": - action_yml = agent_dir / "action.yml" - assert action_yml.exists(), ( - f"Missing action.yml for blocking agent {agent_dir.name}" - ) - - -def test_minimum_agent_count() -> None: - """Test that we have at least the expected number of agents.""" - agents = get_all_agents() - assert len(agents) >= 4, f"Expected at least 4 agents, found {len(agents)}" From b72548bd42b76ec95002384d926584f1d4330d80 Mon Sep 17 00:00:00 2001 From: "Enrique G. Ortiz" Date: Thu, 23 Jul 2026 22:15:04 -0400 Subject: [PATCH 08/21] P2: progressive disclosure (thin index + references/), install tiers (#8) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit **Stacked on [#7](https://github.com/ortizeg/whet/pull/7)** — base is `p1-agents-to-skills`, so review #7 first. ## 1. Progressive disclosure Split the 21 skills over 450 lines into a thin `SKILL.md` index plus topic-organized `references/` loaded on demand. A SKILL.md loads fully into context when triggered and **stays there for the session**, so every line is a recurring token cost. Anthropic's guidance is that SKILL.md should read like a table of contents. **Always-resident lines: 14,930 → 7,122** across 32 skills, with ~10k lines relocated into **115 reference files**. Content was *relocated, not trimmed*. | Skill | Index before → after | |---|---| | onnx | 503 → 120 | | opencv | 502 → 131 | | kubernetes | 496 → 125 | | pytorch-lightning | 485 → 125 | | testing | 479 → 103 | | wandb | 477 → 121 | | gcp | 477 → 116 | | aws-sagemaker | 455 → 132 | | …13 more | all → 95–143 | Each index keeps the 80%-case patterns, conventions, and anti-patterns inline, then ends with a `## Deep dives` list giving every reference a *"read this when…"* trigger. Rules enforced: **one level deep** (no reference links to another reference), TOC on any reference over 100 lines, descriptive filenames, frontmatter byte-for-byte unchanged. ## 2. Installer support (required — otherwise the links 404) Without this, the deep-dive links would break exactly the way the external `interface-design` skill's currently do: - `Skill.references_dir` / `reference_files()` / `read_flattened()` - **Claude + Antigravity** adapters copy `references/` on install - **Cursor + Copilot** store one flat file, so references are **inlined** rather than lost ## 3. Install tiers New `tier` field in `skill.toml`. `tier = "extra"` marks a skill as opt-in, skipped unless `whet install --include-extras`. Marked extra: **aws-sagemaker, kubernetes, gradio, huggingface, mlflow** — real skills, but no evidence they're in the flagship workflow, so they no longer dilute the default trigger surface. ## 4. Documentation CLAUDE.md now documents the description formula (trigger-first, pushy, with disambiguation), the progressive-disclosure layout and its rules, and the tier system. `docs/skills/index.md` gains an Install Tiers section and marks extras. Also documents the **`interface-design` companion**: whet ships no general product-UI ruleset (`gradio` is ML demos only), so that external skill is the recommended pairing — with a note that its `references/` must be present or its deep-dive links won't resolve. ## Verification - `uv run pytest tests/` → **192 passed** - `uv run ruff check .` / `ruff format --check .` → clean - `uv run mypy src/whet/ tests/ --strict` → clean - `uv run mkdocs build --strict` → clean 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.8 --- CLAUDE.md | 57 +- docs/skills/index.md | 27 +- skills/abstraction-patterns/SKILL.md | 456 +++------------ .../references/interface-design.md | 130 +++++ .../references/testing-abstractions.md | 39 ++ .../references/when-not-to-abstract.md | 51 ++ .../references/wrapper-patterns.md | 258 +++++++++ skills/aws-sagemaker/SKILL.md | 375 +------------ .../references/batch-transform.md | 56 ++ .../references/endpoints-and-inference.md | 131 +++++ .../references/hyperparameter-tuning.md | 58 ++ .../pipelines-and-model-registry.md | 130 +++++ .../references/s3-data-and-local-mode.md | 97 ++++ .../aws-sagemaker/references/training-jobs.md | 106 ++++ skills/aws-sagemaker/skill.toml | 1 + skills/data-pipelines/SKILL.md | 446 ++------------- .../data-pipelines/references/augmentation.md | 48 ++ .../data-pipelines/references/data-quality.md | 46 ++ .../references/dataset-splitting.md | 119 ++++ .../references/large-scale-processing.md | 44 ++ .../references/pipeline-architecture.md | 136 +++++ .../references/schema-evolution.md | 56 ++ .../references/storage-formats.md | 52 ++ skills/fastapi/SKILL.md | 392 ++----------- .../application-factory-and-lifespan.md | 87 +++ skills/fastapi/references/background-tasks.md | 37 ++ .../references/dependency-injection.md | 65 +++ .../fastapi/references/health-and-errors.md | 83 +++ .../fastapi/references/middleware-and-cors.md | 60 ++ .../references/request-response-models.md | 75 +++ .../references/testing-and-deployment.md | 111 ++++ .../fastapi/references/websocket-streaming.md | 49 ++ skills/gcp/SKILL.md | 445 ++------------- skills/gcp/references/artifact-registry.md | 46 ++ skills/gcp/references/authentication.md | 47 ++ skills/gcp/references/cloud-storage.md | 72 +++ .../gcp/references/docker-image-management.md | 112 ++++ .../gcp/references/pydantic-configuration.md | 71 +++ skills/gcp/references/vertex-training.md | 113 ++++ skills/gradio/SKILL.md | 442 ++------------- .../references/deployment-and-spaces.md | 77 +++ .../references/flagging-and-feedback.md | 62 +++ .../gradio/references/inputs-and-outputs.md | 47 ++ .../gradio/references/interface-and-blocks.md | 126 +++++ skills/gradio/references/model-serving.md | 128 +++++ .../gradio/references/reusable-components.md | 45 ++ skills/gradio/skill.toml | 1 + skills/huggingface/SKILL.md | 496 +++-------------- skills/huggingface/references/datasets.md | 77 +++ .../references/fine-tuning-with-trainer.md | 100 ++++ .../huggingface/references/hub-publishing.md | 58 ++ .../references/inference-optimization.md | 76 +++ skills/huggingface/references/peft-lora.md | 71 +++ .../references/transformers-models.md | 71 +++ skills/huggingface/skill.toml | 1 + skills/hydra-config/SKILL.md | 417 ++------------ .../config-groups-and-composition.md | 194 +++++++ .../instantiate-and-interpolation.md | 74 +++ .../lightning-and-tracking-integration.md | 59 ++ .../references/overrides-and-multirun.md | 37 ++ .../structured-configs-and-pydantic.md | 107 ++++ skills/kubernetes/SKILL.md | 449 ++------------- skills/kubernetes/references/autoscaling.md | 49 ++ .../references/config-and-secrets.md | 63 +++ .../kubernetes/references/gpu-scheduling.md | 43 ++ .../references/helm-and-kustomize.md | 129 +++++ .../references/manifests-and-services.md | 150 +++++ .../references/storage-and-volumes.md | 47 ++ skills/kubernetes/references/training-jobs.md | 54 ++ skills/kubernetes/skill.toml | 1 + skills/matplotlib/SKILL.md | 382 +------------ .../references/confusion-matrices.md | 74 +++ .../references/feature-maps-and-embeddings.md | 138 +++++ .../references/image-grids-and-overlays.md | 123 +++++ .../references/publication-figures.md | 98 ++++ .../saving-and-tracker-integration.md | 80 +++ skills/mlflow/SKILL.md | 430 ++------------- skills/mlflow/references/artifacts.md | 70 +++ .../references/lightning-and-autolog.md | 52 ++ skills/mlflow/references/mlflow-vs-wandb.md | 27 + skills/mlflow/references/model-registry.md | 72 +++ skills/mlflow/references/self-hosted-setup.md | 87 +++ skills/mlflow/references/serving.md | 24 + .../mlflow/references/tracking-and-metrics.md | 150 +++++ skills/mlflow/skill.toml | 1 + skills/onnx/SKILL.md | 485 ++-------------- .../execution-providers-and-sessions.md | 94 ++++ .../references/export-and-dynamic-axes.md | 141 +++++ .../optimization-and-quantization.md | 118 ++++ skills/onnx/references/serving.md | 70 +++ .../references/validation-and-benchmarking.md | 89 +++ skills/opencv/SKILL.md | 521 +++--------------- skills/opencv/references/camera-capture.md | 55 ++ .../references/drawing-and-annotation.md | 142 +++++ .../image-class-and-color-spaces.md | 140 +++++ .../opencv/references/tensor-conversions.md | 27 + skills/opencv/references/video-io.md | 179 ++++++ skills/pixi/SKILL.md | 472 ++-------------- skills/pixi/references/cli-and-lockfiles.md | 56 ++ skills/pixi/references/dependencies.md | 72 +++ .../references/environments-and-features.md | 80 +++ skills/pixi/references/manifest-reference.md | 151 +++++ skills/pixi/references/project-templates.md | 119 ++++ skills/pixi/references/tasks.md | 56 ++ skills/pydantic/SKILL.md | 343 +----------- .../references/mutable-data-models.md | 113 ++++ .../references/nested-config-composition.md | 137 +++++ skills/pydantic/references/testing-configs.md | 89 +++ .../references/validators-and-transforms.md | 111 ++++ skills/pytorch-lightning/SKILL.md | 462 ++-------------- .../references/checkpointing-and-logging.md | 58 ++ .../references/datamodule.md | 124 +++++ .../references/distributed-training.md | 40 ++ .../references/lightning-module.md | 123 +++++ .../references/testing-lightning-code.md | 41 ++ .../references/trainer-and-callbacks.md | 143 +++++ skills/tensorboard/SKILL.md | 453 ++------------- .../tensorboard/references/image-logging.md | 75 +++ .../references/model-graph-and-histograms.md | 80 +++ skills/tensorboard/references/profiling.md | 28 + .../references/scalars-and-metrics.md | 105 ++++ .../references/setup-and-integration.md | 174 ++++++ skills/tensorrt/SKILL.md | 412 ++------------ skills/tensorrt/references/benchmarking.md | 89 +++ .../tensorrt/references/docker-deployment.md | 54 ++ skills/tensorrt/references/dynamic-shapes.md | 55 ++ skills/tensorrt/references/engine-building.md | 136 +++++ .../tensorrt/references/inference-runtime.md | 133 +++++ .../references/precision-and-calibration.md | 112 ++++ skills/testing/SKILL.md | 482 ++-------------- skills/testing/references/ci-integration.md | 57 ++ .../testing/references/cv-specific-testing.md | 156 ++++++ .../references/fixtures-and-conftest.md | 111 ++++ skills/testing/references/mocking.md | 44 ++ .../testing/references/parametrized-tests.md | 55 ++ .../testing/references/performance-tests.md | 46 ++ skills/wandb/SKILL.md | 468 ++-------------- skills/wandb/references/artifacts.md | 72 +++ .../references/logging-metrics-and-media.md | 171 ++++++ skills/wandb/references/model-registry.md | 17 + skills/wandb/references/opt-in-integration.md | 149 +++++ skills/wandb/references/sweeps.md | 52 ++ src/whet/adapters/antigravity.py | 4 + src/whet/adapters/claude.py | 4 + src/whet/adapters/copilot.py | 3 +- src/whet/cli/install.py | 13 + src/whet/core/skill.py | 30 + 147 files changed, 11131 insertions(+), 7903 deletions(-) create mode 100644 skills/abstraction-patterns/references/interface-design.md create mode 100644 skills/abstraction-patterns/references/testing-abstractions.md create mode 100644 skills/abstraction-patterns/references/when-not-to-abstract.md create mode 100644 skills/abstraction-patterns/references/wrapper-patterns.md create mode 100644 skills/aws-sagemaker/references/batch-transform.md create mode 100644 skills/aws-sagemaker/references/endpoints-and-inference.md create mode 100644 skills/aws-sagemaker/references/hyperparameter-tuning.md create mode 100644 skills/aws-sagemaker/references/pipelines-and-model-registry.md create mode 100644 skills/aws-sagemaker/references/s3-data-and-local-mode.md create mode 100644 skills/aws-sagemaker/references/training-jobs.md create mode 100644 skills/data-pipelines/references/augmentation.md create mode 100644 skills/data-pipelines/references/data-quality.md create mode 100644 skills/data-pipelines/references/dataset-splitting.md create mode 100644 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skills/wandb/references/model-registry.md create mode 100644 skills/wandb/references/opt-in-integration.md create mode 100644 skills/wandb/references/sweeps.md diff --git a/CLAUDE.md b/CLAUDE.md index 24a866a..9a6c675 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -61,14 +61,64 @@ uv run mypy src/whet/ tests/ --strict ## How to Add a New Skill -1. Create `skills//SKILL.md` — Must start with YAML frontmatter (`name`, `description`), be >500 chars, include code examples and headers -2. Create `skills//skill.toml` — Machine-readable metadata (category, tags, deps, compatibility) +1. Create `skills//SKILL.md` — YAML frontmatter (`name`, `description`), >500 chars, code examples and headers +2. Create `skills//skill.toml` — Machine-readable metadata (category, tier, tags, deps, compatibility) 3. Create `skills//README.md` — Must explain purpose (include words like "when", "use", "purpose") 4. Create `docs/skills/.md` — Documentation page (Purpose, When to Use, Key Patterns, Anti-Patterns) 5. Add nav entry to `mkdocs.yml` under Skills section Tests discover skills dynamically — no hardcoded lists to update. +### Writing the `description` (this is the trigger) + +Only the frontmatter is preloaded; the `description` is the entire discovery mechanism. +Write it third-person and trigger-first, and be a little "pushy": + +``` +description: > + Use this skill when . Reach for it any time you would otherwise + , even if the user doesn't say "" explicitly. Not for + (see ). +``` + +Always disambiguate against adjacent skills so two skills never compete for the same trigger. + +### Progressive disclosure (keep SKILL.md thin) + +A SKILL.md loads fully into context when triggered and **stays there for the session**, so +every line is a recurring token cost. Structure a skill as an index plus on-demand detail: + +``` +skills// +├── SKILL.md # ~120-200 line index: core patterns, conventions, anti-patterns +└── references/ # topic files, loaded only when needed + ├── .md + └── .md +``` + +Rules: +- Keep `SKILL.md` **under 500 lines** (target 120–200 for a split skill). +- Keep the 80%-case patterns, conventions, and anti-patterns **inline**; move depth to `references/`. +- End `SKILL.md` with a `## Deep dives` list giving each reference a *"read this when…"* trigger. +- **One level deep only** — a reference file must never link to another reference file. +- Reference files over 100 lines start with a table of contents; name them descriptively. + +The installer copies `references/` for directory-based platforms (Claude, Antigravity) and +inlines them for flat-file platforms (Cursor, Copilot), so no content is lost either way. + +### Core vs extra tier + +`tier = "core"` (default) installs with `whet install`. `tier = "extra"` marks a skill as +opt-in — it is skipped unless the user passes `--include-extras`. Use `extra` for skills +that are real but outside the flagship path, so they don't dilute the default trigger surface. + +## Companion skills (not shipped by whet) + +whet does not ship a general product-UI ruleset — `gradio` covers ML demos only. For +application/dashboard UI work, use the external **`interface-design`** skill alongside whet. +If its `references/` directory is missing from your install, its "Deep dives" links will not +resolve — reinstall it with the reference files present. + ## How to Add a New Archetype 1. Create `archetypes//README.md` — Must be >500 chars, include code blocks @@ -98,7 +148,8 @@ description: > [skill] name = "skill-name" version = "1.0.0" -category = "cv-ml" # core | cv-ml | infra | experiment-tracking +category = "cv-ml" # core | cv-ml | infra | cloud | experiment-tracking +tier = "core" # core (default, installed) | extra (opt-in via --include-extras) tags = ["tag1", "tag2"] [dependencies] diff --git a/docs/skills/index.md b/docs/skills/index.md index 239a231..fac4a37 100644 --- a/docs/skills/index.md +++ b/docs/skills/index.md @@ -13,6 +13,23 @@ Each skill lives in its own directory under `skills/` and contains: When you reference a skill in a Claude Code session, Claude reads the `SKILL.md` and uses its contents to guide code generation. The skill does not execute code -- it provides expert context that shapes Claude's output. +## Install Tiers + +Skills are either **core** (installed by `whet install`) or **extra** (opt-in). Extras are +marked _(extra)_ below — they are real skills, but outside the flagship CV/ML path, so they +stay out of the default trigger surface. Install them with: + +```bash +whet install --include-extras +``` + +## Companion Skills + +whet does not ship a general product-UI ruleset — `gradio` covers ML demos only. For +application or dashboard UI work, pair whet with the external **`interface-design`** skill. +Note that if its `references/` directory is missing from your install, its "Deep dives" +links will not resolve. + ## Skill Categories ### Core Framework @@ -36,7 +53,7 @@ Skills for building and training deep learning models. | [PyTorch Lightning](pytorch-lightning.md) | Training loops, modules, and callbacks | Lightning 2.x | | [Hydra Config](hydra-config.md) | Hierarchical configuration management | Hydra, OmegaConf | | [Weights & Biases](wandb.md) | Experiment tracking and visualization | wandb | -| [MLflow](mlflow.md) | ML lifecycle and model registry | MLflow | +| [MLflow](mlflow.md) _(extra)_ | ML lifecycle and model registry | MLflow | | [TensorBoard](tensorboard.md) | Training visualization and profiling | TensorBoard | ### Computer Vision @@ -51,16 +68,16 @@ Skills specific to computer vision workflows. | [TensorRT](tensorrt.md) | GPU-optimized inference engine building | TensorRT, trtexec | | [Model Evaluation](model-evaluation.md) | Metrics, mAP/IoU, eval sets, failure analysis | supervision, torchmetrics | | [PydanticAI](pydantic-ai.md) | Type-safe LLM/VLM structured outputs, auto-labeling | pydantic-ai | -| [Hugging Face](huggingface.md) | Pretrained models, fine-tuning, PEFT/LoRA | transformers, datasets, peft | +| [Hugging Face](huggingface.md) _(extra)_ | Pretrained models, fine-tuning, PEFT/LoRA | transformers, datasets, peft | ### Cloud & Deployment | Skill | Description | Key Libraries | |-------|-------------|---------------| -| [AWS SageMaker](aws-sagemaker.md) | ML training and deployment on AWS | sagemaker, boto3 | +| [AWS SageMaker](aws-sagemaker.md) _(extra)_ | ML training and deployment on AWS | sagemaker, boto3 | | [FastAPI](fastapi.md) | ML model serving APIs | FastAPI, uvicorn | -| [Kubernetes](kubernetes.md) | ML service deployment and orchestration on K8s | kubectl, helm | -| [Gradio](gradio.md) | Interactive ML model demos and prototypes | Gradio | +| [Kubernetes](kubernetes.md) _(extra)_ | ML service deployment and orchestration on K8s | kubectl, helm | +| [Gradio](gradio.md) _(extra)_ | Interactive ML model demos and prototypes | Gradio | ### Infrastructure & DevOps diff --git a/skills/abstraction-patterns/SKILL.md b/skills/abstraction-patterns/SKILL.md index 1e3cf7f..eabf31f 100644 --- a/skills/abstraction-patterns/SKILL.md +++ b/skills/abstraction-patterns/SKILL.md @@ -13,7 +13,10 @@ description: > # Abstraction Patterns Skill -Well-abstracted Python for AI/CV projects. Abstraction exists to reduce cognitive load, not to add layers — every abstraction must justify itself by making at least three call sites simpler. +Well-abstracted Python for AI/CV projects. Abstraction exists to reduce cognitive +load, not to add layers — every abstraction must justify itself by making at least +three call sites simpler. This page carries the decision rule and the two shapes +that cover most cases; the deep dives hold the full worked patterns. ## The Rule: When to Abstract @@ -28,69 +31,40 @@ Do NOT abstract when: - The abstraction hides important details (GPU memory management, batch dimension handling) - A simple function would suffice (do not create a class for a single method) -## Pattern 1: VideoReader Abstraction +## Shape 1: A function, until proven otherwise -Video reading involves resource management (opening/closing file handles), frame iteration, and metadata access — a perfect candidate for abstraction. Raw `cv2.VideoCapture` usage scattered across a codebase is easy to get wrong (forgetting `cap.release()`, missing `isOpened()` checks). Wrap it in a context manager: +The default abstraction is a module-level function with a precise signature and +explicit errors. Reach for a class only when there is state to hold or resources to +manage. ```python -"""Video reader with proper resource management.""" - -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Iterator - -import cv2 -import numpy as np - - -@dataclass(frozen=True) -class VideoMetadata: - """Immutable metadata for a video file.""" +def load_image(path: str | Path, color_space: ColorSpace = "rgb") -> np.ndarray: + """Load an image with validation. Raises FileNotFoundError / ValueError / RuntimeError.""" + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"Image not found: {path}") + image = cv2.imread(str(path), cv2.IMREAD_COLOR) # BGR + if image is None: + raise RuntimeError(f"Failed to decode image: {path}") + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if color_space == "rgb" else image +``` - path: Path - fps: float - total_frames: int - width: int - height: int +If you find yourself writing `class XProcessor` whose only job is to store one +parameter and expose one method, use `functools.partial` instead. - @property - def duration_seconds(self) -> float: - return self.total_frames / self.fps if self.fps > 0 else 0.0 +## Shape 2: A context manager for anything that must be released +File handles, capture devices, model sessions, and connections all leak when the +release path is left to the caller. Own it in `__enter__`/`__exit__`: +```python class VideoReader: - """Context-managed OpenCV video reader. - - Always use as a context manager so resources are released: - with VideoReader("input.mp4") as reader: - for frame in reader: - process(frame) - """ - - def __init__(self, path: str | Path) -> None: - self._path = Path(path) - self._cap: cv2.VideoCapture | None = None - self._metadata: VideoMetadata | None = None - - @property - def metadata(self) -> VideoMetadata: - if self._metadata is None: - raise RuntimeError("VideoReader must be used as a context manager") - return self._metadata + """Context-managed OpenCV video reader.""" def __enter__(self) -> VideoReader: self._cap = cv2.VideoCapture(str(self._path)) if not self._cap.isOpened(): raise RuntimeError(f"Failed to open video: {self._path}") - self._metadata = VideoMetadata( - path=self._path, - fps=self._cap.get(cv2.CAP_PROP_FPS), - total_frames=int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT)), - width=int(self._cap.get(cv2.CAP_PROP_FRAME_WIDTH)), - height=int(self._cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), - ) return self def __exit__(self, *args: object) -> None: @@ -99,371 +73,67 @@ class VideoReader: self._cap = None def __iter__(self) -> Iterator[np.ndarray]: - if self._cap is None: - raise RuntimeError("VideoReader must be used as a context manager") while True: ret, frame = self._cap.read() if not ret: break yield frame - def read_frame(self, frame_idx: int) -> np.ndarray: - """Read a specific frame by zero-based index (BGR).""" - if self._cap is None: - raise RuntimeError("VideoReader must be used as a context manager") - if frame_idx < 0 or frame_idx >= self.metadata.total_frames: - raise IndexError(f"Frame index {frame_idx} out of range [0, {self.metadata.total_frames})") - self._cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) - ret, frame = self._cap.read() - if not ret: - raise RuntimeError(f"Failed to read frame {frame_idx}") - return frame -``` - -Usage is now clean and leak-free: -```python with VideoReader("input.mp4") as reader: - print(f"{reader.metadata.fps} FPS, {reader.metadata.total_frames} frames") for frame in reader: visualize(frame, model.predict(frame)) - middle = reader.read_frame(reader.metadata.total_frames // 2) ``` -## Pattern 2: Image Loading Abstraction - -Image loading seems simple but involves format detection, color space conversion, validation, and error handling. Abstracting it prevents inconsistencies. - -```python -"""Robust image loading with validation.""" - -from __future__ import annotations +Methods that require the managed resource should raise a clear `RuntimeError` when +used outside the `with` block rather than failing on a `None` attribute. -from pathlib import Path -from typing import Literal +## Shape 3: A frozen dataclass for the values that travel together -import cv2 -import numpy as np - -ColorSpace = Literal["rgb", "bgr", "gray"] - -SUPPORTED_EXTENSIONS: frozenset[str] = frozenset({ - ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".webp", -}) - - -def load_image( - path: str | Path, - color_space: ColorSpace = "rgb", - max_size: int | None = None, -) -> np.ndarray: - """Load an image with validation and optional longest-edge resize. - - Returns (H, W, 3) for color or (H, W) for grayscale. Raises - FileNotFoundError, ValueError (unsupported extension), or RuntimeError - (decode failure). - """ - path = Path(path) - if not path.exists(): - raise FileNotFoundError(f"Image not found: {path}") - if path.suffix.lower() not in SUPPORTED_EXTENSIONS: - raise ValueError(f"Unsupported image format: {path.suffix}") - - image = cv2.imread(str(path), cv2.IMREAD_COLOR) # OpenCV loads BGR - if image is None: - raise RuntimeError(f"Failed to decode image: {path}") - - if color_space == "rgb": - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - elif color_space == "gray": - image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - # "bgr" needs no conversion - - if max_size is not None: - h, w = image.shape[:2] - scale = max_size / max(h, w) - if scale < 1.0: - image = cv2.resize(image, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA) - - return image - - -def save_image( - image: np.ndarray, - path: str | Path, - color_space: ColorSpace = "rgb", - quality: int = 95, -) -> None: - """Save an image, creating parent dirs and picking encode params by suffix.""" - path = Path(path) - path.parent.mkdir(parents=True, exist_ok=True) - if image.ndim not in (2, 3): - raise ValueError(f"Expected 2D or 3D array, got {image.ndim}D") - - if color_space == "rgb" and image.ndim == 3: - image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) - - params: list[int] = [] - if path.suffix.lower() in (".jpg", ".jpeg"): - params = [cv2.IMWRITE_JPEG_QUALITY, quality] - elif path.suffix.lower() == ".png": - params = [cv2.IMWRITE_PNG_COMPRESSION, 3] - - cv2.imwrite(str(path), image, params) -``` - -## Pattern 3: Metric Computation Abstraction - -Metrics in CV projects require accumulation over batches and reset semantics. Abstract this into a consistent `update`/`compute`/`reset` interface. +When several call sites pass around the same tuple of values, name it. A frozen +dataclass documents the shape, prevents accidental mutation, and gives derived +values a home — without introducing behaviour the caller has to learn. ```python -"""Metric computation with accumulation and reset.""" - -from __future__ import annotations - -from abc import ABC, abstractmethod - -import numpy as np - - -class Metric(ABC): - """Base class for metrics accumulated over batches; reset() between epochs.""" - - @abstractmethod - def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - ... - - @abstractmethod - def compute(self) -> dict[str, float]: - ... - - @abstractmethod - def reset(self) -> None: - """Reset accumulated state for a new epoch.""" - ... - - -class AccuracyMetric(Metric): - """Top-1 accuracy with accumulation.""" - - def __init__(self) -> None: - self._correct: int = 0 - self._total: int = 0 - - def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - pred_classes = np.argmax(predictions, axis=1) - self._correct += int(np.sum(pred_classes == targets)) - self._total += len(targets) - - def compute(self) -> dict[str, float]: - if self._total == 0: - return {"accuracy": 0.0} - return {"accuracy": self._correct / self._total} - - def reset(self) -> None: - self._correct = 0 - self._total = 0 - - -class IoUMetric(Metric): - """Per-class + mean IoU for segmentation; predictions/targets are (N, H, W) class indices.""" - - def __init__(self, num_classes: int, ignore_index: int = -1) -> None: - self._num_classes = num_classes - self._ignore_index = ignore_index - self._intersection = np.zeros(num_classes, dtype=np.int64) - self._union = np.zeros(num_classes, dtype=np.int64) - - def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - mask = targets != self._ignore_index - pred_masked, target_masked = predictions[mask], targets[mask] - for cls in range(self._num_classes): - pred_cls = pred_masked == cls - target_cls = target_masked == cls - self._intersection[cls] += int(np.sum(pred_cls & target_cls)) - self._union[cls] += int(np.sum(pred_cls | target_cls)) - - def compute(self) -> dict[str, float]: - iou_per_class = np.zeros(self._num_classes) - for cls in range(self._num_classes): - if self._union[cls] > 0: - iou_per_class[cls] = self._intersection[cls] / self._union[cls] - result: dict[str, float] = {"mean_iou": float(np.mean(iou_per_class))} - for cls in range(self._num_classes): - result[f"iou_class_{cls}"] = float(iou_per_class[cls]) - return result - - def reset(self) -> None: - self._intersection[:] = 0 - self._union[:] = 0 - - -class MetricCollection: - """Runs several metrics together, prefixing each metric's keys with its name.""" - - def __init__(self, metrics: dict[str, Metric]) -> None: - self._metrics = metrics - - def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: - for metric in self._metrics.values(): - metric.update(predictions, targets) - - def compute(self) -> dict[str, float]: - results: dict[str, float] = {} - for name, metric in self._metrics.items(): - for key, value in metric.compute().items(): - results[f"{name}/{key}"] = value - return results - - def reset(self) -> None: - for metric in self._metrics.values(): - metric.reset() -``` - -## Pattern 4: Model Inference Wrapper - -Wrap model inference to handle preprocessing, batching, postprocessing, and device management in one place. - -```python -"""Model inference wrapper with preprocessing and postprocessing.""" - -from __future__ import annotations - -from pathlib import Path - -import numpy as np -import torch -import torch.nn as nn - - -class InferenceWrapper: - """Runs inference with device management, preprocessing, batching, and postprocessing in one interface.""" - - def __init__( - self, - model: nn.Module, - device: str = "cuda", - input_size: tuple[int, int] = (224, 224), - mean: tuple[float, ...] = (0.485, 0.456, 0.406), - std: tuple[float, ...] = (0.229, 0.224, 0.225), - ) -> None: - self._model = model.to(device).eval() - self._device = torch.device(device) - self._input_size = input_size - self._mean = np.array(mean, dtype=np.float32) - self._std = np.array(std, dtype=np.float32) - - def preprocess(self, image: np.ndarray) -> torch.Tensor: - """Resize, normalize, CHW, add batch dim, move to device.""" - import cv2 - - resized = cv2.resize(image, (self._input_size[1], self._input_size[0])) - normalized = (resized.astype(np.float32) / 255.0 - self._mean) / self._std - tensor = torch.from_numpy(normalized).permute(2, 0, 1).unsqueeze(0) - return tensor.to(self._device) - - @torch.no_grad() - def predict(self, image: np.ndarray) -> np.ndarray: - return self._model(self.preprocess(image)).cpu().numpy() - - @torch.no_grad() - def predict_batch(self, images: list[np.ndarray]) -> np.ndarray: - batch = torch.cat([self.preprocess(img) for img in images], dim=0) - return self._model(batch).cpu().numpy() - - @classmethod - def from_checkpoint( - cls, - checkpoint_path: str | Path, - model_class: type[nn.Module], - **kwargs: object, - ) -> InferenceWrapper: - """Load model from a checkpoint and wrap it.""" - checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True) - model = model_class(**checkpoint.get("hparams", {})) - model.load_state_dict(checkpoint["state_dict"]) - return cls(model=model, **kwargs) -``` - -## Testing Abstractions - -```python -"""Tests for abstraction patterns.""" - -from __future__ import annotations - -import numpy as np -import pytest - -from my_project.io import VideoReader, load_image -from my_project.metrics import AccuracyMetric, IoUMetric, MetricCollection - - -def test_accuracy_metric_accumulates_across_batches() -> None: - metric = AccuracyMetric() - metric.update(np.array([[0.9, 0.1], [0.2, 0.8], [0.7, 0.3], [0.4, 0.6]]), np.array([0, 1, 0, 0])) # 3/4 - metric.update(np.array([[0.8, 0.2], [0.3, 0.7]]), np.array([0, 1])) # 2/2 - assert metric.compute()["accuracy"] == pytest.approx(5 / 6) - - -def test_accuracy_metric_reset() -> None: - metric = AccuracyMetric() - metric.update(np.array([[0.9, 0.1]]), np.array([0])) - metric.reset() - assert metric.compute()["accuracy"] == 0.0 - - -def test_load_image_not_found() -> None: - with pytest.raises(FileNotFoundError, match="Image not found"): - load_image("/nonexistent/image.jpg") -``` - -## When NOT to Abstract - -### Do Not Abstract Single-Use Logic - -```python -# BAD: Unnecessary abstraction for one-off logic -class ImagePreprocessor: - def __init__(self, size): - self.size = size +@dataclass(frozen=True) +class VideoMetadata: + """Immutable metadata for a video file.""" - def process(self, image): - return cv2.resize(image, self.size) + path: Path + fps: float + total_frames: int + width: int + height: int -# GOOD: Just use the function directly -resized = cv2.resize(image, (224, 224)) + @property + def duration_seconds(self) -> float: + return self.total_frames / self.fps if self.fps > 0 else 0.0 ``` -### Do Not Hide Critical Details - -```python -# BAD: Hides GPU memory management -class AutoBatcher: - def auto_batch(self, items): - # Magically figures out batch size based on GPU memory - # Developer has no idea what's happening - ... +This is the cheapest abstraction available: no inheritance, no lifecycle, no hidden +control flow. Prefer it before reaching for a class with methods. -# GOOD: Be explicit about batch size -for batch in DataLoader(dataset, batch_size=32): - ... -``` +## Conventions -### Do Not Create Classes for Single Functions +- **Count call sites before abstracting** — three real uses, not three imagined ones. +- **Type every boundary** — the whole point of a wrapper is that call sites stop guessing shapes and dtypes. +- **Raise specific exceptions** with the offending value in the message; never return `None` for failure. +- **Keep configuration explicit** — frozen dataclasses or Pydantic models over loose kwargs dicts. +- **One interface per concept** — if several implementations must be interchangeable, define the ABC once (`update`/`compute`/`reset`) and let the call site stay ignorant of which one it holds. +- **Make the abstraction testable without hardware** — if a wrapper can only be tested with a real camera or GPU, the boundary is wrong. +- **Prefer composition over inheritance** — a collection that drives several implementations beats a deep class hierarchy. -```python -# BAD: A class with one method is just a function -class NMSProcessor: - def __init__(self, threshold): - self.threshold = threshold +## Anti-patterns - def process(self, boxes, scores): - return nms(boxes, scores, self.threshold) +- **Abstracting single-use logic** — one call site means inline it; a class wrapping a single `cv2.resize` is noise. +- **Hiding critical details** — an "auto batcher" that silently picks batch size from GPU memory removes the developer's ability to reason about OOM. +- **A class for a single function** — `NMSProcessor(threshold).process(...)` is just `partial(nms, iou_threshold=...)`. +- **Premature base classes** — an ABC with exactly one subclass is indirection with no payoff. +- **Leaky resource wrappers** — exposing the raw `cv2.VideoCapture` alongside the wrapper invites callers to bypass cleanup. -# GOOD: Use functools.partial or just pass the argument -from functools import partial +## Deep dives -apply_nms = partial(nms, iou_threshold=0.5) -filtered = apply_nms(boxes, scores) -``` +- `references/wrapper-patterns.md` — read when writing a full wrapper around a third-party API: complete `VideoReader`, validated image load/save, and an inference wrapper with preprocessing and batching. +- `references/interface-design.md` — read when several implementations must be interchangeable and you need an ABC plus a collection that drives them (metrics example). +- `references/when-not-to-abstract.md` — read when judging whether existing code is over-engineered, or before adding a class you are not sure earns its place. +- `references/testing-abstractions.md` — read when writing tests that pin an abstraction's contract: accumulation, reset semantics, and error paths. diff --git a/skills/abstraction-patterns/references/interface-design.md b/skills/abstraction-patterns/references/interface-design.md new file mode 100644 index 0000000..1992ff2 --- /dev/null +++ b/skills/abstraction-patterns/references/interface-design.md @@ -0,0 +1,130 @@ +# Interface Design with Abstract Base Classes + +Scope: designing a small, consistent interface (`update`/`compute`/`reset`) that many implementations share, using metric computation as the worked example. + +## Contents + +- [When an ABC earns its place](#when-an-abc-earns-its-place) +- [The Metric interface](#the-metric-interface) +- [Composing implementations](#composing-implementations) + +## When an ABC earns its place + +An abstract base class is justified when several concrete implementations must be +interchangeable at the call site — the training loop should not care whether it is +accumulating accuracy or IoU. If there is only one implementation, skip the ABC and +write the class directly. + +Metrics in CV projects require accumulation over batches and reset semantics. Abstract this into a consistent `update`/`compute`/`reset` interface. + +## The Metric interface + +```python +"""Metric computation with accumulation and reset.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod + +import numpy as np + + +class Metric(ABC): + """Base class for metrics accumulated over batches; reset() between epochs.""" + + @abstractmethod + def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: + ... + + @abstractmethod + def compute(self) -> dict[str, float]: + ... + + @abstractmethod + def reset(self) -> None: + """Reset accumulated state for a new epoch.""" + ... + + +class AccuracyMetric(Metric): + """Top-1 accuracy with accumulation.""" + + def __init__(self) -> None: + self._correct: int = 0 + self._total: int = 0 + + def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: + pred_classes = np.argmax(predictions, axis=1) + self._correct += int(np.sum(pred_classes == targets)) + self._total += len(targets) + + def compute(self) -> dict[str, float]: + if self._total == 0: + return {"accuracy": 0.0} + return {"accuracy": self._correct / self._total} + + def reset(self) -> None: + self._correct = 0 + self._total = 0 + + +class IoUMetric(Metric): + """Per-class + mean IoU for segmentation; predictions/targets are (N, H, W) class indices.""" + + def __init__(self, num_classes: int, ignore_index: int = -1) -> None: + self._num_classes = num_classes + self._ignore_index = ignore_index + self._intersection = np.zeros(num_classes, dtype=np.int64) + self._union = np.zeros(num_classes, dtype=np.int64) + + def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: + mask = targets != self._ignore_index + pred_masked, target_masked = predictions[mask], targets[mask] + for cls in range(self._num_classes): + pred_cls = pred_masked == cls + target_cls = target_masked == cls + self._intersection[cls] += int(np.sum(pred_cls & target_cls)) + self._union[cls] += int(np.sum(pred_cls | target_cls)) + + def compute(self) -> dict[str, float]: + iou_per_class = np.zeros(self._num_classes) + for cls in range(self._num_classes): + if self._union[cls] > 0: + iou_per_class[cls] = self._intersection[cls] / self._union[cls] + result: dict[str, float] = {"mean_iou": float(np.mean(iou_per_class))} + for cls in range(self._num_classes): + result[f"iou_class_{cls}"] = float(iou_per_class[cls]) + return result + + def reset(self) -> None: + self._intersection[:] = 0 + self._union[:] = 0 +``` + +## Composing implementations + +Because every implementation shares one interface, a collection can drive them all +without knowing what they are: + +```python +class MetricCollection: + """Runs several metrics together, prefixing each metric's keys with its name.""" + + def __init__(self, metrics: dict[str, Metric]) -> None: + self._metrics = metrics + + def update(self, predictions: np.ndarray, targets: np.ndarray) -> None: + for metric in self._metrics.values(): + metric.update(predictions, targets) + + def compute(self) -> dict[str, float]: + results: dict[str, float] = {} + for name, metric in self._metrics.items(): + for key, value in metric.compute().items(): + results[f"{name}/{key}"] = value + return results + + def reset(self) -> None: + for metric in self._metrics.values(): + metric.reset() +``` diff --git a/skills/abstraction-patterns/references/testing-abstractions.md b/skills/abstraction-patterns/references/testing-abstractions.md new file mode 100644 index 0000000..843482a --- /dev/null +++ b/skills/abstraction-patterns/references/testing-abstractions.md @@ -0,0 +1,39 @@ +# Testing Abstractions + +Scope: pytest examples that verify an abstraction's contract — accumulation, reset semantics, and error paths. + +A good abstraction is easy to test in isolation: state accumulates as documented, +`reset()` really clears it, and invalid input raises the documented exception. If a +wrapper is hard to test without real hardware or files, the abstraction boundary is +in the wrong place. + +```python +"""Tests for abstraction patterns.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from my_project.io import VideoReader, load_image +from my_project.metrics import AccuracyMetric, IoUMetric, MetricCollection + + +def test_accuracy_metric_accumulates_across_batches() -> None: + metric = AccuracyMetric() + metric.update(np.array([[0.9, 0.1], [0.2, 0.8], [0.7, 0.3], [0.4, 0.6]]), np.array([0, 1, 0, 0])) # 3/4 + metric.update(np.array([[0.8, 0.2], [0.3, 0.7]]), np.array([0, 1])) # 2/2 + assert metric.compute()["accuracy"] == pytest.approx(5 / 6) + + +def test_accuracy_metric_reset() -> None: + metric = AccuracyMetric() + metric.update(np.array([[0.9, 0.1]]), np.array([0])) + metric.reset() + assert metric.compute()["accuracy"] == 0.0 + + +def test_load_image_not_found() -> None: + with pytest.raises(FileNotFoundError, match="Image not found"): + load_image("/nonexistent/image.jpg") +``` diff --git a/skills/abstraction-patterns/references/when-not-to-abstract.md b/skills/abstraction-patterns/references/when-not-to-abstract.md new file mode 100644 index 0000000..ef4871f --- /dev/null +++ b/skills/abstraction-patterns/references/when-not-to-abstract.md @@ -0,0 +1,51 @@ +# When NOT to Abstract + +Scope: the three failure modes that produce over-engineered code, each with a bad/good pair. + +## Do not abstract single-use logic + +```python +# BAD: Unnecessary abstraction for one-off logic +class ImagePreprocessor: + def __init__(self, size): + self.size = size + + def process(self, image): + return cv2.resize(image, self.size) + +# GOOD: Just use the function directly +resized = cv2.resize(image, (224, 224)) +``` + +## Do not hide critical details + +```python +# BAD: Hides GPU memory management +class AutoBatcher: + def auto_batch(self, items): + # Magically figures out batch size based on GPU memory + # Developer has no idea what's happening + ... + +# GOOD: Be explicit about batch size +for batch in DataLoader(dataset, batch_size=32): + ... +``` + +## Do not create classes for single functions + +```python +# BAD: A class with one method is just a function +class NMSProcessor: + def __init__(self, threshold): + self.threshold = threshold + + def process(self, boxes, scores): + return nms(boxes, scores, self.threshold) + +# GOOD: Use functools.partial or just pass the argument +from functools import partial + +apply_nms = partial(nms, iou_threshold=0.5) +filtered = apply_nms(boxes, scores) +``` diff --git a/skills/abstraction-patterns/references/wrapper-patterns.md b/skills/abstraction-patterns/references/wrapper-patterns.md new file mode 100644 index 0000000..5beaca3 --- /dev/null +++ b/skills/abstraction-patterns/references/wrapper-patterns.md @@ -0,0 +1,258 @@ +# Wrapper Patterns + +Scope: full worked wrappers around third-party APIs — context-managed video reading, validated image I/O, and a model inference wrapper. + +## Contents + +- [Pattern 1: VideoReader abstraction](#pattern-1-videoreader-abstraction) +- [Pattern 2: Image loading abstraction](#pattern-2-image-loading-abstraction) +- [Pattern 3: Model inference wrapper](#pattern-3-model-inference-wrapper) + +## Pattern 1: VideoReader abstraction + +Video reading involves resource management (opening/closing file handles), frame iteration, and metadata access — a perfect candidate for abstraction. Raw `cv2.VideoCapture` usage scattered across a codebase is easy to get wrong (forgetting `cap.release()`, missing `isOpened()` checks). Wrap it in a context manager: + +```python +"""Video reader with proper resource management.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Iterator + +import cv2 +import numpy as np + + +@dataclass(frozen=True) +class VideoMetadata: + """Immutable metadata for a video file.""" + + path: Path + fps: float + total_frames: int + width: int + height: int + + @property + def duration_seconds(self) -> float: + return self.total_frames / self.fps if self.fps > 0 else 0.0 + + +class VideoReader: + """Context-managed OpenCV video reader. + + Always use as a context manager so resources are released: + with VideoReader("input.mp4") as reader: + for frame in reader: + process(frame) + """ + + def __init__(self, path: str | Path) -> None: + self._path = Path(path) + self._cap: cv2.VideoCapture | None = None + self._metadata: VideoMetadata | None = None + + @property + def metadata(self) -> VideoMetadata: + if self._metadata is None: + raise RuntimeError("VideoReader must be used as a context manager") + return self._metadata + + def __enter__(self) -> VideoReader: + self._cap = cv2.VideoCapture(str(self._path)) + if not self._cap.isOpened(): + raise RuntimeError(f"Failed to open video: {self._path}") + self._metadata = VideoMetadata( + path=self._path, + fps=self._cap.get(cv2.CAP_PROP_FPS), + total_frames=int(self._cap.get(cv2.CAP_PROP_FRAME_COUNT)), + width=int(self._cap.get(cv2.CAP_PROP_FRAME_WIDTH)), + height=int(self._cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), + ) + return self + + def __exit__(self, *args: object) -> None: + if self._cap is not None: + self._cap.release() + self._cap = None + + def __iter__(self) -> Iterator[np.ndarray]: + if self._cap is None: + raise RuntimeError("VideoReader must be used as a context manager") + while True: + ret, frame = self._cap.read() + if not ret: + break + yield frame + + def read_frame(self, frame_idx: int) -> np.ndarray: + """Read a specific frame by zero-based index (BGR).""" + if self._cap is None: + raise RuntimeError("VideoReader must be used as a context manager") + if frame_idx < 0 or frame_idx >= self.metadata.total_frames: + raise IndexError(f"Frame index {frame_idx} out of range [0, {self.metadata.total_frames})") + self._cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) + ret, frame = self._cap.read() + if not ret: + raise RuntimeError(f"Failed to read frame {frame_idx}") + return frame +``` + +Usage is now clean and leak-free: + +```python +with VideoReader("input.mp4") as reader: + print(f"{reader.metadata.fps} FPS, {reader.metadata.total_frames} frames") + for frame in reader: + visualize(frame, model.predict(frame)) + middle = reader.read_frame(reader.metadata.total_frames // 2) +``` + +## Pattern 2: Image loading abstraction + +Image loading seems simple but involves format detection, color space conversion, validation, and error handling. Abstracting it prevents inconsistencies. + +```python +"""Robust image loading with validation.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Literal + +import cv2 +import numpy as np + +ColorSpace = Literal["rgb", "bgr", "gray"] + +SUPPORTED_EXTENSIONS: frozenset[str] = frozenset({ + ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".webp", +}) + + +def load_image( + path: str | Path, + color_space: ColorSpace = "rgb", + max_size: int | None = None, +) -> np.ndarray: + """Load an image with validation and optional longest-edge resize. + + Returns (H, W, 3) for color or (H, W) for grayscale. Raises + FileNotFoundError, ValueError (unsupported extension), or RuntimeError + (decode failure). + """ + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"Image not found: {path}") + if path.suffix.lower() not in SUPPORTED_EXTENSIONS: + raise ValueError(f"Unsupported image format: {path.suffix}") + + image = cv2.imread(str(path), cv2.IMREAD_COLOR) # OpenCV loads BGR + if image is None: + raise RuntimeError(f"Failed to decode image: {path}") + + if color_space == "rgb": + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + elif color_space == "gray": + image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # "bgr" needs no conversion + + if max_size is not None: + h, w = image.shape[:2] + scale = max_size / max(h, w) + if scale < 1.0: + image = cv2.resize(image, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA) + + return image + + +def save_image( + image: np.ndarray, + path: str | Path, + color_space: ColorSpace = "rgb", + quality: int = 95, +) -> None: + """Save an image, creating parent dirs and picking encode params by suffix.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if image.ndim not in (2, 3): + raise ValueError(f"Expected 2D or 3D array, got {image.ndim}D") + + if color_space == "rgb" and image.ndim == 3: + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + params: list[int] = [] + if path.suffix.lower() in (".jpg", ".jpeg"): + params = [cv2.IMWRITE_JPEG_QUALITY, quality] + elif path.suffix.lower() == ".png": + params = [cv2.IMWRITE_PNG_COMPRESSION, 3] + + cv2.imwrite(str(path), image, params) +``` + +## Pattern 3: Model inference wrapper + +Wrap model inference to handle preprocessing, batching, postprocessing, and device management in one place. + +```python +"""Model inference wrapper with preprocessing and postprocessing.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn + + +class InferenceWrapper: + """Runs inference with device management, preprocessing, batching, and postprocessing in one interface.""" + + def __init__( + self, + model: nn.Module, + device: str = "cuda", + input_size: tuple[int, int] = (224, 224), + mean: tuple[float, ...] = (0.485, 0.456, 0.406), + std: tuple[float, ...] = (0.229, 0.224, 0.225), + ) -> None: + self._model = model.to(device).eval() + self._device = torch.device(device) + self._input_size = input_size + self._mean = np.array(mean, dtype=np.float32) + self._std = np.array(std, dtype=np.float32) + + def preprocess(self, image: np.ndarray) -> torch.Tensor: + """Resize, normalize, CHW, add batch dim, move to device.""" + import cv2 + + resized = cv2.resize(image, (self._input_size[1], self._input_size[0])) + normalized = (resized.astype(np.float32) / 255.0 - self._mean) / self._std + tensor = torch.from_numpy(normalized).permute(2, 0, 1).unsqueeze(0) + return tensor.to(self._device) + + @torch.no_grad() + def predict(self, image: np.ndarray) -> np.ndarray: + return self._model(self.preprocess(image)).cpu().numpy() + + @torch.no_grad() + def predict_batch(self, images: list[np.ndarray]) -> np.ndarray: + batch = torch.cat([self.preprocess(img) for img in images], dim=0) + return self._model(batch).cpu().numpy() + + @classmethod + def from_checkpoint( + cls, + checkpoint_path: str | Path, + model_class: type[nn.Module], + **kwargs: object, + ) -> InferenceWrapper: + """Load model from a checkpoint and wrap it.""" + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True) + model = model_class(**checkpoint.get("hparams", {})) + model.load_state_dict(checkpoint["state_dict"]) + return cls(model=model, **kwargs) +``` diff --git a/skills/aws-sagemaker/SKILL.md b/skills/aws-sagemaker/SKILL.md index e0e8cb5..36c461c 100644 --- a/skills/aws-sagemaker/SKILL.md +++ b/skills/aws-sagemaker/SKILL.md @@ -27,9 +27,9 @@ project/ └── tests/{test_training_local.py, test_inference.py} ``` -## Training Jobs +## Essential Core: Launching a Training Job -Configure a PyTorch estimator with frozen Pydantic configs. Distribution is enabled automatically for multi-instance jobs. +Configure a PyTorch estimator with frozen Pydantic configs. Distribution is enabled automatically for multi-instance jobs. Submit with `wait=False` and poll — training jobs run for hours. ```python """SageMaker training job configuration.""" @@ -91,353 +91,20 @@ def launch_training( return estimator.latest_training_job.name ``` -### Training Script Entry Point - -SageMaker injects channels and paths via `SM_*` environment variables. Read them as argparse defaults, never hardcode paths. - -```python -"""src/training/train.py""" - -from __future__ import annotations - -import argparse -import json -import os -from pathlib import Path - -import torch -import torch.distributed as dist -from loguru import logger - - -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser() - p.add_argument("--epochs", type=int, default=50) - p.add_argument("--batch-size", type=int, default=32) - p.add_argument("--learning-rate", type=float, default=1e-3) - p.add_argument("--model-name", type=str, default="resnet50") - # SageMaker-injected paths - p.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model")) - p.add_argument("--train", default=os.environ.get("SM_CHANNEL_TRAIN")) - p.add_argument("--validation", default=os.environ.get("SM_CHANNEL_VALIDATION")) - p.add_argument("--output-data-dir", default=os.environ.get("SM_OUTPUT_DATA_DIR")) - return p.parse_args() - - -def train(args: argparse.Namespace) -> None: - logger.info("Starting training: {}", vars(args)) - world_size = int(os.environ.get("SM_NUM_GPUS", 1)) - local_rank = int(os.environ.get("LOCAL_RANK", 0)) - if world_size > 1: - dist.init_process_group(backend="nccl") - torch.cuda.set_device(local_rank) - - device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") - model = build_model(args.model_name, args.num_classes).to(device) - if world_size > 1: - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank]) - - # ... training loop ... - - torch.save(model.state_dict(), Path(args.model_dir) / "model.pth") - metrics = {"final_val_loss": 0.25, "final_val_acc": 0.92} - (Path(args.output_data_dir) / "metrics.json").write_text(json.dumps(metrics)) - - -if __name__ == "__main__": - train(parse_args()) -``` - -## Real-Time Endpoints - -Deploy a `PyTorchModel` with a custom inference script: - -```python -from pydantic import BaseModel -from sagemaker.pytorch import PyTorchModel - - -class EndpointConfig(BaseModel, frozen=True): - role: str - instance_type: str = "ml.g5.xlarge" - instance_count: int = 1 - endpoint_name: str - model_data_s3: str - framework_version: str = "2.1.0" - py_version: str = "py310" - - -def deploy_endpoint(config: EndpointConfig) -> str: - model = PyTorchModel( - model_data=config.model_data_s3, - role=config.role, - framework_version=config.framework_version, - py_version=config.py_version, - entry_point="inference.py", - source_dir="src/inference", - ) - predictor = model.deploy( - initial_instance_count=config.instance_count, - instance_type=config.instance_type, - endpoint_name=config.endpoint_name, - ) - return predictor.endpoint_name -``` - -### Custom Inference Handlers - -SageMaker calls these in order: `model_fn` (once at load), then `input_fn → predict_fn → output_fn` per request. - -```python -"""src/inference/inference.py""" - -from __future__ import annotations - -import io -import json - -import torch -from PIL import Image -from torchvision import transforms - - -def model_fn(model_dir: str) -> torch.nn.Module: - model = build_model("resnet50", num_classes=10) - model.load_state_dict(torch.load(f"{model_dir}/model.pth", map_location="cpu")) - model.eval() - return model.cuda() if torch.cuda.is_available() else model - - -def input_fn(request_body: bytes, content_type: str) -> torch.Tensor: - if content_type == "application/x-image": - image = Image.open(io.BytesIO(request_body)).convert("RGB") - tfm = transforms.Compose([ - transforms.Resize((224, 224)), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ]) - return tfm(image).unsqueeze(0) - if content_type == "application/json": - return torch.tensor(json.loads(request_body)["instances"]) - raise ValueError(f"Unsupported content type: {content_type}") - - -def predict_fn(input_data: torch.Tensor, model: torch.nn.Module) -> dict: - device = next(model.parameters()).device - with torch.no_grad(): - outputs = model(input_data.to(device)) - probs = torch.softmax(outputs, dim=1) - preds = torch.argmax(probs, dim=1) - return { - "predictions": preds.cpu().numpy().tolist(), - "probabilities": probs.cpu().numpy().tolist(), - } - - -def output_fn(prediction: dict, accept: str) -> str: - if accept == "application/json": - return json.dumps(prediction) - raise ValueError(f"Unsupported accept type: {accept}") -``` - -## Hyperparameter Tuning - -Bayesian search over parameter ranges. Metrics are parsed from training logs via regex `metric_definitions`. - -```python -from sagemaker.tuner import ( - CategoricalParameter, - ContinuousParameter, - HyperparameterTuner, - IntegerParameter, -) - - -def create_tuner(estimator: PyTorch) -> HyperparameterTuner: - return HyperparameterTuner( - estimator=estimator, - objective_metric_name="validation:accuracy", - objective_type="Maximize", - hyperparameter_ranges={ - "learning-rate": ContinuousParameter(1e-5, 1e-2, scaling_type="Logarithmic"), - "batch-size": CategoricalParameter([16, 32, 64, 128]), - "epochs": IntegerParameter(10, 100), - }, - metric_definitions=[ - {"Name": "validation:accuracy", "Regex": r"val_acc=(\S+)"}, - {"Name": "validation:loss", "Regex": r"val_loss=(\S+)"}, - ], - max_jobs=20, - max_parallel_jobs=4, - strategy="Bayesian", - ) -``` - -## SageMaker Pipelines - -End-to-end preprocess → train → conditional-register. Steps pass data via `.properties` references; registration only runs if accuracy clears a threshold. - -```python -import sagemaker -from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor -from sagemaker.pytorch import PyTorch -from sagemaker.workflow.condition_step import ConditionStep -from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo -from sagemaker.workflow.functions import JsonGet -from sagemaker.workflow.model_step import ModelStep -from sagemaker.workflow.parameters import ParameterFloat, ParameterString -from sagemaker.workflow.pipeline import Pipeline -from sagemaker.workflow.steps import ProcessingStep, TrainingStep - - -def create_pipeline(role: str, pipeline_name: str = "cv-training-pipeline") -> Pipeline: - session = sagemaker.Session() - input_data = ParameterString(name="InputData") - accuracy_threshold = ParameterFloat(name="AccuracyThreshold", default_value=0.90) - - processor = ScriptProcessor( - role=role, - image_uri=session.sagemaker_client.describe_image("pytorch-training")["ImageUri"], - instance_type="ml.m5.xlarge", - instance_count=1, - command=["python3"], - ) - preprocess = ProcessingStep( - name="PreprocessData", - processor=processor, - inputs=[ProcessingInput(source=input_data, destination="/opt/ml/processing/input")], - outputs=[ - ProcessingOutput(output_name="train", source="/opt/ml/processing/output/train"), - ProcessingOutput(output_name="val", source="/opt/ml/processing/output/val"), - ], - code="src/processing/preprocess.py", - ) - - estimator = PyTorch( - entry_point="train.py", - source_dir="src/training", - role=role, - instance_type="ml.g5.2xlarge", - instance_count=1, - framework_version="2.1.0", - py_version="py310", - ) - outs = preprocess.properties.ProcessingOutputConfig.Outputs - train_step = TrainingStep( - name="TrainModel", - estimator=estimator, - inputs={"train": outs["train"].S3Output.S3Uri, "validation": outs["val"].S3Output.S3Uri}, - ) - register = ModelStep( - name="RegisterModel", - step_args=estimator.register( - content_types=["application/json"], - response_types=["application/json"], - model_package_group_name="cv-models", - approval_status="PendingManualApproval", - ), - ) - condition = ConditionStep( - name="CheckAccuracy", - conditions=[ConditionGreaterThanOrEqualTo( - left=JsonGet(step_name=train_step.name, property_file="metrics", json_path="val_accuracy"), - right=accuracy_threshold, - )], - if_steps=[register], - else_steps=[], - ) - return Pipeline( - name=pipeline_name, - parameters=[input_data, accuracy_threshold], - steps=[preprocess, train_step, condition], - sagemaker_session=session, - ) -``` - -## S3 Data Management - -Use the SageMaker session for uploads (auto-resolves the default bucket); use boto3 to pull artifacts back. - -```python -from pathlib import Path - -import boto3 -import sagemaker -from loguru import logger - - -def upload_dataset(local_path: Path, bucket: str, prefix: str = "datasets") -> str: - s3_uri = sagemaker.Session().upload_data( - path=str(local_path), bucket=bucket, key_prefix=prefix - ) - logger.info("Uploaded {} to {}", local_path, s3_uri) - return s3_uri - - -def download_model_artifacts(model_data_s3: str, local_dir: Path) -> Path: - local_dir.mkdir(parents=True, exist_ok=True) - bucket, key = model_data_s3.replace("s3://", "").split("/", 1) - local_path = local_dir / Path(key).name - boto3.resource("s3").Bucket(bucket).download_file(key, str(local_path)) - logger.info("Downloaded {} to {}", model_data_s3, local_path) - return local_path -``` - -## Batch Transform - -Offline inference over an S3 dataset — reuses the same `PyTorchModel`/inference script as endpoints: - -```python -from sagemaker.pytorch import PyTorchModel - - -def run_batch_transform( - model_data_s3: str, input_s3_uri: str, output_s3_uri: str, role: str, - instance_type: str = "ml.g5.xlarge", -) -> None: - model = PyTorchModel( - model_data=model_data_s3, role=role, - framework_version="2.1.0", py_version="py310", - entry_point="inference.py", source_dir="src/inference", - ) - transformer = model.transformer( - instance_count=1, instance_type=instance_type, - output_path=output_s3_uri, strategy="MultiRecord", max_payload=6, - ) - transformer.transform(data=input_s3_uri, content_type="application/json", split_type="Line") -``` - -## Local Mode Testing - -Test the training script with `instance_type="local"` and `file://` inputs before submitting cloud jobs: - -```python -import pytest -from sagemaker.pytorch import PyTorch - - -@pytest.fixture -def local_estimator() -> PyTorch: - return PyTorch( - entry_point="train.py", - source_dir="src/training", - role="arn:aws:iam::000000000000:role/dummy", - instance_type="local", - instance_count=1, - framework_version="2.1.0", - py_version="py310", - hyperparameters={"epochs": 1, "batch-size": 4, "model-name": "resnet18"}, - ) - - -def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: - create_test_dataset(tmp_path / "train") - create_test_dataset(tmp_path / "val") - local_estimator.fit({ - "train": f"file://{tmp_path / 'train'}", - "validation": f"file://{tmp_path / 'val'}", - }) -``` +## Conventions + +- **Frozen Pydantic configs** for every job/endpoint/pipeline parameter set — no loose kwargs. +- **The training script reads `SM_*` environment variables as argparse defaults**, so the same + script runs locally and in the cloud. +- **Keep `framework_version` / `py_version` identical** between training and inference so + checkpoints load in the serving container. +- **One `inference.py`** (`model_fn`, `input_fn`, `predict_fn`, `output_fn`) shared by real-time + endpoints and batch transform. +- **Local mode first**: `instance_type="local"` with `file://` inputs, in CI, before any cloud job. +- **Pipelines take `ParameterString`/`ParameterFloat`** inputs and pass data between steps via + `.properties` references, never literal S3 paths. +- **Register models as `PendingManualApproval`** so promotion stays a deliberate act. +- **Delete idle endpoints** — they bill continuously; use batch transform for offline scoring. ## Anti-Patterns @@ -447,8 +114,18 @@ def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: - **Never put credentials in training scripts** — SageMaker injects the IAM role. - **Never download the full dataset inside the script** — use `SM_CHANNEL_*` input channels. - **Never block on long jobs** — submit with `wait=False` and poll status. +- **Never let training and inference preprocessing drift apart** — mismatched normalization is the most common cause of a "worse in production" model. ## Integration with Other Skills - **PyTorch Lightning** — LightningModule inside training jobs; **Hydra Config** — hyperparameters flattened to key-value pairs. - **W&B / MLflow** — experiment tracking in containers; **Docker CV** — custom containers when built-in images fall short. + +## Deep dives + +- `references/training-jobs.md` — read when writing `train.py`: `SM_*` environment variables, argparse wiring, distributed setup, instance sizing. +- `references/endpoints-and-inference.md` — read when deploying a real-time endpoint or writing the `model_fn`/`input_fn`/`predict_fn`/`output_fn` handlers. +- `references/batch-transform.md` — read when scoring an S3 dataset offline instead of standing up an endpoint. +- `references/hyperparameter-tuning.md` — read when running a Bayesian search, including the log-regex `metric_definitions` that make it work. +- `references/pipelines-and-model-registry.md` — read when composing preprocess → train → conditional-register into a SageMaker Pipeline, or versioning models in the registry. +- `references/s3-data-and-local-mode.md` — read when uploading datasets, pulling model artifacts back, or testing the training script with `instance_type="local"`. diff --git a/skills/aws-sagemaker/references/batch-transform.md b/skills/aws-sagemaker/references/batch-transform.md new file mode 100644 index 0000000..fdee4b7 --- /dev/null +++ b/skills/aws-sagemaker/references/batch-transform.md @@ -0,0 +1,56 @@ +# Batch Transform + +Offline inference over an S3 dataset, without standing up a persistent endpoint. + +## Running a Transform Job + +Offline inference over an S3 dataset — reuses the same `PyTorchModel`/inference script as endpoints: + +```python +from sagemaker.pytorch import PyTorchModel + + +def run_batch_transform( + model_data_s3: str, input_s3_uri: str, output_s3_uri: str, role: str, + instance_type: str = "ml.g5.xlarge", +) -> None: + model = PyTorchModel( + model_data=model_data_s3, role=role, + framework_version="2.1.0", py_version="py310", + entry_point="inference.py", source_dir="src/inference", + ) + transformer = model.transformer( + instance_count=1, instance_type=instance_type, + output_path=output_s3_uri, strategy="MultiRecord", max_payload=6, + ) + transformer.transform(data=input_s3_uri, content_type="application/json", split_type="Line") +``` + +## When to Use It + +Batch transform beats a real-time endpoint whenever there is no interactive consumer: nightly +scoring, backfilling predictions over a historical dataset, or evaluating a candidate model over +a full validation set. The instances are provisioned for the job and torn down afterwards, so +you pay only for the runtime instead of an always-on endpoint. + +## Parameters That Matter + +- **`strategy="MultiRecord"`** batches as many records as fit within `max_payload` into a single + invocation — far faster than `SingleRecord`, which calls the handler once per line. Use + `SingleRecord` only when the inference script cannot handle batched input. +- **`max_payload=6`** is megabytes per request. Raise it for larger batches, but the handler + must be able to hold that much decoded data in memory. +- **`split_type="Line"`** tells SageMaker how to split the input objects into records; use + `"Line"` for JSON-Lines input, `"None"` when each S3 object is one record (e.g. one image + per file, with `content_type="application/x-image"`). +- **`instance_count`** scales horizontally by sharding the input objects — increase it for large + datasets rather than reaching for a bigger single instance. + +## Output Layout + +Results land in `output_path` as one `.out` object per input object, in the same relative +prefix structure. To correlate predictions with inputs, either preserve an id field inside each +record or rely on the ordering within each file — SageMaker does not add one for you. + +The inference script is identical to the endpoint one (`model_fn`, `input_fn`, `predict_fn`, +`output_fn`), which is the main reason to keep it free of endpoint-specific assumptions. diff --git a/skills/aws-sagemaker/references/endpoints-and-inference.md b/skills/aws-sagemaker/references/endpoints-and-inference.md new file mode 100644 index 0000000..4401c5f --- /dev/null +++ b/skills/aws-sagemaker/references/endpoints-and-inference.md @@ -0,0 +1,131 @@ +# Real-Time Endpoints and Inference Handlers + +Deploying a trained model as a persistent HTTPS endpoint, and the four handler functions SageMaker calls to serve requests. + +## Contents + +- [Deploying an Endpoint](#deploying-an-endpoint) +- [Custom Inference Handlers](#custom-inference-handlers) +- [Handler Rules](#handler-rules) + +## Deploying an Endpoint + +Deploy a `PyTorchModel` with a custom inference script: + +```python +from pydantic import BaseModel +from sagemaker.pytorch import PyTorchModel + + +class EndpointConfig(BaseModel, frozen=True): + role: str + instance_type: str = "ml.g5.xlarge" + instance_count: int = 1 + endpoint_name: str + model_data_s3: str + framework_version: str = "2.1.0" + py_version: str = "py310" + + +def deploy_endpoint(config: EndpointConfig) -> str: + model = PyTorchModel( + model_data=config.model_data_s3, + role=config.role, + framework_version=config.framework_version, + py_version=config.py_version, + entry_point="inference.py", + source_dir="src/inference", + ) + predictor = model.deploy( + initial_instance_count=config.instance_count, + instance_type=config.instance_type, + endpoint_name=config.endpoint_name, + ) + return predictor.endpoint_name +``` + +Notes: + +- `model_data` points at the `model.tar.gz` a training job wrote to `output_path`. +- `source_dir` is uploaded and unpacked in the container; a `requirements.txt` there is + pip-installed at container start. +- `framework_version` / `py_version` select the managed DLC image — keep them identical to the + training job so the serialized weights load cleanly. +- Endpoints bill continuously while they exist. Delete them (`predictor.delete_endpoint()`) + when idle, or use batch transform for offline workloads. +- Passing an existing `endpoint_name` to `deploy` creates a new endpoint; to update in place, + create a new endpoint config and call `update_endpoint`. + +## Custom Inference Handlers + +SageMaker calls these in order: `model_fn` (once at load), then `input_fn → predict_fn → output_fn` per request. + +```python +"""src/inference/inference.py""" + +from __future__ import annotations + +import io +import json + +import torch +from PIL import Image +from torchvision import transforms + + +def model_fn(model_dir: str) -> torch.nn.Module: + model = build_model("resnet50", num_classes=10) + model.load_state_dict(torch.load(f"{model_dir}/model.pth", map_location="cpu")) + model.eval() + return model.cuda() if torch.cuda.is_available() else model + + +def input_fn(request_body: bytes, content_type: str) -> torch.Tensor: + if content_type == "application/x-image": + image = Image.open(io.BytesIO(request_body)).convert("RGB") + tfm = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + return tfm(image).unsqueeze(0) + if content_type == "application/json": + return torch.tensor(json.loads(request_body)["instances"]) + raise ValueError(f"Unsupported content type: {content_type}") + + +def predict_fn(input_data: torch.Tensor, model: torch.nn.Module) -> dict: + device = next(model.parameters()).device + with torch.no_grad(): + outputs = model(input_data.to(device)) + probs = torch.softmax(outputs, dim=1) + preds = torch.argmax(probs, dim=1) + return { + "predictions": preds.cpu().numpy().tolist(), + "probabilities": probs.cpu().numpy().tolist(), + } + + +def output_fn(prediction: dict, accept: str) -> str: + if accept == "application/json": + return json.dumps(prediction) + raise ValueError(f"Unsupported accept type: {accept}") +``` + +## Handler Rules + +- **`model_fn` runs once per container**, not per request. All expensive setup — weight loading, + compilation, warmup — belongs here. `map_location="cpu"` then `.cuda()` avoids device-mismatch + errors when the checkpoint was saved from a GPU. +- **`model.eval()` is not optional.** Forgetting it leaves dropout and batch-norm in training + mode and silently degrades predictions. +- **`input_fn` must dispatch on `content_type`** and raise for anything unsupported — an + unhandled content type otherwise produces a confusing 500 rather than a clear 415-style error. +- **The preprocessing in `input_fn` must match training exactly** (same resize, same + normalization constants). This is the single most common source of "the endpoint is worse + than my local eval". +- **`predict_fn` wraps inference in `torch.no_grad()`** and reads the device off the model + rather than assuming CUDA, so the same script runs on CPU instance types. +- **Return JSON-serializable Python**, never tensors or numpy arrays — hence `.cpu().numpy().tolist()`. +- The same `inference.py` is reused verbatim by batch transform, so keep it free of + endpoint-specific assumptions. diff --git a/skills/aws-sagemaker/references/hyperparameter-tuning.md b/skills/aws-sagemaker/references/hyperparameter-tuning.md new file mode 100644 index 0000000..41430e7 --- /dev/null +++ b/skills/aws-sagemaker/references/hyperparameter-tuning.md @@ -0,0 +1,58 @@ +# Hyperparameter Tuning + +Bayesian hyperparameter search over a SageMaker estimator, with metrics scraped from training logs. + +## Tuner Definition + +Bayesian search over parameter ranges. Metrics are parsed from training logs via regex `metric_definitions`. + +```python +from sagemaker.tuner import ( + CategoricalParameter, + ContinuousParameter, + HyperparameterTuner, + IntegerParameter, +) + + +def create_tuner(estimator: PyTorch) -> HyperparameterTuner: + return HyperparameterTuner( + estimator=estimator, + objective_metric_name="validation:accuracy", + objective_type="Maximize", + hyperparameter_ranges={ + "learning-rate": ContinuousParameter(1e-5, 1e-2, scaling_type="Logarithmic"), + "batch-size": CategoricalParameter([16, 32, 64, 128]), + "epochs": IntegerParameter(10, 100), + }, + metric_definitions=[ + {"Name": "validation:accuracy", "Regex": r"val_acc=(\S+)"}, + {"Name": "validation:loss", "Regex": r"val_loss=(\S+)"}, + ], + max_jobs=20, + max_parallel_jobs=4, + strategy="Bayesian", + ) +``` + +Launch it exactly like an estimator: `tuner.fit({"train": train_uri, "validation": val_uri})`. +`tuner.best_estimator()` returns the winning job for deployment or registration. + +## Rules + +- **Metrics come from stdout, not from an API.** The training script must actually print lines + matching `metric_definitions` regexes — e.g. `logger.info("val_acc={}", acc)`. If nothing + matches, every job reports no objective and the search degenerates to random. +- **The regex capture group is the value.** `r"val_acc=(\S+)"` captures everything up to + whitespace; make the surrounding text distinctive enough not to match training-loss lines. +- **Hyperparameter keys must match the estimator's flags** (`learning-rate`, not + `learning_rate`) — they are passed to the script the same way as static hyperparameters. +- **Use `scaling_type="Logarithmic"` for learning rates** and other quantities that span orders + of magnitude; linear scaling wastes most of the budget in the uninteresting upper decade. +- **`max_parallel_jobs` trades speed against search quality.** Bayesian optimization learns from + completed jobs, so running everything in parallel is equivalent to random search. Keep it to + roughly a fifth of `max_jobs`. +- **`epochs` as a tuned parameter is usually wasteful** — prefer a fixed epoch budget with early + stopping, and spend the search budget on learning rate, weight decay, and augmentation strength. +- Every trial is a full training job with its own instances. Cost is `max_jobs` × job cost — + size the instance type down before scaling the search up. diff --git a/skills/aws-sagemaker/references/pipelines-and-model-registry.md b/skills/aws-sagemaker/references/pipelines-and-model-registry.md new file mode 100644 index 0000000..b7a4c09 --- /dev/null +++ b/skills/aws-sagemaker/references/pipelines-and-model-registry.md @@ -0,0 +1,130 @@ +# SageMaker Pipelines and Model Registry + +Wiring preprocessing, training, and conditional model registration into a single reproducible pipeline definition. + +## Contents + +- [Pipeline Definition](#pipeline-definition) +- [How Steps Pass Data](#how-steps-pass-data) +- [Conditional Registration](#conditional-registration) +- [Running a Pipeline](#running-a-pipeline) + +## Pipeline Definition + +End-to-end preprocess → train → conditional-register. Steps pass data via `.properties` references; registration only runs if accuracy clears a threshold. + +```python +import sagemaker +from sagemaker.processing import ProcessingInput, ProcessingOutput, ScriptProcessor +from sagemaker.pytorch import PyTorch +from sagemaker.workflow.condition_step import ConditionStep +from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo +from sagemaker.workflow.functions import JsonGet +from sagemaker.workflow.model_step import ModelStep +from sagemaker.workflow.parameters import ParameterFloat, ParameterString +from sagemaker.workflow.pipeline import Pipeline +from sagemaker.workflow.steps import ProcessingStep, TrainingStep + + +def create_pipeline(role: str, pipeline_name: str = "cv-training-pipeline") -> Pipeline: + session = sagemaker.Session() + input_data = ParameterString(name="InputData") + accuracy_threshold = ParameterFloat(name="AccuracyThreshold", default_value=0.90) + + processor = ScriptProcessor( + role=role, + image_uri=session.sagemaker_client.describe_image("pytorch-training")["ImageUri"], + instance_type="ml.m5.xlarge", + instance_count=1, + command=["python3"], + ) + preprocess = ProcessingStep( + name="PreprocessData", + processor=processor, + inputs=[ProcessingInput(source=input_data, destination="/opt/ml/processing/input")], + outputs=[ + ProcessingOutput(output_name="train", source="/opt/ml/processing/output/train"), + ProcessingOutput(output_name="val", source="/opt/ml/processing/output/val"), + ], + code="src/processing/preprocess.py", + ) + + estimator = PyTorch( + entry_point="train.py", + source_dir="src/training", + role=role, + instance_type="ml.g5.2xlarge", + instance_count=1, + framework_version="2.1.0", + py_version="py310", + ) + outs = preprocess.properties.ProcessingOutputConfig.Outputs + train_step = TrainingStep( + name="TrainModel", + estimator=estimator, + inputs={"train": outs["train"].S3Output.S3Uri, "validation": outs["val"].S3Output.S3Uri}, + ) + register = ModelStep( + name="RegisterModel", + step_args=estimator.register( + content_types=["application/json"], + response_types=["application/json"], + model_package_group_name="cv-models", + approval_status="PendingManualApproval", + ), + ) + condition = ConditionStep( + name="CheckAccuracy", + conditions=[ConditionGreaterThanOrEqualTo( + left=JsonGet(step_name=train_step.name, property_file="metrics", json_path="val_accuracy"), + right=accuracy_threshold, + )], + if_steps=[register], + else_steps=[], + ) + return Pipeline( + name=pipeline_name, + parameters=[input_data, accuracy_threshold], + steps=[preprocess, train_step, condition], + sagemaker_session=session, + ) +``` + +## How Steps Pass Data + +- `preprocess.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri` is a + **symbolic reference**, not a value — it resolves at execution time. Referencing it in + `train_step` is also what creates the dependency edge, so the steps run in the right order + without an explicit `depends_on`. +- Never interpolate these references into an f-string; they are objects, and stringifying them + produces a placeholder rather than the real URI. +- `ParameterString` / `ParameterFloat` are the pipeline's inputs. Supply them at start time so + one definition serves dev and prod without editing code — this is what replaces hardcoded S3 + paths. +- `ProcessingStep` inputs/outputs map S3 prefixes to container paths under + `/opt/ml/processing/`; the processing script reads and writes those local paths only. + +## Conditional Registration + +`JsonGet(step_name=..., property_file="metrics", json_path="val_accuracy")` reads a value out of +a property file the training job emitted. The training script must write that JSON into +`SM_OUTPUT_DATA_DIR` and the step must declare the property file for this to resolve. + +`approval_status="PendingManualApproval"` puts the new model package in the registry without +promoting it — a human (or a separate approval pipeline) flips it to `Approved` before +deployment. Using `Approved` directly removes the only human gate between training and +production; do it only when downstream deployment has its own gate. + +`model_package_group_name` groups versions of the same logical model. Each pipeline run that +clears the threshold appends a new version, which is what gives you rollback. + +## Running a Pipeline + +```python +pipeline = create_pipeline(role=role) +pipeline.upsert(role_arn=role) +execution = pipeline.start(parameters={"InputData": "s3://bucket/raw/", "AccuracyThreshold": 0.92}) +``` + +`upsert` creates or updates the definition; `start` launches an execution. Poll with +`execution.describe()` rather than blocking — pipeline runs take hours. diff --git a/skills/aws-sagemaker/references/s3-data-and-local-mode.md b/skills/aws-sagemaker/references/s3-data-and-local-mode.md new file mode 100644 index 0000000..bd01440 --- /dev/null +++ b/skills/aws-sagemaker/references/s3-data-and-local-mode.md @@ -0,0 +1,97 @@ +# S3 Data Management and Local Mode Testing + +Moving datasets and model artifacts in and out of S3, and validating a training script locally before paying for cloud instances. + +## Contents + +- [S3 Data Management](#s3-data-management) +- [Local Mode Testing](#local-mode-testing) + +## S3 Data Management + +Use the SageMaker session for uploads (auto-resolves the default bucket); use boto3 to pull artifacts back. + +```python +from pathlib import Path + +import boto3 +import sagemaker +from loguru import logger + + +def upload_dataset(local_path: Path, bucket: str, prefix: str = "datasets") -> str: + s3_uri = sagemaker.Session().upload_data( + path=str(local_path), bucket=bucket, key_prefix=prefix + ) + logger.info("Uploaded {} to {}", local_path, s3_uri) + return s3_uri + + +def download_model_artifacts(model_data_s3: str, local_dir: Path) -> Path: + local_dir.mkdir(parents=True, exist_ok=True) + bucket, key = model_data_s3.replace("s3://", "").split("/", 1) + local_path = local_dir / Path(key).name + boto3.resource("s3").Bucket(bucket).download_file(key, str(local_path)) + logger.info("Downloaded {} to {}", model_data_s3, local_path) + return local_path +``` + +Notes: + +- `Session().upload_data` returns the full `s3://` URI, which is exactly what + `estimator.fit(inputs={"train": uri})` expects. Omit `bucket` to use the account's default + SageMaker bucket rather than hardcoding one. +- Training artifacts arrive as `model.tar.gz`; `download_model_artifacts` fetches the object, + then untar it locally to inspect weights or metrics. +- Keep a stable prefix convention (`datasets///`) so pipeline parameters can + point at a dataset version rather than an ad-hoc path. +- The training job never reaches into S3 itself — it reads from `SM_CHANNEL_*` paths that + SageMaker populated from these URIs. Downloading inside the script bypasses the channel + mechanism and breaks local mode. +- For very large datasets, prefer `input_mode="FastFile"` or Pipe mode on the estimator so the + job streams from S3 instead of waiting for a full EBS download. + +## Local Mode Testing + +Test the training script with `instance_type="local"` and `file://` inputs before submitting cloud jobs: + +```python +import pytest +from sagemaker.pytorch import PyTorch + + +@pytest.fixture +def local_estimator() -> PyTorch: + return PyTorch( + entry_point="train.py", + source_dir="src/training", + role="arn:aws:iam::000000000000:role/dummy", + instance_type="local", + instance_count=1, + framework_version="2.1.0", + py_version="py310", + hyperparameters={"epochs": 1, "batch-size": 4, "model-name": "resnet18"}, + ) + + +def test_training_local(local_estimator: PyTorch, tmp_path: Path) -> None: + create_test_dataset(tmp_path / "train") + create_test_dataset(tmp_path / "val") + local_estimator.fit({ + "train": f"file://{tmp_path / 'train'}", + "validation": f"file://{tmp_path / 'val'}", + }) +``` + +Notes: + +- Local mode runs the **real DLC container** on the local Docker daemon, so it catches the + failures that matter: missing dependencies, wrong entry-point path, `SM_*` misuse, and + serialization bugs. Docker must be installed and running. +- The IAM role is a dummy ARN — local mode never calls the SageMaker control plane. +- Use a tiny model and one epoch (`resnet18`, `epochs=1`, `batch-size=4`) with a synthetic + dataset in `tmp_path`. The point is to prove the script runs end to end, not to train anything. +- `instance_type="local_gpu"` exercises the GPU path if the dev machine has one and the NVIDIA + container runtime is installed. +- Run this in CI before any cloud submission — a typo caught here costs seconds instead of a + failed multi-hour GPU job. diff --git a/skills/aws-sagemaker/references/training-jobs.md b/skills/aws-sagemaker/references/training-jobs.md new file mode 100644 index 0000000..44d09a2 --- /dev/null +++ b/skills/aws-sagemaker/references/training-jobs.md @@ -0,0 +1,106 @@ +# Training Job Entry Points + +The `train.py` script that runs inside a SageMaker training container: how it receives paths and hyperparameters, and how it sets up distributed training. + +## Contents + +- [Entry Point Script](#entry-point-script) +- [SageMaker Environment Variables](#sagemaker-environment-variables) +- [Distributed Training](#distributed-training) +- [Instance Selection](#instance-selection) + +## Entry Point Script + +SageMaker injects channels and paths via `SM_*` environment variables. Read them as argparse defaults, never hardcode paths. + +```python +"""src/training/train.py""" + +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path + +import torch +import torch.distributed as dist +from loguru import logger + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser() + p.add_argument("--epochs", type=int, default=50) + p.add_argument("--batch-size", type=int, default=32) + p.add_argument("--learning-rate", type=float, default=1e-3) + p.add_argument("--model-name", type=str, default="resnet50") + # SageMaker-injected paths + p.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model")) + p.add_argument("--train", default=os.environ.get("SM_CHANNEL_TRAIN")) + p.add_argument("--validation", default=os.environ.get("SM_CHANNEL_VALIDATION")) + p.add_argument("--output-data-dir", default=os.environ.get("SM_OUTPUT_DATA_DIR")) + return p.parse_args() + + +def train(args: argparse.Namespace) -> None: + logger.info("Starting training: {}", vars(args)) + world_size = int(os.environ.get("SM_NUM_GPUS", 1)) + local_rank = int(os.environ.get("LOCAL_RANK", 0)) + if world_size > 1: + dist.init_process_group(backend="nccl") + torch.cuda.set_device(local_rank) + + device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") + model = build_model(args.model_name, args.num_classes).to(device) + if world_size > 1: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank]) + + # ... training loop ... + + torch.save(model.state_dict(), Path(args.model_dir) / "model.pth") + metrics = {"final_val_loss": 0.25, "final_val_acc": 0.92} + (Path(args.output_data_dir) / "metrics.json").write_text(json.dumps(metrics)) + + +if __name__ == "__main__": + train(parse_args()) +``` + +## SageMaker Environment Variables + +Hyperparameters passed to the estimator arrive as **command-line flags** with the same names, +which is why `hyperparameters=hp.model_dump()` on the estimator and `--batch-size` in argparse +line up (SageMaker converts the key `batch-size` to `--batch-size`). Use hyphens in the +`HyperParameters` model field names when they must match CLI flags. + +| Variable | Meaning | +| --- | --- | +| `SM_MODEL_DIR` | Where to write the final model. Contents are tarred to `output_path` on S3. | +| `SM_CHANNEL_` | Local path where the input channel `` from `estimator.fit(inputs=...)` was downloaded. | +| `SM_OUTPUT_DATA_DIR` | Where to write non-model outputs (metrics, plots). Also uploaded to S3. | +| `SM_NUM_GPUS` | GPU count on this instance. | +| `LOCAL_RANK` | Rank of this process within the instance, set by the distributed launcher. | + +Reading them as argparse **defaults** (rather than directly) means the same script runs +locally by passing flags explicitly — this is what makes local-mode testing possible. + +## Distributed Training + +- The estimator enables `distribution={"torch_distributed": {"enabled": True}}` only when + `instance_count > 1`; passing it for a single instance adds launcher overhead for nothing. +- `dist.init_process_group(backend="nccl")` requires no address/port arguments — SageMaker + populates the rendezvous environment variables. +- `torch.cuda.set_device(local_rank)` must be called **before** moving the model to the device, + otherwise every rank lands on GPU 0. +- `NCCL_DEBUG=INFO` and `TORCH_DISTRIBUTED_DEBUG=DETAIL` in the estimator's `environment` make + multi-node hangs diagnosable from CloudWatch logs. +- Only rank 0 should write to `SM_MODEL_DIR`; concurrent writes from all ranks corrupt the + checkpoint. + +## Instance Selection + +Start at `ml.g5.xlarge` (one A10G) and scale up only when profiling shows the GPU is saturated. +`ml.g5.2xlarge` is the usual default for CV training; reach for `ml.p4d`/`ml.p5` only for large +models or genuine multi-node work. `max_run` caps runaway jobs — set it deliberately rather than +accepting the 24-hour default silently. `volume_size` must exceed the size of all input channels +combined, since SageMaker downloads them to the instance's EBS volume before the script starts. diff --git a/skills/aws-sagemaker/skill.toml b/skills/aws-sagemaker/skill.toml index d7eca1c..00cf603 100644 --- a/skills/aws-sagemaker/skill.toml +++ b/skills/aws-sagemaker/skill.toml @@ -2,6 +2,7 @@ name = "aws-sagemaker" version = "1.0.0" category = "cloud" +tier = "extra" tags = ["aws", "sagemaker", "training", "deployment", "cloud", "mlops"] [dependencies] diff --git a/skills/data-pipelines/SKILL.md b/skills/data-pipelines/SKILL.md index ab4ec37..e42c149 100644 --- a/skills/data-pipelines/SKILL.md +++ b/skills/data-pipelines/SKILL.md @@ -12,7 +12,9 @@ description: > # Data Pipelines for CV/ML -Reproducible, scalable pipelines that feed ML training and inference. +Reproducible, scalable pipelines that feed ML training and inference. This page is the +index: core principles, the leakage rule, and the pipeline shape are inline; the full +patterns live in `references/` and should be read only when the task calls for them. ## Core Principles @@ -22,12 +24,13 @@ Reproducible, scalable pipelines that feed ML training and inference. 4. **Immutable datasets.** Published versions are never modified in place; new versions carry lineage to their source. 5. **Fail fast, fail loud.** Raise on quality violations immediately rather than propagating corrupt data downstream. -```bash -pixi add polars pillow lmdb -pixi add --pypi great-expectations albumentations webdataset -``` +## Pipeline Shape -## Pipeline Architecture +A pipeline is a fixed sequence of named stages — `extract → validate_raw → transform → +validate_transformed → load` — with a real gate between each. The manifest is the source of +truth: one validated row per sample with a content hash, written as Parquet (never CSV). +`split` is deliberately *not* a manifest field — it is assigned downstream so a dataset can +be re-split without re-extracting. ``` Building a data pipeline? @@ -38,149 +41,17 @@ Building a data pipeline? └── Annotation pipeline ........ Label Studio / CVAT + validation hooks ``` -A pipeline is a fixed sequence of named stages — `extract → validate_raw → -transform → validate_transformed → load` — with a real gate between each. -Configure it with Pydantic so bad parameters fail at load time, not three hours in. - -```python -"""Data pipeline configuration.""" - -from pathlib import Path -from typing import Literal - -from pydantic import BaseModel, Field, ValidationInfo, field_validator - - -class PipelineConfig(BaseModel): - """Top-level data pipeline configuration.""" +## Splitting Without Leakage — The Single Highest-Value Rule - name: str = Field(min_length=1) - source_dir: Path - output_dir: Path - storage_format: Literal["parquet", "arrow", "lmdb", "tfrecord", "webdataset"] = "parquet" - num_workers: int = Field(ge=1, le=64, default=8) - schema_version: str = Field(pattern=r"^\d+\.\d+\.\d+$", default="1.0.0") - - @field_validator("output_dir") - @classmethod - def output_must_differ_from_source(cls, v: Path, info: ValidationInfo) -> Path: - """Prevent writing transformed data over the raw inputs.""" - if v == info.data.get("source_dir"): - msg = "output_dir must differ from source_dir" - raise ValueError(msg) - return v -``` - -The anti-pattern this replaces: a bare `process_data(input_path, output_path)` that -globs `*.jpg` and rewrites images with no schema, no logging, and no guard against -clobbering the raw inputs. - -## ETL: Building a Dataset Manifest - -The manifest is the source of truth: one validated row per sample, carrying a -content hash for deduplication and integrity, written as Parquet (never CSV). +**This is the highest-risk step in any CV pipeline.** Correlated samples — frames from the +same video, images of the same patient, plays from the same match, crops of the same player +— are near-duplicates. If they straddle train/val/test, validation measures memorization +rather than generalization and the model fails silently in production. **Split on the +*group*, never on the row**, whenever a grouping key exists: video/clip ID, patient/subject +ID, match/session ID, player identity, camera or site ID, or capture date for +time-correlated data. Stratify by class only when no such key exists. ```python -"""ETL for image datasets: hash, validate, write a Parquet manifest.""" - -import hashlib -from pathlib import Path - -import polars as pl -from loguru import logger -from PIL import Image -from pydantic import BaseModel, Field - - -class ImageRecord(BaseModel): - """Validated image metadata record.""" - - file_hash: str = Field(min_length=64, max_length=64) # SHA-256 - relative_path: str - width: int = Field(ge=1) - height: int = Field(ge=1) - channels: int = Field(ge=1, le=4) - label: str - group_id: str | None = None # patient / video / scene identifier - file_size_bytes: int = Field(ge=1) - - -def compute_file_hash(path: Path) -> str: - """SHA-256 of file contents, for content-addressable storage.""" - hasher = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(8192), b""): - hasher.update(chunk) - return hasher.hexdigest() - - -def build_manifest(paths: list[Path], labels: dict[Path, str], out: Path) -> pl.DataFrame: - """Read image metadata into validated records and write them as Parquet.""" - records = [] - for path in paths: - with Image.open(path) as img: - width, height, channels = *img.size, len(img.getbands()) - records.append( - ImageRecord( - file_hash=compute_file_hash(path), - relative_path=str(path), - width=width, - height=height, - channels=channels, - label=labels[path], - file_size_bytes=path.stat().st_size, - ).model_dump() - ) - df = pl.DataFrame(records) - df.write_parquet(out, compression="zstd") - logger.info("Manifest written: {} records to {}", len(df), out) - return df -``` - -Hashes deduplicate byte-identical images that would otherwise straddle splits and -detect corruption between runs. `split` is deliberately *not* a manifest field: it -is assigned downstream, so a dataset can be re-split without re-extracting. - -## Dataset Splitting Without Leakage - -**This is the highest-risk step in any CV pipeline.** Correlated samples — frames -from the same video, images of the same patient, plays from the same match, crops -of the same player — are near-duplicates. If they straddle train/val/test, -validation measures memorization rather than generalization and the model fails -silently in production. Split on the *group*, never on the row, whenever a grouping -key exists: video/clip ID, patient/subject ID, match/session ID, player identity, -camera or site ID, or capture date for time-correlated data. Stratify only when no -such key exists. - -```python -"""Dataset splitting with stratification and group-aware leak prevention.""" - -from __future__ import annotations # for the SplitConfig self-reference - -import polars as pl -from loguru import logger -from pydantic import BaseModel, Field, model_validator - - -class SplitConfig(BaseModel): - """Configuration for dataset splitting.""" - - train_ratio: float = Field(gt=0.0, lt=1.0, default=0.7) - val_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) - test_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) - seed: int = 42 - stratify_column: str = "label" - group_column: str | None = None # set whenever a grouping key exists - - @model_validator(mode="after") - def ratios_must_sum_to_one(self) -> SplitConfig: - total = self.train_ratio + self.val_ratio + self.test_ratio - if abs(total - 1.0) > 1e-6: - msg = f"Split ratios must sum to 1.0, got {total}" - raise ValueError(msg) - return self - - def split_dataset(manifest: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: """Group-aware split when group_column is set, otherwise stratified.""" if config.group_column is not None: @@ -188,272 +59,31 @@ def split_dataset(manifest: pl.DataFrame, config: SplitConfig) -> dict[str, pl.D return stratified_split(manifest, config) -def group_aware_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: - """Assign whole groups to a split so no group straddles train/val/test.""" - key = config.group_column - assert key is not None - groups = df[key].unique().sample(fraction=1.0, seed=config.seed, shuffle=True) - n_train = int(len(groups) * config.train_ratio) - n_val = int(len(groups) * config.val_ratio) - - train_groups = set(groups[:n_train].to_list()) - val_groups = set(groups[n_train : n_train + n_val].to_list()) - test_groups = set(groups[n_train + n_val :].to_list()) - - # Leakage checks belong in the pipeline, not only in tests. - pairs = {"train/val": (train_groups, val_groups), "train/test": (train_groups, test_groups)} - pairs["val/test"] = (val_groups, test_groups) - for name, (a, b) in pairs.items(): - if not a.isdisjoint(b): - msg = f"{name} group overlap — data leakage" - raise ValueError(msg) - - splits = { - "train": df.filter(pl.col(key).is_in(train_groups)), - "val": df.filter(pl.col(key).is_in(val_groups)), - "test": df.filter(pl.col(key).is_in(test_groups)), - } - for name, part in splits.items(): - logger.info("Split '{}': {} records ({} groups)", name, len(part), part[key].n_unique()) - return splits - - -def stratified_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: - """Per-class stratified random split — only when there is no grouping key.""" - parts: dict[str, list[pl.DataFrame]] = {"train": [], "val": [], "test": []} - for label in sorted(df[config.stratify_column].unique().to_list()): - rows = df.filter(pl.col(config.stratify_column) == label).sample( - fraction=1.0, seed=config.seed, shuffle=True - ) - a = int(len(rows) * config.train_ratio) - b = a + int(len(rows) * config.val_ratio) - parts["train"].append(rows[:a]) - parts["val"].append(rows[a:b]) - parts["test"].append(rows[b:]) - return {name: pl.concat(chunks) for name, chunks in parts.items()} +# Inside group_aware_split: leakage checks belong in the pipeline, not only in tests. +for name, (a, b) in pairs.items(): # train/val, train/test, val/test + if not a.isdisjoint(b): + msg = f"{name} group overlap — data leakage" + raise ValueError(msg) ``` -Anti-pattern — a plain random split silently leaks every group: - ```python # WRONG: neighbouring frames of the same clip land in both train and test. train, val, test = np.split(df.sample(frac=1), [int(0.7 * len(df)), int(0.85 * len(df))]) ``` -Group-aware splits skew ratios when group sizes are uneven. Always log realized -per-split record counts and class distribution and check them before training; -rebalance by greedily assigning the largest groups first if needed. - -## Storage Format Selection - -``` -Choosing a storage format? -├── Tabular metadata (labels, splits, paths, hashes) -│ └── Parquet ....... columnar, compressed, fast predicate filtering (Polars/DuckDB) -├── Streaming large image/video datasets -│ ├── WebDataset .... .tar shards; distributed/multi-node, S3-friendly, no random access -│ └── TFRecord ...... TensorFlow ecosystem, sequential reads -├── Random-access image datasets (single node, reshuffled every epoch) -│ └── LMDB .......... memory-mapped zero-copy reads, single-writer, bad over NFS -├── In-memory analytics / cross-language interchange -│ └── Arrow IPC ..... zero-copy, language-agnostic -└── Small datasets (< 1 GB) or active annotation churn - └── Raw folders + Parquet manifest ..... simplest, debuggable, diffable -``` - -Rules of thumb: raw files until random reads become the bottleneck; LMDB for -single-node random access; WebDataset once training spans nodes or lives in object -storage; Parquet for everything tabular, always. Write manifests with Hive-style -partitions (`split=train/data.parquet`, `compression="zstd"`, -`row_group_size=10_000`) so readers can skip whole splits. - -```python -"""LMDB packing for fast random-access image reads.""" - -from pathlib import Path - -import lmdb -from loguru import logger +Group-aware splits skew ratios when group sizes are uneven — always log realized per-split +record counts and class distribution and check them before training. Full implementation: +`references/dataset-splitting.md`. +## Conventions -def build_lmdb_dataset(image_paths: list[Path], out_path: Path, map_gb: int = 50) -> None: - """Pack encoded image bytes into LMDB keyed by zero-padded index.""" - env = lmdb.open(str(out_path), map_size=map_gb * 1024**3) - with env.begin(write=True) as txn: - for idx, img_path in enumerate(image_paths): - txn.put(f"{idx:08d}".encode(), img_path.read_bytes()) - txn.put(b"__len__", str(len(image_paths)).encode()) - env.close() - logger.info("Built LMDB: {} images at {}", len(image_paths), out_path) -``` - -## Data Quality Validation - -Two complementary layers: Pydantic validates each record at construction time; -Great Expectations validates the dataset in aggregate — distributions, uniqueness, -ranges — and emits a durable report artifact. - -```python -"""Great Expectations suite for an image dataset manifest.""" - -import great_expectations as gx -from loguru import logger - -COLUMNS = ["file_hash", "relative_path", "width", "height", "channels", "label", "group_id"] - - -def create_image_dataset_expectations(context: gx.DataContext) -> None: - """Define the expectation suite for an image classification manifest.""" - suite = context.add_expectation_suite("image_classification_suite") - gxe = gx.expectations - expectations = [ - gxe.ExpectTableColumnsToMatchSet(column_set=COLUMNS), - *(gxe.ExpectColumnValuesToNotBeNull(column=c) for c in ("file_hash", "label")), - *(gxe.ExpectColumnValuesToBeBetween(column=c, min_value=32, max_value=8192) - for c in ("width", "height")), - # Duplicate hashes mean duplicate images — a cross-split leakage vector. - gxe.ExpectColumnValuesToBeUnique(column="file_hash"), - ] - for expectation in expectations: - suite.add_expectation(expectation) - logger.info("Created suite with {} expectations", len(expectations)) -``` - -Mirror the cheapest checks as an in-pipeline gate — null counts per column, -`df["file_hash"].is_duplicated().sum()`, per-class counts from -`df.group_by("label").len()`, and a filter for undersized images — returning a -frozen Pydantic `QualityReport` and raising when it fails. Run the gate after -extraction *and* after splitting: a healthy manifest still yields empty or -single-class splits when ratios or group sizes are wrong. - -## Augmentation Pipeline Design - -Augmentation is part of the data contract, not a training detail: configure it, -scale it with a single `strength` knob, and keep val/test strictly deterministic. - -```python -"""Albumentations pipelines driven by config.""" - -import albumentations as A -from albumentations.pytorch import ToTensorV2 -from pydantic import BaseModel, Field - -MEAN, STD = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225) - - -class AugmentationConfig(BaseModel): - image_size: int = Field(ge=32, default=640) - strength: float = Field(ge=0.0, le=1.0, default=0.5) - - -def build_train_transforms(cfg: AugmentationConfig) -> A.Compose: - """Training augmentations, all probabilities scaled by a single strength knob.""" - s, size = cfg.strength, cfg.image_size - return A.Compose( - [ - A.RandomResizedCrop(height=size, width=size, scale=(0.5, 1.0)), - A.HorizontalFlip(p=0.5), - A.ColorJitter(brightness=0.2 * s, contrast=0.2 * s, saturation=0.2 * s, p=0.8), - A.GaussNoise(p=0.3 * s), - A.CoarseDropout(max_holes=int(8 * s), p=0.3 * s), - A.Normalize(mean=MEAN, std=STD), - ToTensorV2(), - ], - bbox_params=A.BboxParams(format="pascal_voc", label_fields=["class_labels"]), - ) -``` - -Val/test transforms are the same `Compose` with everything random removed — -`A.Resize`, `A.Normalize`, `ToTensorV2`, nothing else. Log the augmentation config -with the run: an unrecorded `strength` change is an unreproducible run. - -## Large-Scale Processing - -Chunked parallel processing with per-item error isolation and progress logging; -streaming reads for datasets larger than RAM. - -```python -"""Parallel chunked processing and streaming reads.""" - -from collections.abc import Callable -from concurrent.futures import ProcessPoolExecutor, as_completed -from pathlib import Path -from typing import Any - - -def process_in_chunks( - paths: list[Path], fn: Callable[[Path], Any], workers: int = 8, chunk: int = 1000 -) -> list[Any]: - """Process files in parallel chunks; one failed item never kills the run.""" - results: list[Any] = [] - total = len(paths) - with ProcessPoolExecutor(max_workers=workers) as executor: - for start in range(0, total, chunk): - futures = {executor.submit(fn, p): p for p in paths[start : start + chunk]} - for future in as_completed(futures): - try: - results.append(future.result(timeout=300)) - except Exception: - logger.exception("Failed to process: {}", futures[future]) - done = min(start + chunk, total) - logger.info("Progress: {}/{} ({:.1f}%)", done, total, done / total * 100) - logger.info("Processed {}/{} files successfully", len(results), total) - return results -``` - -For datasets larger than RAM, never call `pl.read_parquet`. Scan lazily and pull -fixed-size slices: `reader = pl.scan_parquet(path)`, then -`reader.slice(offset, batch).collect()` per batch, with the row count from -`reader.select(pl.len()).collect().item()`. - -## Schema Evolution and Migration - -Never mutate a dataset schema in place. Declare each version as its own model, -register it, and provide an explicit migration function per hop. - -```python -"""Versioned dataset schemas with an explicit migration chain.""" - -from pydantic import BaseModel, Field - - -class SchemaV1(BaseModel): - """Original — path and label only.""" - image_path: str - label: str - - -class SchemaV2(SchemaV1): - """V2 — adds dimensions and content hash.""" - width: int = Field(ge=1) - height: int = Field(ge=1) - file_hash: str - - -class SchemaV3(SchemaV2): - """V3 — adds split assignment and quality score.""" - split: str = Field(pattern=r"^(train|val|test)$") - quality_score: float = Field(ge=0.0, le=1.0, default=1.0) - - -def migrate_v2_to_v3(record: SchemaV2, split: str, quality_score: float = 1.0) -> SchemaV3: - """Enrich a V2 record with its split assignment and quality score.""" - return SchemaV3(**record.model_dump(), split=split, quality_score=quality_score) - - -# Ordered registry: reading version N and targeting M applies hops N..M in order. -SCHEMA_REGISTRY: dict[str, type[BaseModel]] = { - "1.0.0": SchemaV1, - "2.0.0": SchemaV2, - "3.0.0": SchemaV3, -} -MIGRATIONS = {("2.0.0", "3.0.0"): migrate_v2_to_v3} -``` - -Subclassing keeps additive versions short, but write a standalone model whenever a -field changes meaning or type — inheritance must never hide a breaking change. -Store `schema_version` alongside the data (Parquet key-value metadata or a sidecar -`dataset.json`) so readers dispatch to the right model instead of guessing. +- Configure every stage with a validated **Pydantic** model; bad parameters must fail at load time, not three hours in. +- **Parquet** (`compression="zstd"`) for all tabular data; LMDB for single-node random access; WebDataset for multi-node streaming. +- **Content-hash** every sample (SHA-256) for deduplication and corruption detection. +- **Log with loguru** at every stage boundary — realized counts, not just "done". +- Validate **after extraction and after splitting**; a healthy manifest still yields empty or single-class splits when ratios are wrong. +- Keep augmentation **config-driven** with one `strength` knob, and log the config with the run. +- Version schemas explicitly and store `schema_version` alongside the data. ## Anti-Patterns @@ -467,6 +97,16 @@ Store `schema_version` alongside the data (Parquet key-value metadata or a sidec - **Never hard-code paths or ratios.** Put them in a validated Pydantic config. - **Never version data in Git.** Keep large artifacts in object storage and track a manifest. +## Deep dives + +- `references/dataset-splitting.md` — read when splitting a dataset, choosing a grouping key, or auditing an existing split for leakage. Full `SplitConfig`, `group_aware_split`, and `stratified_split`. +- `references/pipeline-architecture.md` — read when standing up a new pipeline: sizing the stack, the Pydantic `PipelineConfig`, and building the hashed Parquet manifest. +- `references/storage-formats.md` — read when choosing between Parquet, LMDB, WebDataset, TFRecord, Arrow, or raw folders, or packing images into LMDB. +- `references/data-quality.md` — read when adding validation: Great Expectations suites for a manifest and the cheap in-pipeline quality gate. +- `references/augmentation.md` — read when designing an augmentation pipeline or wiring Albumentations to a config with a single strength knob. +- `references/large-scale-processing.md` — read when data exceeds RAM or a single core: chunked `ProcessPoolExecutor` processing and lazy Polars scans. +- `references/schema-evolution.md` — read when a dataset schema must change: versioned Pydantic models, the migration registry, and when not to subclass. + ## Related Skills - `pydantic` — validated configs and schema definitions for every stage diff --git a/skills/data-pipelines/references/augmentation.md b/skills/data-pipelines/references/augmentation.md new file mode 100644 index 0000000..1ebfc70 --- /dev/null +++ b/skills/data-pipelines/references/augmentation.md @@ -0,0 +1,48 @@ +# Augmentation Pipeline Design + +Config-driven Albumentations pipelines, with a single strength knob and strictly deterministic val/test transforms. + +## Principle + +Augmentation is part of the data contract, not a training detail: configure it, +scale it with a single `strength` knob, and keep val/test strictly deterministic. + +## Config-Driven Albumentations + +```python +"""Albumentations pipelines driven by config.""" + +import albumentations as A +from albumentations.pytorch import ToTensorV2 +from pydantic import BaseModel, Field + +MEAN, STD = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225) + + +class AugmentationConfig(BaseModel): + image_size: int = Field(ge=32, default=640) + strength: float = Field(ge=0.0, le=1.0, default=0.5) + + +def build_train_transforms(cfg: AugmentationConfig) -> A.Compose: + """Training augmentations, all probabilities scaled by a single strength knob.""" + s, size = cfg.strength, cfg.image_size + return A.Compose( + [ + A.RandomResizedCrop(height=size, width=size, scale=(0.5, 1.0)), + A.HorizontalFlip(p=0.5), + A.ColorJitter(brightness=0.2 * s, contrast=0.2 * s, saturation=0.2 * s, p=0.8), + A.GaussNoise(p=0.3 * s), + A.CoarseDropout(max_holes=int(8 * s), p=0.3 * s), + A.Normalize(mean=MEAN, std=STD), + ToTensorV2(), + ], + bbox_params=A.BboxParams(format="pascal_voc", label_fields=["class_labels"]), + ) +``` + +## Val/Test Transforms + +Val/test transforms are the same `Compose` with everything random removed — +`A.Resize`, `A.Normalize`, `ToTensorV2`, nothing else. Log the augmentation config +with the run: an unrecorded `strength` change is an unreproducible run. diff --git a/skills/data-pipelines/references/data-quality.md b/skills/data-pipelines/references/data-quality.md new file mode 100644 index 0000000..bbf3387 --- /dev/null +++ b/skills/data-pipelines/references/data-quality.md @@ -0,0 +1,46 @@ +# Data Quality Validation + +Two-layer validation for datasets: per-record Pydantic checks plus aggregate suite validation with Great Expectations. + +## The Two Layers + +Two complementary layers: Pydantic validates each record at construction time; +Great Expectations validates the dataset in aggregate — distributions, uniqueness, +ranges — and emits a durable report artifact. + +## Great Expectations Suite + +```python +"""Great Expectations suite for an image dataset manifest.""" + +import great_expectations as gx +from loguru import logger + +COLUMNS = ["file_hash", "relative_path", "width", "height", "channels", "label", "group_id"] + + +def create_image_dataset_expectations(context: gx.DataContext) -> None: + """Define the expectation suite for an image classification manifest.""" + suite = context.add_expectation_suite("image_classification_suite") + gxe = gx.expectations + expectations = [ + gxe.ExpectTableColumnsToMatchSet(column_set=COLUMNS), + *(gxe.ExpectColumnValuesToNotBeNull(column=c) for c in ("file_hash", "label")), + *(gxe.ExpectColumnValuesToBeBetween(column=c, min_value=32, max_value=8192) + for c in ("width", "height")), + # Duplicate hashes mean duplicate images — a cross-split leakage vector. + gxe.ExpectColumnValuesToBeUnique(column="file_hash"), + ] + for expectation in expectations: + suite.add_expectation(expectation) + logger.info("Created suite with {} expectations", len(expectations)) +``` + +## In-Pipeline Gate + +Mirror the cheapest checks as an in-pipeline gate — null counts per column, +`df["file_hash"].is_duplicated().sum()`, per-class counts from +`df.group_by("label").len()`, and a filter for undersized images — returning a +frozen Pydantic `QualityReport` and raising when it fails. Run the gate after +extraction *and* after splitting: a healthy manifest still yields empty or +single-class splits when ratios or group sizes are wrong. diff --git a/skills/data-pipelines/references/dataset-splitting.md b/skills/data-pipelines/references/dataset-splitting.md new file mode 100644 index 0000000..d2b6852 --- /dev/null +++ b/skills/data-pipelines/references/dataset-splitting.md @@ -0,0 +1,119 @@ +# Dataset Splitting Without Leakage + +Group-aware and stratified splitting for CV/ML datasets — the highest-risk step in any pipeline. + +## Contents + +- [Why Group-Aware Splitting Matters](#why-group-aware-splitting-matters) +- [Implementation](#implementation) +- [The Anti-Pattern](#the-anti-pattern) +- [Skewed Ratios](#skewed-ratios) + +## Why Group-Aware Splitting Matters + +**This is the highest-risk step in any CV pipeline.** Correlated samples — frames +from the same video, images of the same patient, plays from the same match, crops +of the same player — are near-duplicates. If they straddle train/val/test, +validation measures memorization rather than generalization and the model fails +silently in production. Split on the *group*, never on the row, whenever a grouping +key exists: video/clip ID, patient/subject ID, match/session ID, player identity, +camera or site ID, or capture date for time-correlated data. Stratify only when no +such key exists. + +## Implementation + +```python +"""Dataset splitting with stratification and group-aware leak prevention.""" + +from __future__ import annotations # for the SplitConfig self-reference + +import polars as pl +from loguru import logger +from pydantic import BaseModel, Field, model_validator + + +class SplitConfig(BaseModel): + """Configuration for dataset splitting.""" + + train_ratio: float = Field(gt=0.0, lt=1.0, default=0.7) + val_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + test_ratio: float = Field(gt=0.0, lt=1.0, default=0.15) + seed: int = 42 + stratify_column: str = "label" + group_column: str | None = None # set whenever a grouping key exists + + @model_validator(mode="after") + def ratios_must_sum_to_one(self) -> SplitConfig: + total = self.train_ratio + self.val_ratio + self.test_ratio + if abs(total - 1.0) > 1e-6: + msg = f"Split ratios must sum to 1.0, got {total}" + raise ValueError(msg) + return self + + +def split_dataset(manifest: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Group-aware split when group_column is set, otherwise stratified.""" + if config.group_column is not None: + return group_aware_split(manifest, config) + return stratified_split(manifest, config) + + +def group_aware_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Assign whole groups to a split so no group straddles train/val/test.""" + key = config.group_column + assert key is not None + groups = df[key].unique().sample(fraction=1.0, seed=config.seed, shuffle=True) + n_train = int(len(groups) * config.train_ratio) + n_val = int(len(groups) * config.val_ratio) + + train_groups = set(groups[:n_train].to_list()) + val_groups = set(groups[n_train : n_train + n_val].to_list()) + test_groups = set(groups[n_train + n_val :].to_list()) + + # Leakage checks belong in the pipeline, not only in tests. + pairs = {"train/val": (train_groups, val_groups), "train/test": (train_groups, test_groups)} + pairs["val/test"] = (val_groups, test_groups) + for name, (a, b) in pairs.items(): + if not a.isdisjoint(b): + msg = f"{name} group overlap — data leakage" + raise ValueError(msg) + + splits = { + "train": df.filter(pl.col(key).is_in(train_groups)), + "val": df.filter(pl.col(key).is_in(val_groups)), + "test": df.filter(pl.col(key).is_in(test_groups)), + } + for name, part in splits.items(): + logger.info("Split '{}': {} records ({} groups)", name, len(part), part[key].n_unique()) + return splits + + +def stratified_split(df: pl.DataFrame, config: SplitConfig) -> dict[str, pl.DataFrame]: + """Per-class stratified random split — only when there is no grouping key.""" + parts: dict[str, list[pl.DataFrame]] = {"train": [], "val": [], "test": []} + for label in sorted(df[config.stratify_column].unique().to_list()): + rows = df.filter(pl.col(config.stratify_column) == label).sample( + fraction=1.0, seed=config.seed, shuffle=True + ) + a = int(len(rows) * config.train_ratio) + b = a + int(len(rows) * config.val_ratio) + parts["train"].append(rows[:a]) + parts["val"].append(rows[a:b]) + parts["test"].append(rows[b:]) + return {name: pl.concat(chunks) for name, chunks in parts.items()} +``` + +## The Anti-Pattern + +A plain random split silently leaks every group: + +```python +# WRONG: neighbouring frames of the same clip land in both train and test. +train, val, test = np.split(df.sample(frac=1), [int(0.7 * len(df)), int(0.85 * len(df))]) +``` + +## Skewed Ratios + +Group-aware splits skew ratios when group sizes are uneven. Always log realized +per-split record counts and class distribution and check them before training; +rebalance by greedily assigning the largest groups first if needed. diff --git a/skills/data-pipelines/references/large-scale-processing.md b/skills/data-pipelines/references/large-scale-processing.md new file mode 100644 index 0000000..bce12c4 --- /dev/null +++ b/skills/data-pipelines/references/large-scale-processing.md @@ -0,0 +1,44 @@ +# Large-Scale Processing + +Parallel chunked processing with error isolation, and streaming reads for datasets larger than RAM. + +## Chunked Parallel Processing + +Chunked parallel processing with per-item error isolation and progress logging; +streaming reads for datasets larger than RAM. + +```python +"""Parallel chunked processing and streaming reads.""" + +from collections.abc import Callable +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path +from typing import Any + + +def process_in_chunks( + paths: list[Path], fn: Callable[[Path], Any], workers: int = 8, chunk: int = 1000 +) -> list[Any]: + """Process files in parallel chunks; one failed item never kills the run.""" + results: list[Any] = [] + total = len(paths) + with ProcessPoolExecutor(max_workers=workers) as executor: + for start in range(0, total, chunk): + futures = {executor.submit(fn, p): p for p in paths[start : start + chunk]} + for future in as_completed(futures): + try: + results.append(future.result(timeout=300)) + except Exception: + logger.exception("Failed to process: {}", futures[future]) + done = min(start + chunk, total) + logger.info("Progress: {}/{} ({:.1f}%)", done, total, done / total * 100) + logger.info("Processed {}/{} files successfully", len(results), total) + return results +``` + +## Streaming Reads + +For datasets larger than RAM, never call `pl.read_parquet`. Scan lazily and pull +fixed-size slices: `reader = pl.scan_parquet(path)`, then +`reader.slice(offset, batch).collect()` per batch, with the row count from +`reader.select(pl.len()).collect().item()`. diff --git a/skills/data-pipelines/references/pipeline-architecture.md b/skills/data-pipelines/references/pipeline-architecture.md new file mode 100644 index 0000000..51c19a3 --- /dev/null +++ b/skills/data-pipelines/references/pipeline-architecture.md @@ -0,0 +1,136 @@ +# Pipeline Architecture and ETL + +Sizing the pipeline, configuring it with Pydantic, and building the Parquet dataset manifest that everything downstream reads. + +## Contents + +- [Pipeline Architecture](#pipeline-architecture) +- [PipelineConfig](#pipelineconfig) +- [ETL: Building a Dataset Manifest](#etl-building-a-dataset-manifest) +- [Why Hashes, and Why No `split` Column](#why-hashes-and-why-no-split-column) +- [Dependencies](#dependencies) + +## Pipeline Architecture + +``` +Building a data pipeline? +├── Small (< 10 GB) ............ Polars / Pandas +├── Medium (10–500 GB) ......... Polars / DuckDB + Parquet +├── Large (500 GB – 10 TB) ..... Arrow / Spark + cloud object storage +├── Streaming / continuous ..... Kafka / Flink + Delta Lake +└── Annotation pipeline ........ Label Studio / CVAT + validation hooks +``` + +A pipeline is a fixed sequence of named stages — `extract → validate_raw → +transform → validate_transformed → load` — with a real gate between each. +Configure it with Pydantic so bad parameters fail at load time, not three hours in. + +## PipelineConfig + +```python +"""Data pipeline configuration.""" + +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, Field, ValidationInfo, field_validator + + +class PipelineConfig(BaseModel): + """Top-level data pipeline configuration.""" + + name: str = Field(min_length=1) + source_dir: Path + output_dir: Path + storage_format: Literal["parquet", "arrow", "lmdb", "tfrecord", "webdataset"] = "parquet" + num_workers: int = Field(ge=1, le=64, default=8) + schema_version: str = Field(pattern=r"^\d+\.\d+\.\d+$", default="1.0.0") + + @field_validator("output_dir") + @classmethod + def output_must_differ_from_source(cls, v: Path, info: ValidationInfo) -> Path: + """Prevent writing transformed data over the raw inputs.""" + if v == info.data.get("source_dir"): + msg = "output_dir must differ from source_dir" + raise ValueError(msg) + return v +``` + +The anti-pattern this replaces: a bare `process_data(input_path, output_path)` that +globs `*.jpg` and rewrites images with no schema, no logging, and no guard against +clobbering the raw inputs. + +## ETL: Building a Dataset Manifest + +The manifest is the source of truth: one validated row per sample, carrying a +content hash for deduplication and integrity, written as Parquet (never CSV). + +```python +"""ETL for image datasets: hash, validate, write a Parquet manifest.""" + +import hashlib +from pathlib import Path + +import polars as pl +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ImageRecord(BaseModel): + """Validated image metadata record.""" + + file_hash: str = Field(min_length=64, max_length=64) # SHA-256 + relative_path: str + width: int = Field(ge=1) + height: int = Field(ge=1) + channels: int = Field(ge=1, le=4) + label: str + group_id: str | None = None # patient / video / scene identifier + file_size_bytes: int = Field(ge=1) + + +def compute_file_hash(path: Path) -> str: + """SHA-256 of file contents, for content-addressable storage.""" + hasher = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(8192), b""): + hasher.update(chunk) + return hasher.hexdigest() + + +def build_manifest(paths: list[Path], labels: dict[Path, str], out: Path) -> pl.DataFrame: + """Read image metadata into validated records and write them as Parquet.""" + records = [] + for path in paths: + with Image.open(path) as img: + width, height, channels = *img.size, len(img.getbands()) + records.append( + ImageRecord( + file_hash=compute_file_hash(path), + relative_path=str(path), + width=width, + height=height, + channels=channels, + label=labels[path], + file_size_bytes=path.stat().st_size, + ).model_dump() + ) + df = pl.DataFrame(records) + df.write_parquet(out, compression="zstd") + logger.info("Manifest written: {} records to {}", len(df), out) + return df +``` + +## Why Hashes, and Why No `split` Column + +Hashes deduplicate byte-identical images that would otherwise straddle splits and +detect corruption between runs. `split` is deliberately *not* a manifest field: it +is assigned downstream, so a dataset can be re-split without re-extracting. + +## Dependencies + +```bash +pixi add polars pillow lmdb +pixi add --pypi great-expectations albumentations webdataset +``` diff --git a/skills/data-pipelines/references/schema-evolution.md b/skills/data-pipelines/references/schema-evolution.md new file mode 100644 index 0000000..9626fde --- /dev/null +++ b/skills/data-pipelines/references/schema-evolution.md @@ -0,0 +1,56 @@ +# Schema Evolution and Migration + +Versioning dataset schemas as explicit Pydantic models with a registered migration chain. + +## Principle + +Never mutate a dataset schema in place. Declare each version as its own model, +register it, and provide an explicit migration function per hop. + +## Versioned Schemas + +```python +"""Versioned dataset schemas with an explicit migration chain.""" + +from pydantic import BaseModel, Field + + +class SchemaV1(BaseModel): + """Original — path and label only.""" + image_path: str + label: str + + +class SchemaV2(SchemaV1): + """V2 — adds dimensions and content hash.""" + width: int = Field(ge=1) + height: int = Field(ge=1) + file_hash: str + + +class SchemaV3(SchemaV2): + """V3 — adds split assignment and quality score.""" + split: str = Field(pattern=r"^(train|val|test)$") + quality_score: float = Field(ge=0.0, le=1.0, default=1.0) + + +def migrate_v2_to_v3(record: SchemaV2, split: str, quality_score: float = 1.0) -> SchemaV3: + """Enrich a V2 record with its split assignment and quality score.""" + return SchemaV3(**record.model_dump(), split=split, quality_score=quality_score) + + +# Ordered registry: reading version N and targeting M applies hops N..M in order. +SCHEMA_REGISTRY: dict[str, type[BaseModel]] = { + "1.0.0": SchemaV1, + "2.0.0": SchemaV2, + "3.0.0": SchemaV3, +} +MIGRATIONS = {("2.0.0", "3.0.0"): migrate_v2_to_v3} +``` + +## When Not to Subclass + +Subclassing keeps additive versions short, but write a standalone model whenever a +field changes meaning or type — inheritance must never hide a breaking change. +Store `schema_version` alongside the data (Parquet key-value metadata or a sidecar +`dataset.json`) so readers dispatch to the right model instead of guessing. diff --git a/skills/data-pipelines/references/storage-formats.md b/skills/data-pipelines/references/storage-formats.md new file mode 100644 index 0000000..8dfd2df --- /dev/null +++ b/skills/data-pipelines/references/storage-formats.md @@ -0,0 +1,52 @@ +# Storage Format Selection + +Choosing between Parquet, LMDB, WebDataset, TFRecord, Arrow, and raw folders for CV/ML data. + +## Decision Tree + +``` +Choosing a storage format? +├── Tabular metadata (labels, splits, paths, hashes) +│ └── Parquet ....... columnar, compressed, fast predicate filtering (Polars/DuckDB) +├── Streaming large image/video datasets +│ ├── WebDataset .... .tar shards; distributed/multi-node, S3-friendly, no random access +│ └── TFRecord ...... TensorFlow ecosystem, sequential reads +├── Random-access image datasets (single node, reshuffled every epoch) +│ └── LMDB .......... memory-mapped zero-copy reads, single-writer, bad over NFS +├── In-memory analytics / cross-language interchange +│ └── Arrow IPC ..... zero-copy, language-agnostic +└── Small datasets (< 1 GB) or active annotation churn + └── Raw folders + Parquet manifest ..... simplest, debuggable, diffable +``` + +## Rules of Thumb + +Raw files until random reads become the bottleneck; LMDB for +single-node random access; WebDataset once training spans nodes or lives in object +storage; Parquet for everything tabular, always. Write manifests with Hive-style +partitions (`split=train/data.parquet`, `compression="zstd"`, +`row_group_size=10_000`) so readers can skip whole splits. + +Never use CSV for large datasets. + +## LMDB Packing + +```python +"""LMDB packing for fast random-access image reads.""" + +from pathlib import Path + +import lmdb +from loguru import logger + + +def build_lmdb_dataset(image_paths: list[Path], out_path: Path, map_gb: int = 50) -> None: + """Pack encoded image bytes into LMDB keyed by zero-padded index.""" + env = lmdb.open(str(out_path), map_size=map_gb * 1024**3) + with env.begin(write=True) as txn: + for idx, img_path in enumerate(image_paths): + txn.put(f"{idx:08d}".encode(), img_path.read_bytes()) + txn.put(b"__len__", str(len(image_paths)).encode()) + env.close() + logger.info("Built LMDB: {} images at {}", len(image_paths), out_path) +``` diff --git a/skills/fastapi/SKILL.md b/skills/fastapi/SKILL.md index 28c8442..b67e827 100644 --- a/skills/fastapi/SKILL.md +++ b/skills/fastapi/SKILL.md @@ -11,84 +11,15 @@ description: > # FastAPI Skill -Build FastAPI applications for serving ML models and CV pipelines. Use Pydantic models for all request/response schemas — never accept raw dicts. Use an application factory for testable configuration and clean startup/shutdown lifecycle. +Build FastAPI applications for serving ML models and CV pipelines. Use Pydantic models for +all request/response schemas — never accept raw dicts. Load models once in the lifespan +handler and inject them into endpoints with `Depends`; use an application factory so the +app is configurable and testable. -## Application Factory +## Essential Core: A Typed Prediction Endpoint -```python -from __future__ import annotations - -from contextlib import asynccontextmanager -from collections.abc import AsyncGenerator -from typing import Any - -from fastapi import FastAPI -from fastapi.middleware.cors import CORSMiddleware -from loguru import logger -from pydantic import BaseModel, Field - - -class AppConfig(BaseModel, frozen=True): - title: str = "ML Model API" - version: str = "1.0.0" - cors_origins: list[str] = Field(default_factory=lambda: ["*"]) - model_path: str = "models/best.onnx" - max_batch_size: int = 32 - device: str = "cuda:0" - - -class ModelRegistry: - """Holds loaded models for the application lifetime.""" - def __init__(self) -> None: - self.models: dict[str, Any] = {} - - async def load(self, config: AppConfig) -> None: - logger.info("Loading model from {}", config.model_path) - self.models["default"] = await _load_model(config.model_path) - - async def shutdown(self) -> None: - self.models.clear() - - -model_registry = ModelRegistry() - - -@asynccontextmanager -async def lifespan(app: FastAPI) -> AsyncGenerator[None]: - """Manage model loading on startup and cleanup on shutdown.""" - config = AppConfig() - await model_registry.load(config) - yield - await model_registry.shutdown() - - -def create_app(config: AppConfig | None = None) -> FastAPI: - """Create and configure the FastAPI application.""" - config = config or AppConfig() - - app = FastAPI( - title=config.title, - version=config.version, - lifespan=lifespan, - ) - - app.add_middleware( - CORSMiddleware, - allow_origins=config.cors_origins, - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - from .routes import prediction, health - app.include_router(health.router, tags=["health"]) - app.include_router(prediction.router, prefix="/api/v1", tags=["prediction"]) - - return app -``` - -## Request and Response Models - -Define explicit Pydantic models for every endpoint. Never use `dict` or `Any` in API signatures. +Define explicit Pydantic models for the request and response, then inject the model and +preprocessor. This is the shape of nearly every endpoint in a serving API. ```python from __future__ import annotations @@ -125,18 +56,8 @@ class PredictionResponse(BaseModel, frozen=True): detections: list[Detection] inference_time_ms: float model_version: str - - -class ErrorResponse(BaseModel, frozen=True): - error: str - detail: str | None = None - request_id: str | None = None ``` -## Prediction Endpoint with Dependency Injection - -Inject the model and preprocessor via `Depends` — never load them inside the handler. For batch endpoints, accept a `list[PredictionRequest]` (cap with `Field(max_length=...)`) and loop this same logic. - ```python from __future__ import annotations @@ -178,276 +99,23 @@ async def predict( ) ``` -### Health Check Endpoints - -```python -from __future__ import annotations - -from fastapi import APIRouter -from pydantic import BaseModel - -router = APIRouter() - - -class HealthResponse(BaseModel, frozen=True): - status: str - model_loaded: bool - version: str - - -@router.get("/health", response_model=HealthResponse) -async def health_check() -> HealthResponse: - """Liveness probe.""" - from .app import model_registry - - return HealthResponse( - status="healthy", - model_loaded=bool(model_registry.models), - version="1.0.0", - ) - - -@router.get("/ready") -async def readiness() -> dict[str, bool]: - """Kubernetes readiness probe — 503 until the model is loaded.""" - from .app import model_registry - - if not model_registry.models: - raise HTTPException(status_code=503, detail="Model not loaded") - return {"ready": True} -``` - -## Dependency Injection - -```python -from __future__ import annotations - -from functools import lru_cache - -from fastapi import Depends - - -@lru_cache(maxsize=1) -def get_app_config() -> AppConfig: - """Load application configuration once.""" - return AppConfig() - - -async def get_model( - config: AppConfig = Depends(get_app_config), -) -> Model: - """Provide the loaded model instance.""" - from .app import model_registry - - model = model_registry.models.get("default") - if model is None: - raise HTTPException(status_code=503, detail="Model not available") - return model - - -async def get_preprocessor( - config: AppConfig = Depends(get_app_config), -) -> Preprocessor: - """Provide the image preprocessor.""" - return Preprocessor(target_size=(640, 640), device=config.device) -``` - -## Middleware and Error Handling - -### Request ID and Logging Middleware - -```python -from __future__ import annotations - -import time -import uuid - -from fastapi import Request, Response -from loguru import logger -from starlette.middleware.base import BaseHTTPMiddleware - - -class RequestLoggingMiddleware(BaseHTTPMiddleware): - """Log all requests with timing and request ID.""" - - async def dispatch(self, request: Request, call_next) -> Response: - request_id = str(uuid.uuid4())[:8] - request.state.request_id = request_id - - start = time.perf_counter() - response = await call_next(request) - elapsed = (time.perf_counter() - start) * 1000 - - logger.info( - "{} {} → {} ({:.1f}ms) [{}]", - request.method, request.url.path, response.status_code, elapsed, request_id, - ) - response.headers["X-Request-ID"] = request_id - return response -``` - -### Structured Exception Handlers - -```python -from __future__ import annotations - -from fastapi import FastAPI, Request -from fastapi.responses import JSONResponse -from loguru import logger - - -async def model_error_handler(request: Request, exc: ModelInferenceError) -> JSONResponse: - logger.error("Inference error: {}", exc) - return JSONResponse( - status_code=500, - content={ - "error": "inference_error", - "detail": str(exc), - "request_id": getattr(request.state, "request_id", None), - }, - ) - - -def register_exception_handlers(app: FastAPI) -> None: - app.add_exception_handler(ModelInferenceError, model_error_handler) -``` - -## WebSocket Streaming (Real-Time Video Inference) - -```python -import base64 - -from fastapi import APIRouter, WebSocket, WebSocketDisconnect -from loguru import logger - -router = APIRouter() - - -@router.websocket("/ws/stream") -async def stream_inference(websocket: WebSocket) -> None: - """Stream video frames and return detections in real time.""" - await websocket.accept() - logger.info("WebSocket client connected") - - try: - while True: - data = await websocket.receive_json() - frame_b64 = data.get("frame") - if not frame_b64: - await websocket.send_json({"error": "Missing 'frame' field"}) - continue - - frame_bytes = base64.b64decode(frame_b64) - detections = await run_inference(frame_bytes) - - await websocket.send_json({ - "detections": [d.model_dump() for d in detections], - "frame_id": data.get("frame_id"), - }) - except WebSocketDisconnect: - logger.info("WebSocket client disconnected") -``` - -## Background Tasks (Async Post-Processing) - -Use `BackgroundTasks` to persist results or log after the response is sent, keeping request latency low. - -```python -from fastapi import BackgroundTasks - - -async def save_prediction_to_db(request_id: str, detections: list[Detection]) -> None: - await db.predictions.insert_one( - {"request_id": request_id, "detections": [d.model_dump() for d in detections]} - ) - - -@router.post("/predict") -async def predict_with_logging( - request: PredictionRequest, - background_tasks: BackgroundTasks, -) -> PredictionResponse: - result = await run_prediction(request) - background_tasks.add_task(save_prediction_to_db, request.state.request_id, result.detections) - return result -``` - -## Testing (Async Test Client) - -```python -from __future__ import annotations - -import base64 - -import pytest -from httpx import ASGITransport, AsyncClient - -from app.main import create_app - - -@pytest.fixture -async def client() -> AsyncClient: - """Create async test client.""" - app = create_app() - transport = ASGITransport(app=app) - async with AsyncClient(transport=transport, base_url="http://test") as ac: - yield ac - - -@pytest.mark.anyio -async def test_health_check(client: AsyncClient) -> None: - response = await client.get("/health") - assert response.status_code == 200 - data = response.json() - assert data["status"] == "healthy" - - -@pytest.mark.anyio -async def test_predict_returns_detections(client: AsyncClient) -> None: - image_bytes = b"\x89PNG\r\n\x1a\n" + b"\x00" * 100 - request_body = { - "image_b64": base64.b64encode(image_bytes).decode(), - "confidence_threshold": 0.5, - } - response = await client.post("/api/v1/predict", json=request_body) - assert response.status_code == 200 - data = response.json() - assert "detections" in data - assert "inference_time_ms" in data - - -@pytest.mark.anyio -async def test_predict_rejects_invalid_base64(client: AsyncClient) -> None: - response = await client.post( - "/api/v1/predict", - json={"image_b64": "not-valid-base64!!!"}, - ) - assert response.status_code == 422 -``` - -## Docker Deployment - -Production Dockerfile plus a Uvicorn runner for programmatic startup. - -```dockerfile -FROM python:3.11-slim AS base - -WORKDIR /app -RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* -RUN curl -fsSL https://pixi.sh/install.sh | bash -ENV PATH="/root/.pixi/bin:${PATH}" - -COPY pixi.toml pixi.lock ./ -RUN pixi install - -COPY src/ ./src/ - -EXPOSE 8000 - -CMD ["pixi", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] -``` - -For programmatic startup with the factory, use `uvicorn.run("app.main:create_app", factory=True, host="0.0.0.0", port=8000, workers=4, limit_concurrency=100, timeout_keep_alive=30)`. +## Conventions + +- **Application factory.** `create_app(config) -> FastAPI` with a frozen `AppConfig`; start it + with `uvicorn.run("app.main:create_app", factory=True, ...)`. +- **Lifespan owns model loading.** An `@asynccontextmanager` lifespan loads into a + `ModelRegistry` at startup and clears it at shutdown. +- **Everything through `Depends`.** Config providers use `@lru_cache(maxsize=1)`; tests swap + implementations with `app.dependency_overrides`. +- **`Annotated[T, Depends(...)]`** in handler signatures so MyPy and OpenAPI both see the type. +- **Declare `response_model`** on every route; return frozen Pydantic models, never dicts. +- **Structured errors.** One `ErrorResponse` shape (`error`, `detail`, `request_id`) for all + failures, produced by registered exception handlers. +- **Request IDs.** Middleware assigns one, stores it on `request.state`, and echoes it in + `X-Request-ID`. +- **Two health routes.** `/health` for liveness (cheap, model-independent), `/ready` for + readiness (503 until the model is loaded). +- **Loguru brace formatting** in middleware and handlers, not f-strings. ## Anti-Patterns @@ -457,7 +125,19 @@ For programmatic startup with the factory, use `uvicorn.run("app.main:create_app - **Never hardcode CORS origins in production** — load from configuration. - **Never return raw numpy arrays or tensors** — serialize to lists or base64 in the response model. - **Never skip input validation** — use Pydantic field validators for base64, image dimensions, and parameter bounds. +- **Never rely on background tasks for work that must not be lost** — they die with the process; use a real queue. ## Integration with Other Skills Pydantic (frozen models), Docker CV (multi-stage serving images), ONNX/TensorRT (load optimized models in the lifespan handler), Loguru (middleware/handler logging), Testing (httpx async client). + +## Deep dives + +- `references/application-factory-and-lifespan.md` — read when setting up the app object, `AppConfig`, `ModelRegistry`, or startup/shutdown model loading. +- `references/request-response-models.md` — read when designing schemas, adding validators, capping batch requests, or shaping error payloads. +- `references/dependency-injection.md` — read when wiring the model/preprocessor/config into endpoints or overriding dependencies in tests. +- `references/middleware-and-cors.md` — read when configuring CORS or adding request-ID/timing/logging middleware. +- `references/health-and-errors.md` — read when adding liveness/readiness probes or mapping domain exceptions to JSON error responses. +- `references/background-tasks.md` — read when deferring persistence or logging until after the response is sent. +- `references/websocket-streaming.md` — read when adding WebSocket or streaming responses for real-time video inference. +- `references/testing-and-deployment.md` — read when writing async httpx tests, building the Docker image, or tuning Uvicorn workers. diff --git a/skills/fastapi/references/application-factory-and-lifespan.md b/skills/fastapi/references/application-factory-and-lifespan.md new file mode 100644 index 0000000..5862705 --- /dev/null +++ b/skills/fastapi/references/application-factory-and-lifespan.md @@ -0,0 +1,87 @@ +# Application Factory and Lifespan + +How to build a testable FastAPI app object: a frozen config model, a model registry, an async lifespan handler for startup/shutdown, and a `create_app()` factory that wires middleware and routers. + +## Why a Factory + +A module-level `app = FastAPI()` is hard to test — you cannot vary configuration per test. A `create_app(config)` factory lets tests build a fresh app with test settings, and lets Uvicorn start it with `factory=True`. + +## Full Factory + +```python +from __future__ import annotations + +from contextlib import asynccontextmanager +from collections.abc import AsyncGenerator +from typing import Any + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from loguru import logger +from pydantic import BaseModel, Field + + +class AppConfig(BaseModel, frozen=True): + title: str = "ML Model API" + version: str = "1.0.0" + cors_origins: list[str] = Field(default_factory=lambda: ["*"]) + model_path: str = "models/best.onnx" + max_batch_size: int = 32 + device: str = "cuda:0" + + +class ModelRegistry: + """Holds loaded models for the application lifetime.""" + def __init__(self) -> None: + self.models: dict[str, Any] = {} + + async def load(self, config: AppConfig) -> None: + logger.info("Loading model from {}", config.model_path) + self.models["default"] = await _load_model(config.model_path) + + async def shutdown(self) -> None: + self.models.clear() + + +model_registry = ModelRegistry() + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncGenerator[None]: + """Manage model loading on startup and cleanup on shutdown.""" + config = AppConfig() + await model_registry.load(config) + yield + await model_registry.shutdown() + + +def create_app(config: AppConfig | None = None) -> FastAPI: + """Create and configure the FastAPI application.""" + config = config or AppConfig() + + app = FastAPI( + title=config.title, + version=config.version, + lifespan=lifespan, + ) + + app.add_middleware( + CORSMiddleware, + allow_origins=config.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + from .routes import prediction, health + app.include_router(health.router, tags=["health"]) + app.include_router(prediction.router, prefix="/api/v1", tags=["prediction"]) + + return app +``` + +## Notes + +- The lifespan context manager is the **only** place a model should be loaded. Loading inside an endpoint blocks the first request and reloads per worker request. +- Everything before `yield` runs at startup; everything after runs at shutdown. +- Routers are imported inside `create_app` to avoid import cycles between the app module and route modules that depend on the registry. +- `AppConfig` is frozen so configuration cannot drift at runtime. Load real values from environment variables or a settings model rather than hardcoding CORS origins. diff --git a/skills/fastapi/references/background-tasks.md b/skills/fastapi/references/background-tasks.md new file mode 100644 index 0000000..bdd6477 --- /dev/null +++ b/skills/fastapi/references/background-tasks.md @@ -0,0 +1,37 @@ +# Background Tasks + +Deferring post-response work — persistence, audit logging, cache warming — so it does not inflate request latency. + +## Async Post-Processing + +Use `BackgroundTasks` to persist results or log after the response is sent, keeping request latency low. + +```python +from fastapi import BackgroundTasks + + +async def save_prediction_to_db(request_id: str, detections: list[Detection]) -> None: + await db.predictions.insert_one( + {"request_id": request_id, "detections": [d.model_dump() for d in detections]} + ) + + +@router.post("/predict") +async def predict_with_logging( + request: PredictionRequest, + background_tasks: BackgroundTasks, +) -> PredictionResponse: + result = await run_prediction(request) + background_tasks.add_task(save_prediction_to_db, request.state.request_id, result.detections) + return result +``` + +## Notes + +- Declare `background_tasks: BackgroundTasks` as a parameter; FastAPI injects it. Tasks run **after** the response is flushed to the client. +- `add_task` takes the callable plus its arguments — do not call it (`add_task(fn, arg)`, not `add_task(fn(arg))`). +- Both sync and async callables are accepted. Sync callables run in a threadpool; async ones run on the event loop, so a blocking call inside an `async def` task still stalls the server. +- **Failures are invisible to the client.** The response has already been sent, so a task exception only surfaces in logs. Wrap task bodies in try/except and log with Loguru. +- **Do not use this for work that must not be lost.** Background tasks die with the process. Anything requiring delivery guarantees (retraining triggers, billing events) belongs in a real queue — Celery, RQ, or SQS. +- **Do not use it for long jobs.** The worker is occupied until the task finishes, reducing concurrency. Keep tasks to short I/O like a database insert or a metrics push. +- Pass the request id through so background log lines correlate with the request that produced them. diff --git a/skills/fastapi/references/dependency-injection.md b/skills/fastapi/references/dependency-injection.md new file mode 100644 index 0000000..9771ab3 --- /dev/null +++ b/skills/fastapi/references/dependency-injection.md @@ -0,0 +1,65 @@ +# Dependency Injection + +How to provide the model, preprocessor, and configuration to endpoints via `Depends` instead of loading them inside handlers. + +## Dependencies Module + +```python +from __future__ import annotations + +from functools import lru_cache + +from fastapi import Depends + + +@lru_cache(maxsize=1) +def get_app_config() -> AppConfig: + """Load application configuration once.""" + return AppConfig() + + +async def get_model( + config: AppConfig = Depends(get_app_config), +) -> Model: + """Provide the loaded model instance.""" + from .app import model_registry + + model = model_registry.models.get("default") + if model is None: + raise HTTPException(status_code=503, detail="Model not available") + return model + + +async def get_preprocessor( + config: AppConfig = Depends(get_app_config), +) -> Preprocessor: + """Provide the image preprocessor.""" + return Preprocessor(target_size=(640, 640), device=config.device) +``` + +## Consuming Dependencies + +Use `Annotated[T, Depends(provider)]` in the handler signature — this keeps the type visible to MyPy and to FastAPI's OpenAPI generation: + +```python +from typing import Annotated + +from fastapi import Depends + + +@router.post("/predict", response_model=PredictionResponse) +async def predict( + request: PredictionRequest, + model: Annotated[Model, Depends(get_model)], + preprocessor: Annotated[Preprocessor, Depends(get_preprocessor)], +) -> PredictionResponse: + ... +``` + +## Rules + +- **`@lru_cache(maxsize=1)` on config providers.** Configuration is read once per process; without the cache, every request re-parses environment variables. +- **Model providers read from the registry, never load.** `get_model` looks up the object that the lifespan handler already loaded. If it is missing, raise `HTTPException(503)` — that is a genuine "not ready" condition, not a server bug. +- **Dependencies compose.** `get_preprocessor` depends on `get_app_config`; FastAPI resolves the graph and caches each provider's result for the duration of a single request. +- **Override in tests.** `app.dependency_overrides[get_model] = lambda: FakeModel()` swaps in a stub without touching the production code path — this is the main reason to route everything through `Depends`. +- **Keep providers cheap.** Anything expensive (GPU allocation, file reads, network calls) belongs in the lifespan handler, with the provider returning the already-built object. diff --git a/skills/fastapi/references/health-and-errors.md b/skills/fastapi/references/health-and-errors.md new file mode 100644 index 0000000..2ad61ad --- /dev/null +++ b/skills/fastapi/references/health-and-errors.md @@ -0,0 +1,83 @@ +# Health Checks and Structured Error Handling + +Liveness/readiness endpoints for orchestrators, and exception handlers that turn domain errors into a consistent JSON error shape. + +## Health Check Endpoints + +```python +from __future__ import annotations + +from fastapi import APIRouter +from pydantic import BaseModel + +router = APIRouter() + + +class HealthResponse(BaseModel, frozen=True): + status: str + model_loaded: bool + version: str + + +@router.get("/health", response_model=HealthResponse) +async def health_check() -> HealthResponse: + """Liveness probe.""" + from .app import model_registry + + return HealthResponse( + status="healthy", + model_loaded=bool(model_registry.models), + version="1.0.0", + ) + + +@router.get("/ready") +async def readiness() -> dict[str, bool]: + """Kubernetes readiness probe — 503 until the model is loaded.""" + from .app import model_registry + + if not model_registry.models: + raise HTTPException(status_code=503, detail="Model not loaded") + return {"ready": True} +``` + +### Liveness vs Readiness + +- **`/health` (liveness)** answers "is the process alive?" — it must stay cheap and must not depend on the model. If it fails, the orchestrator restarts the container. +- **`/ready` (readiness)** answers "can this replica serve traffic?" — it returns 503 while the model is still loading so the load balancer withholds traffic instead of returning errors. Model load can take minutes; pair this with a generous startup probe. + +Neither endpoint should run inference or touch external services. + +## Structured Exception Handlers + +```python +from __future__ import annotations + +from fastapi import FastAPI, Request +from fastapi.responses import JSONResponse +from loguru import logger + + +async def model_error_handler(request: Request, exc: ModelInferenceError) -> JSONResponse: + logger.error("Inference error: {}", exc) + return JSONResponse( + status_code=500, + content={ + "error": "inference_error", + "detail": str(exc), + "request_id": getattr(request.state, "request_id", None), + }, + ) + + +def register_exception_handlers(app: FastAPI) -> None: + app.add_exception_handler(ModelInferenceError, model_error_handler) +``` + +## Notes + +- The handler body mirrors the `ErrorResponse` schema (`error`, `detail`, `request_id`) so every failure path returns the same shape clients already parse. +- `request.state.request_id` is set by the logging middleware; `getattr(..., None)` keeps the handler safe if the middleware is not installed. +- Register one handler per domain exception type (`ModelInferenceError`, `PreprocessingError`, ...) rather than catching broad exceptions inside endpoints. Handlers keep the happy path in the endpoint clean. +- Log at `error` level with the exception, but return only a safe message to the client — never leak stack traces or file paths in the response body. +- Call `register_exception_handlers(app)` from `create_app` after routers are included. diff --git a/skills/fastapi/references/middleware-and-cors.md b/skills/fastapi/references/middleware-and-cors.md new file mode 100644 index 0000000..eac776c --- /dev/null +++ b/skills/fastapi/references/middleware-and-cors.md @@ -0,0 +1,60 @@ +# Middleware and CORS + +Cross-cutting request handling: CORS configuration and a request-ID + timing logging middleware. + +## CORS + +Register CORS in the application factory, driven by configuration — never a hardcoded origin list in production code: + +```python +from fastapi.middleware.cors import CORSMiddleware + +app.add_middleware( + CORSMiddleware, + allow_origins=config.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) +``` + +`allow_origins=["*"]` is acceptable for local development only. In production, load an explicit origin list from configuration; `allow_credentials=True` combined with a wildcard origin is rejected by browsers anyway. + +## Request ID and Logging Middleware + +```python +from __future__ import annotations + +import time +import uuid + +from fastapi import Request, Response +from loguru import logger +from starlette.middleware.base import BaseHTTPMiddleware + + +class RequestLoggingMiddleware(BaseHTTPMiddleware): + """Log all requests with timing and request ID.""" + + async def dispatch(self, request: Request, call_next) -> Response: + request_id = str(uuid.uuid4())[:8] + request.state.request_id = request_id + + start = time.perf_counter() + response = await call_next(request) + elapsed = (time.perf_counter() - start) * 1000 + + logger.info( + "{} {} → {} ({:.1f}ms) [{}]", + request.method, request.url.path, response.status_code, elapsed, request_id, + ) + response.headers["X-Request-ID"] = request_id + return response +``` + +## Notes + +- The middleware stashes `request_id` on `request.state`, which exception handlers and background tasks read back to correlate logs with responses. The same id is echoed to the client in the `X-Request-ID` header. +- Middleware runs in reverse registration order on the way out. Register the logging middleware early so it wraps (and therefore times) everything else. +- Keep middleware non-blocking. Anything CPU-bound here inflates latency for every route; push that work into a background task instead. +- Use Loguru's brace-style formatting (`"{} {}"`, args) rather than f-strings so the log record keeps its structured fields. diff --git a/skills/fastapi/references/request-response-models.md b/skills/fastapi/references/request-response-models.md new file mode 100644 index 0000000..ad956fe --- /dev/null +++ b/skills/fastapi/references/request-response-models.md @@ -0,0 +1,75 @@ +# Request and Response Models + +Every FastAPI endpoint in an ML service takes and returns an explicit Pydantic model — never `dict`, never `Any`. + +## Full Schema Module + +```python +from __future__ import annotations + +import base64 + +from pydantic import BaseModel, Field, field_validator + + +class PredictionRequest(BaseModel, frozen=True): + """Single image prediction request.""" + + image_b64: str = Field(..., description="Base64-encoded image bytes") + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) + max_detections: int = Field(default=100, ge=1, le=1000) + + @field_validator("image_b64") + @classmethod + def validate_base64(cls, v: str) -> str: + try: + base64.b64decode(v, validate=True) + except Exception as exc: + raise ValueError("Invalid base64 encoding") from exc + return v + + +class Detection(BaseModel, frozen=True): + label: str + confidence: float = Field(ge=0.0, le=1.0) + bbox: list[float] = Field(min_length=4, max_length=4, description="[x1, y1, x2, y2]") + + +class PredictionResponse(BaseModel, frozen=True): + detections: list[Detection] + inference_time_ms: float + model_version: str + + +class ErrorResponse(BaseModel, frozen=True): + error: str + detail: str | None = None + request_id: str | None = None +``` + +## Design Rules + +- **Frozen models everywhere.** Request and response payloads are values, not mutable state. +- **Validate at the boundary.** Base64 decoding, image dimension bounds, and threshold ranges belong in `field_validator` / `Field` constraints — not in the handler body. FastAPI converts a `ValueError` in a validator into a 422 with a structured error body for free. +- **Bound every list.** `Field(min_length=..., max_length=...)` on `bbox`, and on any batch request list, prevents unbounded payloads. +- **Never return raw tensors or numpy arrays.** Serialize to Python lists (as `Detection.bbox` does) or base64 strings. +- **Declare `response_model`** on the route decorator so FastAPI both validates the outgoing payload and publishes an accurate OpenAPI schema. + +## Batch Endpoints + +For a batch endpoint, accept a `list[PredictionRequest]` capped with `Field(max_length=...)` on a wrapper model, and loop the same single-item logic: + +```python +class BatchPredictionRequest(BaseModel, frozen=True): + items: list[PredictionRequest] = Field(min_length=1, max_length=32) + + +class BatchPredictionResponse(BaseModel, frozen=True): + results: list[PredictionResponse] +``` + +Cap `max_length` at the model's real batch capacity (`AppConfig.max_batch_size`) so an oversized request fails validation instead of exhausting GPU memory. + +## Error Payloads + +`ErrorResponse` is the single shape every failure returns. Register exception handlers that produce it (see the health-and-errors reference) so clients never have to parse two different error formats. diff --git a/skills/fastapi/references/testing-and-deployment.md b/skills/fastapi/references/testing-and-deployment.md new file mode 100644 index 0000000..27e37b5 --- /dev/null +++ b/skills/fastapi/references/testing-and-deployment.md @@ -0,0 +1,111 @@ +# Testing and Deployment + +Async test client patterns for a model-serving API, plus the production Dockerfile and Uvicorn runner. + +## Contents + +- [Testing (Async Test Client)](#testing-async-test-client) +- [Docker Deployment](#docker-deployment) +- [Programmatic Uvicorn Startup](#programmatic-uvicorn-startup) + +## Testing (Async Test Client) + +```python +from __future__ import annotations + +import base64 + +import pytest +from httpx import ASGITransport, AsyncClient + +from app.main import create_app + + +@pytest.fixture +async def client() -> AsyncClient: + """Create async test client.""" + app = create_app() + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + yield ac + + +@pytest.mark.anyio +async def test_health_check(client: AsyncClient) -> None: + response = await client.get("/health") + assert response.status_code == 200 + data = response.json() + assert data["status"] == "healthy" + + +@pytest.mark.anyio +async def test_predict_returns_detections(client: AsyncClient) -> None: + image_bytes = b"\x89PNG\r\n\x1a\n" + b"\x00" * 100 + request_body = { + "image_b64": base64.b64encode(image_bytes).decode(), + "confidence_threshold": 0.5, + } + response = await client.post("/api/v1/predict", json=request_body) + assert response.status_code == 200 + data = response.json() + assert "detections" in data + assert "inference_time_ms" in data + + +@pytest.mark.anyio +async def test_predict_rejects_invalid_base64(client: AsyncClient) -> None: + response = await client.post( + "/api/v1/predict", + json={"image_b64": "not-valid-base64!!!"}, + ) + assert response.status_code == 422 +``` + +Notes: + +- `ASGITransport` drives the app in-process — no network, no server to start. +- The factory is what makes this work: each test builds its own app instance. +- Test the rejection paths (422 on bad base64, 503 before the model loads) as deliberately as the happy path — validation is a feature of the API contract. +- Swap the real model with `app.dependency_overrides[get_model] = lambda: FakeModel()` to keep tests fast and GPU-free. + +## Docker Deployment + +```dockerfile +FROM python:3.11-slim AS base + +WORKDIR /app +RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" + +COPY pixi.toml pixi.lock ./ +RUN pixi install + +COPY src/ ./src/ + +EXPOSE 8000 + +CMD ["pixi", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"] +``` + +Dependency files are copied before the source so that the `pixi install` layer stays cached across source-only changes. + +## Programmatic Uvicorn Startup + +For programmatic startup with the factory, use: + +```python +uvicorn.run( + "app.main:create_app", + factory=True, + host="0.0.0.0", + port=8000, + workers=4, + limit_concurrency=100, + timeout_keep_alive=30, +) +``` + +- `factory=True` tells Uvicorn the target is a callable returning an app, not an app object. +- Each worker is a separate process and therefore loads its own copy of the model — size `workers` against GPU memory, not CPU count. +- `limit_concurrency` sheds load with 503s instead of queueing unboundedly under a traffic spike. diff --git a/skills/fastapi/references/websocket-streaming.md b/skills/fastapi/references/websocket-streaming.md new file mode 100644 index 0000000..8b873f9 --- /dev/null +++ b/skills/fastapi/references/websocket-streaming.md @@ -0,0 +1,49 @@ +# WebSocket Streaming + +Real-time frame-by-frame inference over a WebSocket, for live video or camera feeds where per-request HTTP overhead is unacceptable. + +## Streaming Endpoint + +```python +import base64 + +from fastapi import APIRouter, WebSocket, WebSocketDisconnect +from loguru import logger + +router = APIRouter() + + +@router.websocket("/ws/stream") +async def stream_inference(websocket: WebSocket) -> None: + """Stream video frames and return detections in real time.""" + await websocket.accept() + logger.info("WebSocket client connected") + + try: + while True: + data = await websocket.receive_json() + frame_b64 = data.get("frame") + if not frame_b64: + await websocket.send_json({"error": "Missing 'frame' field"}) + continue + + frame_bytes = base64.b64decode(frame_b64) + detections = await run_inference(frame_bytes) + + await websocket.send_json({ + "detections": [d.model_dump() for d in detections], + "frame_id": data.get("frame_id"), + }) + except WebSocketDisconnect: + logger.info("WebSocket client disconnected") +``` + +## Notes + +- **Always `await websocket.accept()` first.** Nothing can be sent or received before the handshake completes. +- **Always catch `WebSocketDisconnect`.** Clients drop without closing cleanly; an uncaught disconnect fills logs with tracebacks and can leak per-connection resources. +- **Echo `frame_id` back.** WebSocket responses are not request-scoped, so the client needs a correlation key to match a detection payload to the frame it sent. +- **Validate the frame payload.** The example returns a JSON error and continues rather than closing the socket on a malformed message. For stricter services, validate the incoming dict with a Pydantic model and send back the `ErrorResponse` shape. +- **`model_dump()` before sending.** `send_json` cannot serialize Pydantic models, numpy arrays, or tensors directly. +- **Never block the event loop.** `run_inference` must be an async wrapper; for a synchronous model, dispatch it with `run_in_executor` so other connections keep flowing. +- **Consider back-pressure.** A producer sending frames faster than the GPU can process them will grow the receive queue without bound — drop stale frames client-side or gate on an acknowledgement. diff --git a/skills/gcp/SKILL.md b/skills/gcp/SKILL.md index 0f7eaee..d51d7bb 100644 --- a/skills/gcp/SKILL.md +++ b/skills/gcp/SKILL.md @@ -11,447 +11,77 @@ description: > # GCP Skill -Google Cloud Platform services for CV/ML projects: Artifact Registry, Cloud Storage, Vertex AI training, and Docker image management. +Google Cloud Platform services for CV/ML projects: Artifact Registry for Docker images and +Python packages, Cloud Storage for datasets and checkpoints, Vertex AI for GPU training jobs, +and the gcloud CLI for everything else. Add clients with +`pixi add google-cloud-storage google-cloud-aiplatform`. -## Artifact Registry +## Essential Core -Use Artifact Registry (replaces the deprecated Container Registry) for Docker images and Python packages. - -### Create a Docker Repository - -```bash -# Create a Docker repository in Artifact Registry -gcloud artifacts repositories create ml-images \ - --repository-format=docker \ - --location=us-central1 \ - --description="CV/ML training and inference images" - -# Verify creation -gcloud artifacts repositories list --location=us-central1 -``` - -### Push and Pull Docker Images +The two operations that come up constantly: push an image to Artifact Registry, and move data +to/from a GCS bucket. ```bash -# Configure Docker authentication for Artifact Registry +# Push a training image to Artifact Registry (gcr.io is deprecated — use pkg.dev) gcloud auth configure-docker us-central1-docker.pkg.dev -# Tag and push docker tag my-training:latest \ us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 - docker push us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 - -# Pull -docker pull us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 ``` -### Python Package Repository - ```bash -gcloud artifacts repositories create ml-packages \ - --repository-format=python --location=us-central1 - -# Print pip/uv index settings for the repo -gcloud artifacts print-settings python --repository=ml-packages --location=us-central1 -``` - -Add the printed index to `pyproject.toml` under `[tool.uv]` as an `extra-index-url` (e.g. `https://us-central1-python.pkg.dev/my-project/ml-packages/simple/`). - -## Cloud Storage - -Use Cloud Storage for datasets, model checkpoints, and training artifacts. - -### Upload and Download with gsutil - -```bash -# Upload a dataset +# Datasets up, checkpoints down, outputs synced gsutil -m cp -r ./data/coco/ gs://my-ml-bucket/datasets/coco/ - -# Download model checkpoint gsutil cp gs://my-ml-bucket/checkpoints/resnet50_epoch20.pt ./checkpoints/ - -# Sync outputs (only transfers changed files) gsutil -m rsync -r ./outputs/ gs://my-ml-bucket/runs/experiment-42/ ``` -### Python Client - -```python -from google.cloud import storage - - -def upload_model_artifact( - bucket_name: str, - source_path: str, - destination_blob: str, -) -> str: - """Upload a model artifact to Cloud Storage.""" - client = storage.Client() - bucket = client.bucket(bucket_name) - blob = bucket.blob(destination_blob) - blob.upload_from_filename(source_path) - return f"gs://{bucket_name}/{destination_blob}" - - -def download_checkpoint( - bucket_name: str, - blob_name: str, - destination_path: str, -) -> None: - """Download a checkpoint from Cloud Storage.""" - client = storage.Client() - bucket = client.bucket(bucket_name) - blob = bucket.blob(blob_name) - blob.download_to_filename(destination_path) -``` - -### Mount with gcsfuse +## Vertex AI Artifacts Are Not Automatic -```bash -# Mount a bucket as a local directory (useful for large datasets) -gcsfuse --implicit-dirs my-ml-bucket /mnt/gcs-data - -# Use in a training container -docker run --privileged \ - -v /mnt/gcs-data:/data:ro \ - my-training:latest -``` - -```dockerfile -# Install gcsfuse in a training container -RUN echo "deb https://packages.cloud.google.com/apt gcsfuse-jammy main" \ - > /etc/apt/sources.list.d/gcsfuse.list && \ - curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \ - | apt-key add - && \ - apt-get update && \ - apt-get install -y gcsfuse && \ - rm -rf /var/lib/apt/lists/* -``` - -## Vertex AI Training Jobs - -Submit custom training jobs to Vertex AI for GPU-accelerated model training. - -### Custom Training Job - -```python -from google.cloud import aiplatform - - -def submit_training_job( - project: str, - location: str, - display_name: str, - container_uri: str, - args: list[str], - machine_type: str = "n1-standard-8", - accelerator_type: str = "NVIDIA_TESLA_T4", - accelerator_count: int = 1, - staging_bucket: str = "", -) -> aiplatform.CustomJob: - """Submit a custom training job to Vertex AI.""" - aiplatform.init(project=project, location=location, staging_bucket=staging_bucket) - - job = aiplatform.CustomJob.from_local_script( - display_name=display_name, - script_path="src/train.py", - container_uri=container_uri, - args=args, - machine_type=machine_type, - accelerator_type=accelerator_type, - accelerator_count=accelerator_count, - ) - job.run(sync=False) - return job -``` - -### Custom Container Training - -```python -from google.cloud import aiplatform - -aiplatform.init(project="my-project", location="us-central1") - -job = aiplatform.CustomContainerTrainingJob( - display_name="resnet50-finetune", - container_uri="us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0", - command=["python", "-m", "my_project.train"], - model_serving_container_image_uri=( - "us-central1-docker.pkg.dev/my-project/ml-images/inference:v1.2.0" - ), -) - -model = job.run( - args=["--config=configs/finetune.yaml", "--epochs=50"], - replica_count=1, - machine_type="n1-standard-8", - accelerator_type="NVIDIA_TESLA_V100", - accelerator_count=2, - base_output_dir="gs://my-ml-bucket/vertex-outputs/", -) -``` - -### GPU Selection Reference - -| GPU Type | Vertex AI Name | Use Case | -|----------|----------------|----------| -| T4 | `NVIDIA_TESLA_T4` | Inference, small-batch training | -| V100 | `NVIDIA_TESLA_V100` | Training (16 GB HBM2) | -| A100 40GB | `NVIDIA_TESLA_A100` | Large-scale training | -| A100 80GB | `NVIDIA_A100_80GB` | Large models, large batch sizes | -| L4 | `NVIDIA_L4` | Inference, cost-effective training | -| H100 | `NVIDIA_H100_80GB` | Highest throughput training | - -### Prebuilt Training Containers - -Skip building a custom image: pass a Google prebuilt container (e.g. `us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-3:latest`) as `container_uri` to `from_local_script`, plus `requirements=["torchvision", "albumentations", ...]` for extra deps. - -### Retrieving Artifacts After Training - -**The training VM is ephemeral — anything not written to GCS is gone when the job ends.** -Write checkpoints and exports to the GCS path Vertex provides (`AIP_MODEL_DIR`), then pull -them down explicitly once the job finishes. Do not assume the job "left them somewhere". +**The Vertex training VM is ephemeral — anything not written to GCS is gone when the job ends.** +Write checkpoints and exports to the GCS path Vertex provides (`AIP_MODEL_DIR`), then pull them +down explicitly once the job finishes. Do not assume the job "left them somewhere". ```python import os -# Inside the training container: write to the GCS path Vertex provides. -# Falls back to a local dir so the same script runs off-cloud unchanged. -model_dir = os.environ.get("AIP_MODEL_DIR", "outputs/model") +model_dir = os.environ.get("AIP_MODEL_DIR", "outputs/model") # local fallback off-cloud trainer.save_checkpoint(f"{model_dir}/best.ckpt") ``` ```bash -# After the job completes: sync every artifact to the local machine. gcloud storage rsync -r "gs://my-bucket/jobs/${JOB_ID}/model" ./artifacts/ - -# Verify before deleting anything remote. -ls -lh ./artifacts/ -``` - -## Docker Image Management - -Build, tag, and push images to Artifact Registry for Vertex AI and GKE workloads. - -### Build and Push Workflow - -```bash -# Variables -PROJECT_ID="my-project" -REGION="us-central1" -REPO="ml-images" -IMAGE="training" -TAG="v1.2.0" -FULL_URI="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPO}/${IMAGE}:${TAG}" - -# Build with cache -docker build \ - --tag "${FULL_URI}" \ - --cache-from "${FULL_URI}" \ - --build-arg BUILDKIT_INLINE_CACHE=1 \ - -f Dockerfile.training . - -# Push -docker push "${FULL_URI}" +ls -lh ./artifacts/ # verify before deleting anything remote ``` -### Multi-Stage for Training and Inference - -```dockerfile -FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 AS base -ENV DEBIAN_FRONTEND=noninteractive PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 -RUN apt-get update && apt-get install -y --no-install-recommends \ - libgl1-mesa-glx libglib2.0-0 curl && rm -rf /var/lib/apt/lists/* -RUN curl -fsSL https://pixi.sh/install.sh | bash -ENV PATH="/root/.pixi/bin:${PATH}" - -# Training — full environment with dev tools -FROM base AS training -WORKDIR /app -COPY pixi.toml pixi.lock ./ -RUN pixi install -COPY src/ src/ -COPY configs/ configs/ -RUN useradd -m -u 1000 trainer -USER trainer -ENTRYPOINT ["pixi", "run", "python", "-m"] -CMD ["my_project.train"] - -# Inference — minimal runtime -FROM base AS inference -WORKDIR /app -COPY pixi.toml pixi.lock ./ -RUN pixi install -COPY src/ src/ -RUN useradd -m -u 1000 appuser -USER appuser -HEALTHCHECK --interval=30s --timeout=10s --retries=3 \ - CMD curl -f http://localhost:8000/health || exit 1 -EXPOSE 8000 -CMD ["pixi", "run", "uvicorn", "src.serve:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -### GitHub Actions: Build and Push to Artifact Registry - -```yaml -# .github/workflows/docker-gcp.yml -name: Build and Push to Artifact Registry - -on: - push: - tags: ["v*"] - -env: - PROJECT_ID: my-project - REGION: us-central1 - REPOSITORY: ml-images +Full job-submission detail is in `references/vertex-training.md`. -jobs: - build-push: - runs-on: ubuntu-latest - permissions: - contents: read - id-token: write - - steps: - - uses: actions/checkout@v4 - - - uses: google-github-actions/auth@v2 - with: - workload_identity_provider: ${{ secrets.WIF_PROVIDER }} - service_account: ${{ secrets.WIF_SERVICE_ACCOUNT }} - - - uses: google-github-actions/setup-gcloud@v2 - - - run: gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev - - - uses: docker/build-push-action@v5 - with: - push: true - tags: | - ${{ env.REGION }}-docker.pkg.dev/${{ env.PROJECT_ID }}/${{ env.REPOSITORY }}/training:${{ github.ref_name }} - ${{ env.REGION }}-docker.pkg.dev/${{ env.PROJECT_ID }}/${{ env.REPOSITORY }}/training:latest - target: training - cache-from: type=gha - cache-to: type=gha,mode=max -``` - -## Authentication - -### Service Account Setup +## Everyday gcloud Commands ```bash -# Create a service account for training jobs -gcloud iam service-accounts create ml-trainer \ - --display-name="ML Training Service Account" - -# Grant required roles (least privilege) -SA_EMAIL="ml-trainer@my-project.iam.gserviceaccount.com" -for ROLE in aiplatform.user storage.objectAdmin artifactregistry.reader; do - gcloud projects add-iam-policy-binding my-project \ - --member="serviceAccount:${SA_EMAIL}" --role="roles/${ROLE}" -done -``` - -### Workload Identity Federation for CI - -```bash -# Create a workload identity pool for GitHub Actions -gcloud iam workload-identity-pools create "github-pool" \ - --location="global" \ - --display-name="GitHub Actions Pool" - -gcloud iam workload-identity-pools providers create-oidc "github-provider" \ - --location="global" \ - --workload-identity-pool="github-pool" \ - --display-name="GitHub Provider" \ - --attribute-mapping="google.subject=assertion.sub,attribute.repository=assertion.repository" \ - --issuer-uri="https://token.actions.githubusercontent.com" - -# Allow the GitHub repo to impersonate the service account -gcloud iam service-accounts add-iam-policy-binding "${SA_EMAIL}" \ - --role="roles/iam.workloadIdentityUser" \ - --member="principalSet://iam.googleapis.com/projects/PROJECT_NUMBER/locations/global/workloadIdentityPools/github-pool/attribute.repository/OWNER/REPO" -``` - -### Docker Authentication - -```bash -# Local: gcloud auth configure-docker us-central1-docker.pkg.dev -# CI (prefer WIF over keys): cat key.json | docker login -u _json_key \ -# --password-stdin https://us-central1-docker.pkg.dev -``` - -## Pydantic Configuration - -Typed models for GCP project settings and job specs. - -```python -from pydantic import BaseModel, Field - - -class GCPConfig(BaseModel, frozen=True): - project_id: str = Field(description="GCP project ID") - region: str = Field(default="us-central1") - zone: str = Field(default="us-central1-a") - - -class ArtifactRegistryConfig(BaseModel, frozen=True): - """Artifact Registry repository settings.""" - - repository: str = Field(description="Repository name") - location: str = Field(default="us-central1") - - def docker_uri(self, project_id: str, image: str, tag: str) -> str: - """Build the full Docker image URI.""" - return ( - f"{self.location}-docker.pkg.dev/{project_id}" - f"/{self.repository}/{image}:{tag}" - ) - - -class StorageBucketConfig(BaseModel, frozen=True): - """Cloud Storage bucket configuration.""" - - bucket_name: str = Field(description="GCS bucket name") - datasets_prefix: str = Field(default="datasets/") - checkpoints_prefix: str = Field(default="checkpoints/") - outputs_prefix: str = Field(default="outputs/") - - def dataset_uri(self, name: str) -> str: - return f"gs://{self.bucket_name}/{self.datasets_prefix}{name}" - - def checkpoint_uri(self, name: str) -> str: - return f"gs://{self.bucket_name}/{self.checkpoints_prefix}{name}" - - -class VertexJobConfig(BaseModel, frozen=True): - """Vertex AI training job configuration.""" - - display_name: str - machine_type: str = Field(default="n1-standard-8") - accelerator_type: str = Field(default="NVIDIA_TESLA_T4") - accelerator_count: int = Field(default=1, ge=1, le=8) - replica_count: int = Field(default=1, ge=1) - staging_bucket: str = Field(description="GCS bucket for staging") - boot_disk_size_gb: int = Field(default=100, ge=50) +# Auth: prefer Application Default Credentials over key files +gcloud auth application-default login +gcloud config set project my-project +gcloud config set compute/region us-central1 +# Repositories and images +gcloud artifacts repositories list --location=us-central1 +gcloud artifacts docker images list us-central1-docker.pkg.dev/my-project/ml-images -class GCPProjectConfig(BaseModel, frozen=True): - """Complete GCP configuration — composed from a Hydra-compatible configs/gcp.yaml.""" +# Vertex AI jobs +gcloud ai custom-jobs list --region=us-central1 +gcloud ai custom-jobs stream-logs JOB_ID --region=us-central1 - gcp: GCPConfig - artifact_registry: ArtifactRegistryConfig - storage: StorageBucketConfig - vertex_job: VertexJobConfig +# Buckets +gcloud storage ls gs://my-ml-bucket/ +gcloud storage rsync -r gs://my-ml-bucket/runs/experiment-42/ ./outputs/ ``` -## Project Dependencies +Wrap the recurring ones in a `justfile` (`just gcs-sync-outputs`, `just docker-push-train`, +`just vertex-list-jobs`) so the team runs identical commands. -Add the GCP client libraries with `pixi add google-cloud-storage google-cloud-aiplatform` (and `google-cloud-artifact-registry` as a dev dependency). Wrap the recurring `gcloud`/`gsutil` commands above in a `justfile` for team consistency — e.g. `just gcs-sync-outputs`, `just docker-push-train`, `just vertex-list-jobs` (`gcloud ai custom-jobs list --region=us-central1`). - -## Best Practices +## Conventions 1. **Artifact Registry, not Container Registry** — the latter is deprecated. 2. **Pin image tags for Vertex AI jobs** — semantic versions or Git SHAs, never `:latest` in production. @@ -475,3 +105,12 @@ Add the GCP client libraries with `pixi add google-cloud-storage google-cloud-ai - ❌ Submitting Vertex jobs without a staging bucket. - ❌ Multi-region buckets for single-region training data — extra egress, no benefit. - ❌ Hardcoding project IDs/regions — use Pydantic config or env vars. + +## Deep dives + +- `references/artifact-registry.md` — read when creating Docker or Python package repositories, or wiring a private index into `pyproject.toml`. +- `references/cloud-storage.md` — read when using the Python `google.cloud.storage` client or mounting a bucket with gcsfuse. +- `references/vertex-training.md` — read when submitting a custom training job, choosing a GPU/accelerator, using prebuilt containers, or retrieving artifacts after a run. +- `references/docker-image-management.md` — read when writing multi-stage training/inference Dockerfiles or a GitHub Actions build-and-push workflow. +- `references/authentication.md` — read when setting up service accounts, IAM roles, or Workload Identity Federation for CI. +- `references/pydantic-configuration.md` — read when modelling GCP project/bucket/job settings as typed config or wiring `just` targets. diff --git a/skills/gcp/references/artifact-registry.md b/skills/gcp/references/artifact-registry.md new file mode 100644 index 0000000..57e501c --- /dev/null +++ b/skills/gcp/references/artifact-registry.md @@ -0,0 +1,46 @@ +# Artifact Registry + +Scope: creating and using Artifact Registry repositories for Docker images and Python packages. + +Use Artifact Registry (replaces the deprecated Container Registry) for Docker images and Python packages. + +## Create a Docker Repository + +```bash +# Create a Docker repository in Artifact Registry +gcloud artifacts repositories create ml-images \ + --repository-format=docker \ + --location=us-central1 \ + --description="CV/ML training and inference images" + +# Verify creation +gcloud artifacts repositories list --location=us-central1 +``` + +## Push and Pull Docker Images + +```bash +# Configure Docker authentication for Artifact Registry +gcloud auth configure-docker us-central1-docker.pkg.dev + +# Tag and push +docker tag my-training:latest \ + us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 + +docker push us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 + +# Pull +docker pull us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0 +``` + +## Python Package Repository + +```bash +gcloud artifacts repositories create ml-packages \ + --repository-format=python --location=us-central1 + +# Print pip/uv index settings for the repo +gcloud artifacts print-settings python --repository=ml-packages --location=us-central1 +``` + +Add the printed index to `pyproject.toml` under `[tool.uv]` as an `extra-index-url` (e.g. `https://us-central1-python.pkg.dev/my-project/ml-packages/simple/`). diff --git a/skills/gcp/references/authentication.md b/skills/gcp/references/authentication.md new file mode 100644 index 0000000..ac82b86 --- /dev/null +++ b/skills/gcp/references/authentication.md @@ -0,0 +1,47 @@ +# GCP Authentication and IAM + +Scope: service accounts for training jobs, Workload Identity Federation for CI, and Docker registry authentication. + +## Service Account Setup + +```bash +# Create a service account for training jobs +gcloud iam service-accounts create ml-trainer \ + --display-name="ML Training Service Account" + +# Grant required roles (least privilege) +SA_EMAIL="ml-trainer@my-project.iam.gserviceaccount.com" +for ROLE in aiplatform.user storage.objectAdmin artifactregistry.reader; do + gcloud projects add-iam-policy-binding my-project \ + --member="serviceAccount:${SA_EMAIL}" --role="roles/${ROLE}" +done +``` + +## Workload Identity Federation for CI + +```bash +# Create a workload identity pool for GitHub Actions +gcloud iam workload-identity-pools create "github-pool" \ + --location="global" \ + --display-name="GitHub Actions Pool" + +gcloud iam workload-identity-pools providers create-oidc "github-provider" \ + --location="global" \ + --workload-identity-pool="github-pool" \ + --display-name="GitHub Provider" \ + --attribute-mapping="google.subject=assertion.sub,attribute.repository=assertion.repository" \ + --issuer-uri="https://token.actions.githubusercontent.com" + +# Allow the GitHub repo to impersonate the service account +gcloud iam service-accounts add-iam-policy-binding "${SA_EMAIL}" \ + --role="roles/iam.workloadIdentityUser" \ + --member="principalSet://iam.googleapis.com/projects/PROJECT_NUMBER/locations/global/workloadIdentityPools/github-pool/attribute.repository/OWNER/REPO" +``` + +## Docker Authentication + +```bash +# Local: gcloud auth configure-docker us-central1-docker.pkg.dev +# CI (prefer WIF over keys): cat key.json | docker login -u _json_key \ +# --password-stdin https://us-central1-docker.pkg.dev +``` diff --git a/skills/gcp/references/cloud-storage.md b/skills/gcp/references/cloud-storage.md new file mode 100644 index 0000000..f71f58b --- /dev/null +++ b/skills/gcp/references/cloud-storage.md @@ -0,0 +1,72 @@ +# Cloud Storage + +Scope: moving datasets, checkpoints, and training artifacts in and out of GCS with gsutil, the Python client, and gcsfuse. + +Use Cloud Storage for datasets, model checkpoints, and training artifacts. + +## Upload and Download with gsutil + +```bash +# Upload a dataset +gsutil -m cp -r ./data/coco/ gs://my-ml-bucket/datasets/coco/ + +# Download model checkpoint +gsutil cp gs://my-ml-bucket/checkpoints/resnet50_epoch20.pt ./checkpoints/ + +# Sync outputs (only transfers changed files) +gsutil -m rsync -r ./outputs/ gs://my-ml-bucket/runs/experiment-42/ +``` + +## Python Client + +```python +from google.cloud import storage + + +def upload_model_artifact( + bucket_name: str, + source_path: str, + destination_blob: str, +) -> str: + """Upload a model artifact to Cloud Storage.""" + client = storage.Client() + bucket = client.bucket(bucket_name) + blob = bucket.blob(destination_blob) + blob.upload_from_filename(source_path) + return f"gs://{bucket_name}/{destination_blob}" + + +def download_checkpoint( + bucket_name: str, + blob_name: str, + destination_path: str, +) -> None: + """Download a checkpoint from Cloud Storage.""" + client = storage.Client() + bucket = client.bucket(bucket_name) + blob = bucket.blob(blob_name) + blob.download_to_filename(destination_path) +``` + +## Mount with gcsfuse + +```bash +# Mount a bucket as a local directory (useful for large datasets) +gcsfuse --implicit-dirs my-ml-bucket /mnt/gcs-data + +# Use in a training container +docker run --privileged \ + -v /mnt/gcs-data:/data:ro \ + my-training:latest +``` + +```dockerfile +# Install gcsfuse in a training container +RUN echo "deb https://packages.cloud.google.com/apt gcsfuse-jammy main" \ + > /etc/apt/sources.list.d/gcsfuse.list && \ + curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \ + | apt-key add - && \ + apt-get update && \ + apt-get install -y gcsfuse && \ + rm -rf /var/lib/apt/lists/* +``` diff --git a/skills/gcp/references/docker-image-management.md b/skills/gcp/references/docker-image-management.md new file mode 100644 index 0000000..ea7f995 --- /dev/null +++ b/skills/gcp/references/docker-image-management.md @@ -0,0 +1,112 @@ +# Docker Image Management for GCP + +Scope: building, tagging, and pushing training/inference images to Artifact Registry for Vertex AI and GKE workloads, including CI. + +## Contents + +- [Build and Push Workflow](#build-and-push-workflow) +- [Multi-Stage for Training and Inference](#multi-stage-for-training-and-inference) +- [GitHub Actions: Build and Push to Artifact Registry](#github-actions-build-and-push-to-artifact-registry) + +## Build and Push Workflow + +```bash +# Variables +PROJECT_ID="my-project" +REGION="us-central1" +REPO="ml-images" +IMAGE="training" +TAG="v1.2.0" +FULL_URI="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPO}/${IMAGE}:${TAG}" + +# Build with cache +docker build \ + --tag "${FULL_URI}" \ + --cache-from "${FULL_URI}" \ + --build-arg BUILDKIT_INLINE_CACHE=1 \ + -f Dockerfile.training . + +# Push +docker push "${FULL_URI}" +``` + +## Multi-Stage for Training and Inference + +```dockerfile +FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 AS base +ENV DEBIAN_FRONTEND=noninteractive PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 +RUN apt-get update && apt-get install -y --no-install-recommends \ + libgl1-mesa-glx libglib2.0-0 curl && rm -rf /var/lib/apt/lists/* +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" + +# Training — full environment with dev tools +FROM base AS training +WORKDIR /app +COPY pixi.toml pixi.lock ./ +RUN pixi install +COPY src/ src/ +COPY configs/ configs/ +RUN useradd -m -u 1000 trainer +USER trainer +ENTRYPOINT ["pixi", "run", "python", "-m"] +CMD ["my_project.train"] + +# Inference — minimal runtime +FROM base AS inference +WORKDIR /app +COPY pixi.toml pixi.lock ./ +RUN pixi install +COPY src/ src/ +RUN useradd -m -u 1000 appuser +USER appuser +HEALTHCHECK --interval=30s --timeout=10s --retries=3 \ + CMD curl -f http://localhost:8000/health || exit 1 +EXPOSE 8000 +CMD ["pixi", "run", "uvicorn", "src.serve:app", "--host", "0.0.0.0", "--port", "8000"] +``` + +## GitHub Actions: Build and Push to Artifact Registry + +```yaml +# .github/workflows/docker-gcp.yml +name: Build and Push to Artifact Registry + +on: + push: + tags: ["v*"] + +env: + PROJECT_ID: my-project + REGION: us-central1 + REPOSITORY: ml-images + +jobs: + build-push: + runs-on: ubuntu-latest + permissions: + contents: read + id-token: write + + steps: + - uses: actions/checkout@v4 + + - uses: google-github-actions/auth@v2 + with: + workload_identity_provider: ${{ secrets.WIF_PROVIDER }} + service_account: ${{ secrets.WIF_SERVICE_ACCOUNT }} + + - uses: google-github-actions/setup-gcloud@v2 + + - run: gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev + + - uses: docker/build-push-action@v5 + with: + push: true + tags: | + ${{ env.REGION }}-docker.pkg.dev/${{ env.PROJECT_ID }}/${{ env.REPOSITORY }}/training:${{ github.ref_name }} + ${{ env.REGION }}-docker.pkg.dev/${{ env.PROJECT_ID }}/${{ env.REPOSITORY }}/training:latest + target: training + cache-from: type=gha + cache-to: type=gha,mode=max +``` diff --git a/skills/gcp/references/pydantic-configuration.md b/skills/gcp/references/pydantic-configuration.md new file mode 100644 index 0000000..3d901c6 --- /dev/null +++ b/skills/gcp/references/pydantic-configuration.md @@ -0,0 +1,71 @@ +# Typed GCP Configuration and Project Dependencies + +Scope: Pydantic models for GCP project settings, registry URIs, buckets, and Vertex job specs — plus the client libraries and task-runner wiring that go with them. + +## Pydantic Configuration + +Typed models for GCP project settings and job specs. + +```python +from pydantic import BaseModel, Field + + +class GCPConfig(BaseModel, frozen=True): + project_id: str = Field(description="GCP project ID") + region: str = Field(default="us-central1") + zone: str = Field(default="us-central1-a") + + +class ArtifactRegistryConfig(BaseModel, frozen=True): + """Artifact Registry repository settings.""" + + repository: str = Field(description="Repository name") + location: str = Field(default="us-central1") + + def docker_uri(self, project_id: str, image: str, tag: str) -> str: + """Build the full Docker image URI.""" + return ( + f"{self.location}-docker.pkg.dev/{project_id}" + f"/{self.repository}/{image}:{tag}" + ) + + +class StorageBucketConfig(BaseModel, frozen=True): + """Cloud Storage bucket configuration.""" + + bucket_name: str = Field(description="GCS bucket name") + datasets_prefix: str = Field(default="datasets/") + checkpoints_prefix: str = Field(default="checkpoints/") + outputs_prefix: str = Field(default="outputs/") + + def dataset_uri(self, name: str) -> str: + return f"gs://{self.bucket_name}/{self.datasets_prefix}{name}" + + def checkpoint_uri(self, name: str) -> str: + return f"gs://{self.bucket_name}/{self.checkpoints_prefix}{name}" + + +class VertexJobConfig(BaseModel, frozen=True): + """Vertex AI training job configuration.""" + + display_name: str + machine_type: str = Field(default="n1-standard-8") + accelerator_type: str = Field(default="NVIDIA_TESLA_T4") + accelerator_count: int = Field(default=1, ge=1, le=8) + replica_count: int = Field(default=1, ge=1) + staging_bucket: str = Field(description="GCS bucket for staging") + boot_disk_size_gb: int = Field(default=100, ge=50) + + +class GCPProjectConfig(BaseModel, frozen=True): + """Complete GCP configuration — composed from a Hydra-compatible configs/gcp.yaml.""" + + gcp: GCPConfig + artifact_registry: ArtifactRegistryConfig + storage: StorageBucketConfig + vertex_job: VertexJobConfig +``` + +## Project Dependencies + +Add the GCP client libraries with `pixi add google-cloud-storage google-cloud-aiplatform` (and `google-cloud-artifact-registry` as a dev dependency). Wrap the recurring `gcloud`/`gsutil` commands in a `justfile` for team consistency — e.g. `just gcs-sync-outputs`, `just docker-push-train`, `just vertex-list-jobs` (`gcloud ai custom-jobs list --region=us-central1`). diff --git a/skills/gcp/references/vertex-training.md b/skills/gcp/references/vertex-training.md new file mode 100644 index 0000000..89cf50b --- /dev/null +++ b/skills/gcp/references/vertex-training.md @@ -0,0 +1,113 @@ +# Vertex AI Training Jobs + +Scope: submitting custom training jobs to Vertex AI, choosing accelerators, using prebuilt containers, and retrieving artifacts from the ephemeral training VM. + +## Contents + +- [Custom Training Job](#custom-training-job) +- [Custom Container Training](#custom-container-training) +- [GPU Selection Reference](#gpu-selection-reference) +- [Prebuilt Training Containers](#prebuilt-training-containers) +- [Retrieving Artifacts After Training](#retrieving-artifacts-after-training) + +Submit custom training jobs to Vertex AI for GPU-accelerated model training. + +## Custom Training Job + +```python +from google.cloud import aiplatform + + +def submit_training_job( + project: str, + location: str, + display_name: str, + container_uri: str, + args: list[str], + machine_type: str = "n1-standard-8", + accelerator_type: str = "NVIDIA_TESLA_T4", + accelerator_count: int = 1, + staging_bucket: str = "", +) -> aiplatform.CustomJob: + """Submit a custom training job to Vertex AI.""" + aiplatform.init(project=project, location=location, staging_bucket=staging_bucket) + + job = aiplatform.CustomJob.from_local_script( + display_name=display_name, + script_path="src/train.py", + container_uri=container_uri, + args=args, + machine_type=machine_type, + accelerator_type=accelerator_type, + accelerator_count=accelerator_count, + ) + job.run(sync=False) + return job +``` + +## Custom Container Training + +```python +from google.cloud import aiplatform + +aiplatform.init(project="my-project", location="us-central1") + +job = aiplatform.CustomContainerTrainingJob( + display_name="resnet50-finetune", + container_uri="us-central1-docker.pkg.dev/my-project/ml-images/training:v1.2.0", + command=["python", "-m", "my_project.train"], + model_serving_container_image_uri=( + "us-central1-docker.pkg.dev/my-project/ml-images/inference:v1.2.0" + ), +) + +model = job.run( + args=["--config=configs/finetune.yaml", "--epochs=50"], + replica_count=1, + machine_type="n1-standard-8", + accelerator_type="NVIDIA_TESLA_V100", + accelerator_count=2, + base_output_dir="gs://my-ml-bucket/vertex-outputs/", +) +``` + +## GPU Selection Reference + +| GPU Type | Vertex AI Name | Use Case | +|----------|----------------|----------| +| T4 | `NVIDIA_TESLA_T4` | Inference, small-batch training | +| V100 | `NVIDIA_TESLA_V100` | Training (16 GB HBM2) | +| A100 40GB | `NVIDIA_TESLA_A100` | Large-scale training | +| A100 80GB | `NVIDIA_A100_80GB` | Large models, large batch sizes | +| L4 | `NVIDIA_L4` | Inference, cost-effective training | +| H100 | `NVIDIA_H100_80GB` | Highest throughput training | + +## Prebuilt Training Containers + +Skip building a custom image: pass a Google prebuilt container (e.g. `us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-3:latest`) as `container_uri` to `from_local_script`, plus `requirements=["torchvision", "albumentations", ...]` for extra deps. + +## Retrieving Artifacts After Training + +**The training VM is ephemeral — anything not written to GCS is gone when the job ends.** +Write checkpoints and exports to the GCS path Vertex provides (`AIP_MODEL_DIR`), then pull +them down explicitly once the job finishes. Do not assume the job "left them somewhere". + +```python +import os + +# Inside the training container: write to the GCS path Vertex provides. +# Falls back to a local dir so the same script runs off-cloud unchanged. +model_dir = os.environ.get("AIP_MODEL_DIR", "outputs/model") +trainer.save_checkpoint(f"{model_dir}/best.ckpt") +``` + +```bash +# After the job completes: sync every artifact to the local machine. +gcloud storage rsync -r "gs://my-bucket/jobs/${JOB_ID}/model" ./artifacts/ + +# Verify before deleting anything remote. +ls -lh ./artifacts/ +``` + +Related job management: `gcloud ai custom-jobs list --region=us-central1` lists submitted jobs; +wrap it in a `justfile` target such as `just vertex-list-jobs`. diff --git a/skills/gradio/SKILL.md b/skills/gradio/SKILL.md index e24a5a4..c1d861d 100644 --- a/skills/gradio/SKILL.md +++ b/skills/gradio/SKILL.md @@ -14,45 +14,26 @@ description: > Build Gradio 4.x demos with typed interfaces, Pydantic-validated configs, and Loguru logging. Use `gr.Interface` for a single prediction function; use `gr.Blocks` for multi-step workflows, comparisons, tabs, and conditional -visibility. Never expose raw model internals to the UI layer. +visibility. Never expose raw model internals to the UI layer. This page carries +the two core constructors; the deep dives hold serving, feedback, and deployment. -## gr.Interface — Single-Function Demos +## Core: `gr.Interface` for one prediction function -Fastest way to wrap one prediction function with defined inputs/outputs. +Wrap a single typed function with declared inputs and outputs. This covers most +"let me try the model" requests. ```python -"""Image classification demo with gr.Interface.""" - from __future__ import annotations import gradio as gr -import numpy as np from loguru import logger from PIL import Image -from pydantic import BaseModel, Field - -class ClassificationConfig(BaseModel, frozen=True): - model_path: str = "models/classifier.onnx" - labels_path: str = "models/labels.txt" - top_k: int = Field(default=5, ge=1, le=20) - image_size: tuple[int, int] = (224, 224) - -def classify_image( - image: Image.Image, - config: ClassificationConfig = ClassificationConfig(), -) -> dict[str, float]: - """Return top-k predictions for a single image.""" +def classify_image(image: Image.Image) -> dict[str, float]: + """Return label -> probability for a single image.""" logger.info("Received image of size {}", image.size) - preprocessed = preprocess(image, config.image_size) - predictions = run_model(preprocessed, config.model_path) - labels = load_labels(config.labels_path) - - top_indices = np.argsort(predictions)[-config.top_k :][::-1] - results = {labels[i]: float(predictions[i]) for i in top_indices} - logger.info("Top prediction: {} ({:.3f})", *next(iter(results.items()))) - return results + return run_model(image) # JSON-serializable dict, never raw tensors demo = gr.Interface( @@ -66,388 +47,52 @@ demo = gr.Interface( ) ``` -## gr.Blocks — Complex Layouts +## Core: `gr.Blocks` when the demo has more than one step -Standard for production demos. Combines tabs, rows/columns, buttons, streaming, -and batch inputs. The pattern below shows upload, webcam streaming, and batch tabs. +`gr.Blocks` gives explicit layout control — tabs, rows, columns, and events wired +per component. Load the model once outside the callback and close over it. ```python -"""Object detection demo with gr.Blocks layout.""" - -from __future__ import annotations - -import gradio as gr -from loguru import logger -from pydantic import BaseModel, Field - - -class DetectionConfig(BaseModel, frozen=True): - model_path: str = "models/detector.onnx" - confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) - nms_threshold: float = Field(default=0.45, ge=0.0, le=1.0) - max_detections: int = Field(default=100, ge=1, le=500) - device: str = "cpu" - - -def build_detection_demo(config: DetectionConfig | None = None) -> gr.Blocks: - config = config or DetectionConfig() - logger.info("Building detection demo with model: {}", config.model_path) +def build_demo(config: DetectionConfig) -> gr.Blocks: + server = ModelServer(config) # loaded once at startup with gr.Blocks(title="Object Detection", theme=gr.themes.Soft()) as demo: gr.Markdown("# Object Detection Demo") - - with gr.Tab("Image Upload"): - with gr.Row(): - with gr.Column(scale=1): - input_image = gr.Image(type="pil", label="Input Image") - confidence_slider = gr.Slider( - 0.0, 1.0, value=config.confidence_threshold, - step=0.05, label="Confidence Threshold", - ) - detect_btn = gr.Button("Detect Objects", variant="primary") - with gr.Column(scale=1): - output_image = gr.Image(type="pil", label="Detections") - output_json = gr.JSON(label="Detection Results") - detect_btn.click( - fn=run_detection, - inputs=[input_image, confidence_slider], - outputs=[output_image, output_json], - ) - - with gr.Tab("Webcam"): # .stream() for real-time per-frame processing - webcam_input = gr.Image(sources=["webcam"], type="pil", label="Webcam Feed") - webcam_output = gr.Image(type="pil", label="Live Detections") - webcam_input.stream( - fn=run_detection_realtime, inputs=[webcam_input], outputs=[webcam_output], - ) - - with gr.Tab("Batch Processing"): # gr.File(multiple) + gr.Gallery output - batch_input = gr.File(file_count="multiple", file_types=["image"], label="Images") - batch_output = gr.Gallery(label="Results", columns=3) - batch_btn = gr.Button("Process Batch", variant="primary") - batch_btn.click( - fn=run_batch_detection, - inputs=[batch_input, confidence_slider], - outputs=[batch_output], - ) - - return demo -``` - -## Multi-Modal Inputs and Outputs - -Mix `gr.Image`, `gr.Video`, `gr.Textbox`, `gr.Gallery`, and `gr.JSON`. Wire events -with `.change()` (fire on input change) or `.click()` (fire on button). - -```python -def build_multimodal_demo() -> gr.Blocks: - with gr.Blocks() as demo: - gr.Markdown("# Multi-Modal Analysis") - - with gr.Tab("Image Captioning"): - img_input = gr.Image(type="pil", label="Upload Image") - caption_output = gr.Textbox(label="Generated Caption", lines=3) - img_input.change(fn=generate_caption, inputs=[img_input], outputs=[caption_output]) - - with gr.Tab("Video Analysis"): # one input -> multiple outputs - video_input = gr.Video(label="Upload Video") - video_output = gr.Video(label="Annotated Video") - frame_gallery = gr.Gallery(label="Key Frames", columns=4) - analysis_json = gr.JSON(label="Analysis Results") - gr.Button("Analyze Video", variant="primary").click( - fn=analyze_video, - inputs=[video_input], - outputs=[video_output, frame_gallery, analysis_json], - ) - - with gr.Tab("Visual Question Answering"): # multiple inputs -> one output - with gr.Row(): - vqa_image = gr.Image(type="pil", label="Image") - with gr.Column(): - vqa_question = gr.Textbox(label="Question", placeholder="What is in this image?") - vqa_answer = gr.Textbox(label="Answer", interactive=False) - gr.Button("Ask", variant="primary").click( - fn=answer_question, inputs=[vqa_image, vqa_question], outputs=[vqa_answer], - ) - - return demo -``` - -## Model Serving - -### From the Hugging Face Hub with gr.load - -```python -demo = gr.load( - name="facebook/detr-resnet-50", - src="models", - title="DETR Object Detection", - description="Detect objects using DETR.", -) -``` - -### Custom ONNX / PyTorch Model Server - -Wrap the model in a class loaded once at startup; reference it via closure in the -callback. Same pattern applies to a PyTorch checkpoint (swap `ort.InferenceSession` -for `torch.load(...).eval()` and preprocess with `torchvision.transforms`). - -```python -"""Serve a custom ONNX model through Gradio.""" - -from __future__ import annotations - -from pathlib import Path - -import gradio as gr -import numpy as np -import onnxruntime as ort -from loguru import logger -from PIL import Image -from pydantic import BaseModel, Field - - -class ONNXModelConfig(BaseModel, frozen=True): - model_path: Path = Path("models/model.onnx") - input_size: tuple[int, int] = (640, 640) - providers: list[str] = Field( - default_factory=lambda: ["CUDAExecutionProvider", "CPUExecutionProvider"] - ) - - -class ModelServer: - """Wraps an ONNX model for Gradio serving (loaded once at startup).""" - - def __init__(self, config: ONNXModelConfig) -> None: - self.config = config - logger.info("Loading ONNX model from {}", config.model_path) - self.session = ort.InferenceSession(str(config.model_path), providers=config.providers) - self.input_name = self.session.get_inputs()[0].name - - def predict(self, image: Image.Image) -> tuple[Image.Image, dict]: - """Run inference, return annotated image and results dict.""" - input_array = self._preprocess(image) - outputs = self.session.run(None, {self.input_name: input_array}) - detections = self._postprocess(outputs, image.size) - annotated = self._draw_detections(image, detections) - logger.info("Found {} detections", len(detections)) - return annotated, {"num_detections": len(detections), "detections": detections} - - def _preprocess(self, image: Image.Image) -> np.ndarray: - resized = image.resize(self.config.input_size) - array = np.array(resized, dtype=np.float32) / 255.0 - return np.transpose(array, (2, 0, 1))[np.newaxis, ...] - - def _postprocess(self, outputs: list, original_size: tuple[int, int]) -> list[dict]: - ... # depends on model architecture - - def _draw_detections(self, image: Image.Image, detections: list[dict]) -> Image.Image: - ... # uses PIL.ImageDraw - - -def create_onnx_demo(config: ONNXModelConfig | None = None) -> gr.Blocks: - server = ModelServer(config or ONNXModelConfig()) - with gr.Blocks(title="ONNX Model Demo") as demo: - gr.Markdown("# Custom ONNX Model Demo") - with gr.Row(): - input_img = gr.Image(type="pil", label="Input") - output_img = gr.Image(type="pil", label="Output") - results_json = gr.JSON(label="Results") - gr.Button("Run Inference", variant="primary").click( - fn=server.predict, inputs=[input_img], outputs=[output_img, results_json], - ) - return demo -``` - -### PyTorch Model with @torch.inference_mode - -```python -from torchvision import transforms - - -def create_pytorch_demo(config: TorchModelConfig) -> gr.Blocks: - model = torch.load(config.checkpoint_path, map_location=config.device) - model.eval() - transform = transforms.Compose([ - transforms.Resize(config.input_size), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ]) - - @torch.inference_mode() - def predict(image: Image.Image) -> dict[str, float]: - tensor = transform(image).unsqueeze(0).to(config.device) - probs = torch.nn.functional.softmax(model(tensor)[0], dim=0) - top5_prob, top5_idx = torch.topk(probs, 5) - return { - (config.class_names[idx] if config.class_names else f"class_{idx}"): float(prob) - for prob, idx in zip(top5_prob, top5_idx) - } - - with gr.Blocks(title="PyTorch Model Demo") as demo: with gr.Row(): - input_img = gr.Image(type="pil", label="Upload Image") - output_label = gr.Label(num_top_classes=5, label="Predictions") - gr.Button("Classify", variant="primary").click( - fn=predict, inputs=[input_img], outputs=[output_label], + with gr.Column(scale=1): + input_image = gr.Image(type="pil", label="Input Image") + confidence = gr.Slider( + 0.0, 1.0, value=config.confidence_threshold, + step=0.05, label="Confidence Threshold", + ) + detect_btn = gr.Button("Detect Objects", variant="primary") + with gr.Column(scale=1): + output_image = gr.Image(type="pil", label="Detections") + output_json = gr.JSON(label="Detection Results") + + detect_btn.click( + fn=server.predict, + inputs=[input_image, confidence], + outputs=[output_image, output_json], ) - return demo -``` - -## Reusable Components and Comparison Layouts - -Factor repeated controls into helper functions returning components. Reuse them to -build side-by-side comparison layouts. - -```python -def create_model_selector(models: dict[str, str], default: str | None = None) -> gr.Dropdown: - choices = list(models.keys()) - return gr.Dropdown(choices=choices, value=default or choices[0], label="Select Model") - -def create_preprocessing_controls() -> tuple[gr.Slider, gr.Slider, gr.Checkbox]: - brightness = gr.Slider(-1.0, 1.0, value=0.0, step=0.1, label="Brightness") - contrast = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Contrast") - grayscale = gr.Checkbox(value=False, label="Convert to Grayscale") - return brightness, contrast, grayscale - - -def build_comparison_layout() -> gr.Blocks: - with gr.Blocks() as demo: - input_image = gr.Image(type="pil", label="Input Image") - with gr.Row(): - with gr.Column(): - gr.Markdown("### Model A") - model_a = create_model_selector({"YOLOv8-n": "yolov8n", "YOLOv8-s": "yolov8s"}) - output_a = gr.Image(type="pil", label="Model A Output") - metrics_a = gr.JSON(label="Model A Metrics") - with gr.Column(): - gr.Markdown("### Model B") - model_b = create_model_selector({"YOLOv8-m": "yolov8m", "YOLOv8-l": "yolov8l"}) - output_b = gr.Image(type="pil", label="Model B Output") - metrics_b = gr.JSON(label="Model B Metrics") - gr.Button("Compare Models", variant="primary").click( - fn=compare_models, - inputs=[input_image, model_a, model_b], - outputs=[output_a, metrics_a, output_b, metrics_b], - ) return demo -``` - -## Flagging and Feedback Collection - -Subclass `gr.FlaggingCallback` to persist structured feedback for active learning. - -```python -"""Custom flagging callback for ML feedback collection.""" - -from __future__ import annotations - -from datetime import datetime, timezone -from pathlib import Path - -import gradio as gr -from loguru import logger -from pydantic import BaseModel -class FlaggedSample(BaseModel, frozen=True): - timestamp: str - flag_reason: str - input_hash: str - prediction: str - user_correction: str | None = None - - -class MLFlaggingCallback(gr.FlaggingCallback): - def __init__(self, output_dir: str = "flagged_data") -> None: - self.output_dir = Path(output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - - def setup(self, components: list, flagging_dir: str | Path) -> None: - self.flagging_dir = Path(flagging_dir) - self.flagging_dir.mkdir(parents=True, exist_ok=True) - - def flag(self, flag_data: list, flag_option: str = "incorrect", username: str | None = None) -> int: - sample = FlaggedSample( - timestamp=datetime.now(tz=timezone.utc).isoformat(), - flag_reason=flag_option, - input_hash=str(hash(str(flag_data[0]))), - prediction=str(flag_data[1]) if len(flag_data) > 1 else "", - user_correction=str(flag_data[2]) if len(flag_data) > 2 else None, - ) - (self.output_dir / f"flag_{sample.timestamp}.json").write_text(sample.model_dump_json(indent=2)) - logger.info("Flagged sample saved (reason: {})", flag_option) - return 1 - - -demo = gr.Interface( - fn=classify_image, - inputs=gr.Image(type="pil"), - outputs=gr.Label(), - flagging_callback=MLFlaggingCallback(output_dir="flagged_data"), - flagging_options=["incorrect", "low_confidence", "interesting"], -) -``` - -## Deployment - -Launch with production settings; optionally add auth or mount inside FastAPI. -Never use `share=True` in production — host on Spaces, Docker, or behind a proxy. - -```python -def launch_demo(demo: gr.Blocks, share: bool = False) -> None: - demo.launch( - server_name="0.0.0.0", - server_port=7860, - share=share, - show_error=True, - max_threads=10, - auth=None, # or [("admin", "secure_password")] for basic auth - ) - - -def mount_on_fastapi(demo: gr.Blocks): - from fastapi import FastAPI - - app = FastAPI(title="ML Demo API") - - @app.get("/api/health") - async def health() -> dict[str, str]: - return {"status": "healthy"} - - return gr.mount_gradio_app(app, demo, path="/demo") +demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True) ``` -### Hugging Face Spaces - -`Dockerfile` and `app.py` entry point: +## Conventions -```dockerfile -FROM python:3.11-slim -WORKDIR /app -RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* -RUN curl -fsSL https://pixi.sh/install.sh | bash -ENV PATH="/root/.pixi/bin:${PATH}" -COPY pixi.toml pixi.lock ./ -RUN pixi install -COPY src/ ./src/ -COPY models/ ./models/ -EXPOSE 7860 -CMD ["pixi", "run", "python", "-m", "src.app", "--server-name", "0.0.0.0", "--server-port", "7860"] -``` - -```python -"""app.py — Hugging Face Spaces entry point.""" - -from src.config import DetectionConfig -from src.demo import build_detection_demo - -demo = build_detection_demo(DetectionConfig()) - -if __name__ == "__main__": - demo.launch(server_name="0.0.0.0", server_port=7860) -``` +- **Load models once at startup** — instantiate a server class or module-level object and reference it from callbacks via closure. +- **Configure with frozen Pydantic models** — model paths, thresholds, and input sizes belong in a `BaseModel(frozen=True)`, not in literals scattered through the layout. +- **Type every callback** — annotated inputs and returns make the component wiring checkable and keep the UI layer honest. +- **Return UI-native types** — PIL Images, JSON-serializable dicts, strings; convert tensors and numpy arrays before returning. +- **Log with Loguru** in prediction functions, model loading, and error handling. +- **Build layouts inside the `with gr.Blocks()` block** — components register with the enclosing context at construction time. +- **Keep `outputs=[...]` ordered to match the return tuple** — Gradio maps positionally, not by name. +- **Validate inputs before inference** — image sizes, file types, and parameter ranges. +- **Bind `0.0.0.0:7860`** so local runs and Hugging Face Spaces behave identically. ## Anti-Patterns @@ -465,3 +110,12 @@ if __name__ == "__main__": Pydantic (frozen config models), Loguru (structured logging), Hugging Face (load Hub models, deploy to Spaces), FastAPI (mount demos), ONNX (low-latency serving), PyTorch Lightning (load checkpoints), Testing (Gradio test client + pytest). + +## Deep dives + +- `references/interface-and-blocks.md` — read when you need the full `gr.Interface` example with config, or a multi-tab `gr.Blocks` demo with webcam streaming and batch processing. +- `references/inputs-and-outputs.md` — read when mixing image/video/text components, choosing `.change()` vs `.click()`, or fanning one input into several outputs. +- `references/model-serving.md` — read when serving a Hub model with `gr.load`, or wrapping a custom ONNX session or PyTorch checkpoint in a startup-loaded server class. +- `references/reusable-components.md` — read when factoring repeated controls into helpers or building side-by-side model comparison layouts. +- `references/flagging-and-feedback.md` — read when collecting user corrections for active learning via a custom `gr.FlaggingCallback`. +- `references/deployment-and-spaces.md` — read when configuring `launch()` for production, mounting the demo inside FastAPI, or shipping to Hugging Face Spaces with Docker. diff --git a/skills/gradio/references/deployment-and-spaces.md b/skills/gradio/references/deployment-and-spaces.md new file mode 100644 index 0000000..38cf3dd --- /dev/null +++ b/skills/gradio/references/deployment-and-spaces.md @@ -0,0 +1,77 @@ +# Deployment and Hugging Face Spaces + +Scope: launch settings for production, mounting a demo inside FastAPI, and packaging the demo for Hugging Face Spaces with Docker. + +## Contents + +- [Launching](#launching) +- [Mounting inside FastAPI](#mounting-inside-fastapi) +- [Hugging Face Spaces](#hugging-face-spaces) + +## Launching + +Launch with production settings; optionally add auth or mount inside FastAPI. +Never use `share=True` in production — host on Spaces, Docker, or behind a proxy. + +```python +def launch_demo(demo: gr.Blocks, share: bool = False) -> None: + demo.launch( + server_name="0.0.0.0", + server_port=7860, + share=share, + show_error=True, + max_threads=10, + auth=None, # or [("admin", "secure_password")] for basic auth + ) +``` + +## Mounting inside FastAPI + +```python +def mount_on_fastapi(demo: gr.Blocks): + from fastapi import FastAPI + + app = FastAPI(title="ML Demo API") + + @app.get("/api/health") + async def health() -> dict[str, str]: + return {"status": "healthy"} + + return gr.mount_gradio_app(app, demo, path="/demo") +``` + +This gives one process serving both a JSON API and the demo UI — useful when the +same model must back a production endpoint and a human-facing playground. + +## Hugging Face Spaces + +`Dockerfile` and `app.py` entry point: + +```dockerfile +FROM python:3.11-slim +WORKDIR /app +RUN apt-get update && apt-get install -y --no-install-recommends curl && rm -rf /var/lib/apt/lists/* +RUN curl -fsSL https://pixi.sh/install.sh | bash +ENV PATH="/root/.pixi/bin:${PATH}" +COPY pixi.toml pixi.lock ./ +RUN pixi install +COPY src/ ./src/ +COPY models/ ./models/ +EXPOSE 7860 +CMD ["pixi", "run", "python", "-m", "src.app", "--server-name", "0.0.0.0", "--server-port", "7860"] +``` + +```python +"""app.py — Hugging Face Spaces entry point.""" + +from src.config import DetectionConfig +from src.demo import build_detection_demo + +demo = build_detection_demo(DetectionConfig()) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", server_port=7860) +``` + +Spaces expects port 7860 and binds `0.0.0.0`; keeping the same values in +`launch_demo` means the local run and the deployed Space behave identically. diff --git a/skills/gradio/references/flagging-and-feedback.md b/skills/gradio/references/flagging-and-feedback.md new file mode 100644 index 0000000..e80b938 --- /dev/null +++ b/skills/gradio/references/flagging-and-feedback.md @@ -0,0 +1,62 @@ +# Flagging and Feedback Collection + +Scope: subclassing `gr.FlaggingCallback` to persist structured user feedback for active learning, and wiring it into a demo. + +Subclass `gr.FlaggingCallback` to persist structured feedback for active learning. + +```python +"""Custom flagging callback for ML feedback collection.""" + +from __future__ import annotations + +from datetime import datetime, timezone +from pathlib import Path + +import gradio as gr +from loguru import logger +from pydantic import BaseModel + + +class FlaggedSample(BaseModel, frozen=True): + timestamp: str + flag_reason: str + input_hash: str + prediction: str + user_correction: str | None = None + + +class MLFlaggingCallback(gr.FlaggingCallback): + def __init__(self, output_dir: str = "flagged_data") -> None: + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def setup(self, components: list, flagging_dir: str | Path) -> None: + self.flagging_dir = Path(flagging_dir) + self.flagging_dir.mkdir(parents=True, exist_ok=True) + + def flag(self, flag_data: list, flag_option: str = "incorrect", username: str | None = None) -> int: + sample = FlaggedSample( + timestamp=datetime.now(tz=timezone.utc).isoformat(), + flag_reason=flag_option, + input_hash=str(hash(str(flag_data[0]))), + prediction=str(flag_data[1]) if len(flag_data) > 1 else "", + user_correction=str(flag_data[2]) if len(flag_data) > 2 else None, + ) + (self.output_dir / f"flag_{sample.timestamp}.json").write_text(sample.model_dump_json(indent=2)) + logger.info("Flagged sample saved (reason: {})", flag_option) + return 1 + + +demo = gr.Interface( + fn=classify_image, + inputs=gr.Image(type="pil"), + outputs=gr.Label(), + flagging_callback=MLFlaggingCallback(output_dir="flagged_data"), + flagging_options=["incorrect", "low_confidence", "interesting"], +) +``` + +`flag_data` arrives as a positional list in the same order as the demo's components +(inputs first, then outputs), which is why the callback indexes defensively. Validate +the record with a frozen Pydantic model before writing so malformed feedback fails at +collection time rather than during retraining. diff --git a/skills/gradio/references/inputs-and-outputs.md b/skills/gradio/references/inputs-and-outputs.md new file mode 100644 index 0000000..fafb05c --- /dev/null +++ b/skills/gradio/references/inputs-and-outputs.md @@ -0,0 +1,47 @@ +# Multi-Modal Inputs and Outputs + +Scope: mixing image/video/text components, wiring `.change()` vs `.click()` events, and fanning one input out to several outputs (or several inputs into one). + +Mix `gr.Image`, `gr.Video`, `gr.Textbox`, `gr.Gallery`, and `gr.JSON`. Wire events +with `.change()` (fire on input change) or `.click()` (fire on button). + +```python +def build_multimodal_demo() -> gr.Blocks: + with gr.Blocks() as demo: + gr.Markdown("# Multi-Modal Analysis") + + with gr.Tab("Image Captioning"): + img_input = gr.Image(type="pil", label="Upload Image") + caption_output = gr.Textbox(label="Generated Caption", lines=3) + img_input.change(fn=generate_caption, inputs=[img_input], outputs=[caption_output]) + + with gr.Tab("Video Analysis"): # one input -> multiple outputs + video_input = gr.Video(label="Upload Video") + video_output = gr.Video(label="Annotated Video") + frame_gallery = gr.Gallery(label="Key Frames", columns=4) + analysis_json = gr.JSON(label="Analysis Results") + gr.Button("Analyze Video", variant="primary").click( + fn=analyze_video, + inputs=[video_input], + outputs=[video_output, frame_gallery, analysis_json], + ) + + with gr.Tab("Visual Question Answering"): # multiple inputs -> one output + with gr.Row(): + vqa_image = gr.Image(type="pil", label="Image") + with gr.Column(): + vqa_question = gr.Textbox(label="Question", placeholder="What is in this image?") + vqa_answer = gr.Textbox(label="Answer", interactive=False) + gr.Button("Ask", variant="primary").click( + fn=answer_question, inputs=[vqa_image, vqa_question], outputs=[vqa_answer], + ) + + return demo +``` + +Key points: + +- The order of `outputs=[...]` must match the order of the tuple your function returns. +- `.change()` fires on every edit, so avoid it for expensive inference — use a button `.click()` instead. +- Mark display-only textboxes `interactive=False` so users cannot type into results. +- Always convert model output to PIL Images, JSON-serializable dicts, or strings before returning — never raw tensors or numpy arrays. diff --git a/skills/gradio/references/interface-and-blocks.md b/skills/gradio/references/interface-and-blocks.md new file mode 100644 index 0000000..4e5dd16 --- /dev/null +++ b/skills/gradio/references/interface-and-blocks.md @@ -0,0 +1,126 @@ +# gr.Interface and gr.Blocks + +Scope: the two top-level demo constructors — a single-function `gr.Interface`, and a multi-tab `gr.Blocks` layout with upload, webcam streaming, and batch processing. + +## Contents + +- [gr.Interface — single-function demos](#grinterface--single-function-demos) +- [gr.Blocks — complex layouts](#grblocks--complex-layouts) + +## gr.Interface — single-function demos + +Fastest way to wrap one prediction function with defined inputs/outputs. + +```python +"""Image classification demo with gr.Interface.""" + +from __future__ import annotations + +import gradio as gr +import numpy as np +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ClassificationConfig(BaseModel, frozen=True): + model_path: str = "models/classifier.onnx" + labels_path: str = "models/labels.txt" + top_k: int = Field(default=5, ge=1, le=20) + image_size: tuple[int, int] = (224, 224) + + +def classify_image( + image: Image.Image, + config: ClassificationConfig = ClassificationConfig(), +) -> dict[str, float]: + """Return top-k predictions for a single image.""" + logger.info("Received image of size {}", image.size) + preprocessed = preprocess(image, config.image_size) + predictions = run_model(preprocessed, config.model_path) + labels = load_labels(config.labels_path) + + top_indices = np.argsort(predictions)[-config.top_k :][::-1] + results = {labels[i]: float(predictions[i]) for i in top_indices} + logger.info("Top prediction: {} ({:.3f})", *next(iter(results.items()))) + return results + + +demo = gr.Interface( + fn=classify_image, + inputs=gr.Image(type="pil", label="Upload Image"), + outputs=gr.Label(num_top_classes=5, label="Predictions"), + title="Image Classifier", + description="Upload an image to classify it.", + examples=[["examples/cat.jpg"], ["examples/dog.jpg"]], + cache_examples=True, +) +``` + +## gr.Blocks — complex layouts + +Standard for production demos. Combines tabs, rows/columns, buttons, streaming, +and batch inputs. The pattern below shows upload, webcam streaming, and batch tabs. + +```python +"""Object detection demo with gr.Blocks layout.""" + +from __future__ import annotations + +import gradio as gr +from loguru import logger +from pydantic import BaseModel, Field + + +class DetectionConfig(BaseModel, frozen=True): + model_path: str = "models/detector.onnx" + confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0) + nms_threshold: float = Field(default=0.45, ge=0.0, le=1.0) + max_detections: int = Field(default=100, ge=1, le=500) + device: str = "cpu" + + +def build_detection_demo(config: DetectionConfig | None = None) -> gr.Blocks: + config = config or DetectionConfig() + logger.info("Building detection demo with model: {}", config.model_path) + + with gr.Blocks(title="Object Detection", theme=gr.themes.Soft()) as demo: + gr.Markdown("# Object Detection Demo") + + with gr.Tab("Image Upload"): + with gr.Row(): + with gr.Column(scale=1): + input_image = gr.Image(type="pil", label="Input Image") + confidence_slider = gr.Slider( + 0.0, 1.0, value=config.confidence_threshold, + step=0.05, label="Confidence Threshold", + ) + detect_btn = gr.Button("Detect Objects", variant="primary") + with gr.Column(scale=1): + output_image = gr.Image(type="pil", label="Detections") + output_json = gr.JSON(label="Detection Results") + detect_btn.click( + fn=run_detection, + inputs=[input_image, confidence_slider], + outputs=[output_image, output_json], + ) + + with gr.Tab("Webcam"): # .stream() for real-time per-frame processing + webcam_input = gr.Image(sources=["webcam"], type="pil", label="Webcam Feed") + webcam_output = gr.Image(type="pil", label="Live Detections") + webcam_input.stream( + fn=run_detection_realtime, inputs=[webcam_input], outputs=[webcam_output], + ) + + with gr.Tab("Batch Processing"): # gr.File(multiple) + gr.Gallery output + batch_input = gr.File(file_count="multiple", file_types=["image"], label="Images") + batch_output = gr.Gallery(label="Results", columns=3) + batch_btn = gr.Button("Process Batch", variant="primary") + batch_btn.click( + fn=run_batch_detection, + inputs=[batch_input, confidence_slider], + outputs=[batch_output], + ) + + return demo +``` diff --git a/skills/gradio/references/model-serving.md b/skills/gradio/references/model-serving.md new file mode 100644 index 0000000..6af11e1 --- /dev/null +++ b/skills/gradio/references/model-serving.md @@ -0,0 +1,128 @@ +# Model Serving in Gradio + +Scope: loading a Hub model with `gr.load`, and wrapping custom ONNX or PyTorch checkpoints in a server class loaded once at startup. + +## Contents + +- [From the Hugging Face Hub with gr.load](#from-the-hugging-face-hub-with-grload) +- [Custom ONNX / PyTorch model server](#custom-onnx--pytorch-model-server) +- [PyTorch model with @torch.inference_mode](#pytorch-model-with-torchinference_mode) + +## From the Hugging Face Hub with gr.load + +```python +demo = gr.load( + name="facebook/detr-resnet-50", + src="models", + title="DETR Object Detection", + description="Detect objects using DETR.", +) +``` + +## Custom ONNX / PyTorch model server + +Wrap the model in a class loaded once at startup; reference it via closure in the +callback. Same pattern applies to a PyTorch checkpoint (swap `ort.InferenceSession` +for `torch.load(...).eval()` and preprocess with `torchvision.transforms`). + +```python +"""Serve a custom ONNX model through Gradio.""" + +from __future__ import annotations + +from pathlib import Path + +import gradio as gr +import numpy as np +import onnxruntime as ort +from loguru import logger +from PIL import Image +from pydantic import BaseModel, Field + + +class ONNXModelConfig(BaseModel, frozen=True): + model_path: Path = Path("models/model.onnx") + input_size: tuple[int, int] = (640, 640) + providers: list[str] = Field( + default_factory=lambda: ["CUDAExecutionProvider", "CPUExecutionProvider"] + ) + + +class ModelServer: + """Wraps an ONNX model for Gradio serving (loaded once at startup).""" + + def __init__(self, config: ONNXModelConfig) -> None: + self.config = config + logger.info("Loading ONNX model from {}", config.model_path) + self.session = ort.InferenceSession(str(config.model_path), providers=config.providers) + self.input_name = self.session.get_inputs()[0].name + + def predict(self, image: Image.Image) -> tuple[Image.Image, dict]: + """Run inference, return annotated image and results dict.""" + input_array = self._preprocess(image) + outputs = self.session.run(None, {self.input_name: input_array}) + detections = self._postprocess(outputs, image.size) + annotated = self._draw_detections(image, detections) + logger.info("Found {} detections", len(detections)) + return annotated, {"num_detections": len(detections), "detections": detections} + + def _preprocess(self, image: Image.Image) -> np.ndarray: + resized = image.resize(self.config.input_size) + array = np.array(resized, dtype=np.float32) / 255.0 + return np.transpose(array, (2, 0, 1))[np.newaxis, ...] + + def _postprocess(self, outputs: list, original_size: tuple[int, int]) -> list[dict]: + ... # depends on model architecture + + def _draw_detections(self, image: Image.Image, detections: list[dict]) -> Image.Image: + ... # uses PIL.ImageDraw + + +def create_onnx_demo(config: ONNXModelConfig | None = None) -> gr.Blocks: + server = ModelServer(config or ONNXModelConfig()) + with gr.Blocks(title="ONNX Model Demo") as demo: + gr.Markdown("# Custom ONNX Model Demo") + with gr.Row(): + input_img = gr.Image(type="pil", label="Input") + output_img = gr.Image(type="pil", label="Output") + results_json = gr.JSON(label="Results") + gr.Button("Run Inference", variant="primary").click( + fn=server.predict, inputs=[input_img], outputs=[output_img, results_json], + ) + return demo +``` + +## PyTorch model with @torch.inference_mode + +```python +from torchvision import transforms + + +def create_pytorch_demo(config: TorchModelConfig) -> gr.Blocks: + model = torch.load(config.checkpoint_path, map_location=config.device) + model.eval() + transform = transforms.Compose([ + transforms.Resize(config.input_size), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + @torch.inference_mode() + def predict(image: Image.Image) -> dict[str, float]: + tensor = transform(image).unsqueeze(0).to(config.device) + probs = torch.nn.functional.softmax(model(tensor)[0], dim=0) + top5_prob, top5_idx = torch.topk(probs, 5) + return { + (config.class_names[idx] if config.class_names else f"class_{idx}"): float(prob) + for prob, idx in zip(top5_prob, top5_idx) + } + + with gr.Blocks(title="PyTorch Model Demo") as demo: + with gr.Row(): + input_img = gr.Image(type="pil", label="Upload Image") + output_label = gr.Label(num_top_classes=5, label="Predictions") + gr.Button("Classify", variant="primary").click( + fn=predict, inputs=[input_img], outputs=[output_label], + ) + return demo +``` diff --git a/skills/gradio/references/reusable-components.md b/skills/gradio/references/reusable-components.md new file mode 100644 index 0000000..5f3807f --- /dev/null +++ b/skills/gradio/references/reusable-components.md @@ -0,0 +1,45 @@ +# Reusable Components and Comparison Layouts + +Scope: factoring repeated controls into helper functions that return components, and using them to build side-by-side model comparison layouts. + +Factor repeated controls into helper functions returning components. Reuse them to +build side-by-side comparison layouts. + +```python +def create_model_selector(models: dict[str, str], default: str | None = None) -> gr.Dropdown: + choices = list(models.keys()) + return gr.Dropdown(choices=choices, value=default or choices[0], label="Select Model") + + +def create_preprocessing_controls() -> tuple[gr.Slider, gr.Slider, gr.Checkbox]: + brightness = gr.Slider(-1.0, 1.0, value=0.0, step=0.1, label="Brightness") + contrast = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Contrast") + grayscale = gr.Checkbox(value=False, label="Convert to Grayscale") + return brightness, contrast, grayscale + + +def build_comparison_layout() -> gr.Blocks: + with gr.Blocks() as demo: + input_image = gr.Image(type="pil", label="Input Image") + with gr.Row(): + with gr.Column(): + gr.Markdown("### Model A") + model_a = create_model_selector({"YOLOv8-n": "yolov8n", "YOLOv8-s": "yolov8s"}) + output_a = gr.Image(type="pil", label="Model A Output") + metrics_a = gr.JSON(label="Model A Metrics") + with gr.Column(): + gr.Markdown("### Model B") + model_b = create_model_selector({"YOLOv8-m": "yolov8m", "YOLOv8-l": "yolov8l"}) + output_b = gr.Image(type="pil", label="Model B Output") + metrics_b = gr.JSON(label="Model B Metrics") + gr.Button("Compare Models", variant="primary").click( + fn=compare_models, + inputs=[input_image, model_a, model_b], + outputs=[output_a, metrics_a, output_b, metrics_b], + ) + return demo +``` + +Helper functions must be called *inside* the `gr.Blocks` context — Gradio components +register themselves with the enclosing block at construction time, so a component +built outside the `with` block will not appear in the demo. diff --git a/skills/gradio/skill.toml b/skills/gradio/skill.toml index f9d8c3b..3becc4e 100644 --- a/skills/gradio/skill.toml +++ b/skills/gradio/skill.toml @@ -2,6 +2,7 @@ name = "gradio" version = "1.0.0" category = "infra" +tier = "extra" tags = ["demo", "ui", "gradio", "prototyping", "serving"] [dependencies] diff --git a/skills/huggingface/SKILL.md b/skills/huggingface/SKILL.md index 401be80..fe5703d 100644 --- a/skills/huggingface/SKILL.md +++ b/skills/huggingface/SKILL.md @@ -11,451 +11,99 @@ description: > # Hugging Face Skill -Patterns for the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Use `Auto*` classes for loading (never hardcode model class names), the `datasets` library for data, and the `Trainer` API for fine-tuning unless you need a custom loop. +Patterns for the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Use +`Auto*` classes for loading (never hardcode model class names), the `datasets` library for +data, and the `Trainer` API for fine-tuning unless you need a custom loop. -## Model Loading +## Essential Core -### AutoModel Pattern +Load a pretrained model with its processor/tokenizer via `Auto*` classes, pinning `revision`: ```python -"""Loading pretrained models with Hugging Face Transformers.""" - -from __future__ import annotations - import torch -from transformers import ( - AutoConfig, - AutoModel, - AutoModelForImageClassification, - AutoModelForObjectDetection, - AutoModelForSequenceClassification, - AutoTokenizer, - AutoImageProcessor, +from transformers import AutoImageProcessor, AutoModelForImageClassification + +processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50", revision="main") +model = AutoModelForImageClassification.from_pretrained( + "microsoft/resnet-50", + revision="main", + torch_dtype=torch.float32, + cache_dir=None, # set to share a cache across runs + trust_remote_code=False, # only True for trusted sources ) -from pydantic import BaseModel, Field -from loguru import logger - - -class ModelConfig(BaseModel, frozen=True): - """Hugging Face model configuration.""" - - model_name: str = "microsoft/resnet-50" - revision: str = "main" - torch_dtype: str = "float32" - device_map: str | None = None - trust_remote_code: bool = False - cache_dir: str | None = None - - -def load_vision_model(config: ModelConfig) -> tuple: - """Load a vision model and its image processor.""" - logger.info("Loading model: {}", config.model_name) - - processor = AutoImageProcessor.from_pretrained( - config.model_name, - revision=config.revision, - cache_dir=config.cache_dir, - ) - - model = AutoModelForImageClassification.from_pretrained( - config.model_name, - revision=config.revision, - torch_dtype=getattr(torch, config.torch_dtype), - device_map=config.device_map, - trust_remote_code=config.trust_remote_code, - cache_dir=config.cache_dir, - ) - - logger.info("Model loaded: {} parameters", sum(p.numel() for p in model.parameters())) - return model, processor - - -def load_text_model(config: ModelConfig) -> tuple: - """Load a text model and tokenizer (same shape as the vision path).""" - tokenizer = AutoTokenizer.from_pretrained(config.model_name, revision=config.revision) - model = AutoModelForSequenceClassification.from_pretrained( - config.model_name, - revision=config.revision, - torch_dtype=getattr(torch, config.torch_dtype), - device_map=config.device_map, - cache_dir=config.cache_dir, - ) - return model, tokenizer ``` -## Datasets Library - -### Loading and Processing Datasets +For quick inference, skip the manual wiring and use a task `pipeline`: ```python -"""Dataset loading and preprocessing with Hugging Face datasets.""" - -from __future__ import annotations - -from datasets import Dataset, DatasetDict, load_dataset -from transformers import AutoImageProcessor, AutoTokenizer -from loguru import logger - - -def load_image_dataset( - dataset_name: str, - processor: AutoImageProcessor, - split: str | None = None, -) -> DatasetDict | Dataset: - """Load and preprocess an image classification dataset.""" - dataset = load_dataset(dataset_name, split=split) - logger.info("Loaded dataset: {} rows", len(dataset) if isinstance(dataset, Dataset) else sum(len(s) for s in dataset.values())) - - def preprocess(batch: dict) -> dict: - images = batch["image"] - inputs = processor(images=images, return_tensors="pt") - inputs["labels"] = batch["label"] - return inputs - - processed = dataset.map( - preprocess, - batched=True, - batch_size=32, - remove_columns=dataset.column_names if isinstance(dataset, Dataset) else dataset["train"].column_names, - ) - - processed.set_format("torch") - return processed - - -def load_text_dataset( - dataset_name: str, - tokenizer: AutoTokenizer, - max_length: int = 512, -) -> DatasetDict: - """Load and tokenize a text dataset.""" - dataset = load_dataset(dataset_name) - - def tokenize(batch: dict) -> dict: - return tokenizer( - batch["text"], - padding="max_length", - truncation=True, - max_length=max_length, - return_tensors="pt", - ) - - tokenized = dataset.map(tokenize, batched=True, remove_columns=["text"]) - tokenized.set_format("torch") - return tokenized - +from transformers import pipeline -def create_custom_dataset( - data_dir: str, - image_column: str = "image", - label_column: str = "label", -) -> Dataset: - """Create a dataset from a local directory of images.""" - dataset = load_dataset( - "imagefolder", - data_dir=data_dir, - ) - logger.info("Created dataset from {}: {} images", data_dir, len(dataset["train"])) - return dataset["train"] +classifier = pipeline("image-classification", model="microsoft/resnet-50", device=0) +results = classifier("path/to/image.jpg") +# -> [{"label": "cat", "score": 0.97}, ...] ``` -## Fine-Tuning with Trainer - -### Standard Trainer Configuration +Load data with `datasets` and map preprocessing in batches: ```python -"""Fine-tuning with Hugging Face Trainer API.""" +from datasets import load_dataset -from __future__ import annotations - -from pathlib import Path - -import evaluate -import numpy as np -from pydantic import BaseModel, Field -from transformers import ( - AutoModelForImageClassification, - AutoImageProcessor, - EarlyStoppingCallback, - Trainer, - TrainingArguments, +dataset = load_dataset("cifar10") +dataset = dataset.map( + lambda batch: processor(images=batch["img"], return_tensors="pt"), + batched=True, + batch_size=32, ) -from loguru import logger - - -class FinetuneConfig(BaseModel, frozen=True): - """Fine-tuning configuration.""" - - model_name: str = "microsoft/resnet-50" - output_dir: str = "outputs/finetuned" - num_train_epochs: int = 10 - per_device_train_batch_size: int = 32 - per_device_eval_batch_size: int = 64 - learning_rate: float = 5e-5 - weight_decay: float = 0.01 - warmup_ratio: float = 0.1 - fp16: bool = True - eval_strategy: str = "epoch" - save_strategy: str = "epoch" - load_best_model_at_end: bool = True - metric_for_best_model: str = "accuracy" - push_to_hub: bool = False - hub_model_id: str | None = None - - -def create_training_args(config: FinetuneConfig) -> TrainingArguments: - """Map config fields directly onto TrainingArguments, plus fixed extras.""" - return TrainingArguments( - **config.model_dump(), - logging_steps=50, - dataloader_num_workers=4, - dataloader_pin_memory=True, - report_to=["tensorboard"], - ) - - -def compute_metrics(eval_pred) -> dict[str, float]: - """Compute accuracy for evaluation.""" - accuracy = evaluate.load("accuracy") - predictions, labels = eval_pred - predicted = np.argmax(predictions, axis=1) - return accuracy.compute(predictions=predicted, references=labels) - - -def finetune( - config: FinetuneConfig, - train_dataset, - eval_dataset, - num_labels: int, -) -> str: - """Fine-tune a pretrained model.""" - logger.info("Fine-tuning {} for {} epochs", config.model_name, config.num_train_epochs) - - model = AutoModelForImageClassification.from_pretrained( - config.model_name, - num_labels=num_labels, - ignore_mismatched_sizes=True, - ) - - training_args = create_training_args(config) - - trainer = Trainer( - model=model, - args=training_args, - train_dataset=train_dataset, - eval_dataset=eval_dataset, - compute_metrics=compute_metrics, - callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], - ) - - trainer.train() - - # Save final model - output_path = Path(config.output_dir) / "best" - trainer.save_model(str(output_path)) - logger.info("Best model saved to {}", output_path) - - return str(output_path) +dataset.set_format("torch") ``` -## PEFT / LoRA - -### Parameter-Efficient Fine-Tuning +Fine-tune with `Trainer` rather than a hand-rolled loop unless you need custom behaviour: ```python -"""PEFT fine-tuning with LoRA adapters.""" - -from __future__ import annotations - -from peft import ( - LoraConfig, - TaskType, - get_peft_model, - PeftModel, +from transformers import Trainer, TrainingArguments + +trainer = Trainer( + model=model, + args=TrainingArguments( + output_dir="outputs/finetuned", + num_train_epochs=10, + per_device_train_batch_size=32, + learning_rate=5e-5, + fp16=True, + eval_strategy="epoch", + save_strategy="epoch", + load_best_model_at_end=True, + report_to=["tensorboard"], + ), + train_dataset=train_dataset, + eval_dataset=eval_dataset, + compute_metrics=compute_metrics, ) -from transformers import AutoModelForSequenceClassification -from loguru import logger - - -def create_lora_model( - model_name: str, - num_labels: int, - lora_r: int = 16, - lora_alpha: int = 32, - lora_dropout: float = 0.1, - target_modules: list[str] | None = None, -) -> PeftModel: - """Create a LoRA-adapted model for fine-tuning.""" - base_model = AutoModelForSequenceClassification.from_pretrained( - model_name, - num_labels=num_labels, - ) - - lora_config = LoraConfig( - task_type=TaskType.SEQ_CLS, - r=lora_r, - lora_alpha=lora_alpha, - lora_dropout=lora_dropout, - target_modules=target_modules or ["query", "value"], - bias="none", - ) - - model = get_peft_model(base_model, lora_config) - - trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) - total = sum(p.numel() for p in model.parameters()) - logger.info( - "LoRA model: {:.1f}M trainable / {:.1f}M total ({:.1f}%)", - trainable / 1e6, - total / 1e6, - 100 * trainable / total, - ) - - return model - - -def load_lora_model( - base_model_name: str, - adapter_path: str, - num_labels: int, -) -> PeftModel: - """Load a saved LoRA adapter on top of a base model.""" - base_model = AutoModelForSequenceClassification.from_pretrained( - base_model_name, - num_labels=num_labels, - ) - model = PeftModel.from_pretrained(base_model, adapter_path) - return model -``` - -## Pipelines for Quick Inference - -### Using Pipeline API - -```python -"""Hugging Face pipeline patterns for quick inference.""" - -from __future__ import annotations - -from transformers import pipeline - - -def create_image_classifier( - model_name: str = "microsoft/resnet-50", - device: int = 0, -) -> pipeline: - """Create an image classification pipeline.""" - return pipeline("image-classification", model=model_name, device=device) - - -# Same pattern for other tasks — just change the task string and default model: -# "object-detection" (facebook/detr-resnet-50, add threshold=0.5) -# "zero-shot-image-classification" (openai/clip-vit-base-patch32) -def create_object_detector(model_name: str = "facebook/detr-resnet-50", device: int = 0) -> pipeline: - """Create an object detection pipeline.""" - return pipeline("object-detection", model=model_name, device=device, threshold=0.5) - - -# Usage: classifier(...) -> [{"label": "cat", "score": 0.97}, ...] -# detector(...) -> [{"label": "person", "score": 0.99, "box": {...}}, ...] -classifier = create_image_classifier() -results = classifier("path/to/image.jpg") +trainer.train() +trainer.save_model("outputs/finetuned/best") ``` -## Model Hub Publishing +## Which Reference for Which Task -### Pushing Models to the Hub +| Task | Entry point | Detail in | +|------|-------------|-----------| +| Load a model | `AutoModelFor*.from_pretrained` | `references/transformers-models.md` | +| Load/preprocess data | `load_dataset`, `.map` | `references/datasets.md` | +| Full fine-tuning run | `Trainer`, `TrainingArguments` | `references/fine-tuning-with-trainer.md` | +| Cheap fine-tuning | `LoraConfig`, `get_peft_model` | `references/peft-lora.md` | +| Share a model | `push_to_hub`, `ModelCard` | `references/hub-publishing.md` | +| Fast/small inference | `pipeline`, `BitsAndBytesConfig` | `references/inference-optimization.md` | -```python -"""Publishing models and datasets to Hugging Face Hub.""" - -from __future__ import annotations - -from pathlib import Path - -from huggingface_hub import HfApi, ModelCard, ModelCardData -from transformers import AutoModel, AutoTokenizer -from loguru import logger - - -def push_model_to_hub( - model_path: str, - repo_id: str, - private: bool = True, -) -> str: - """Push a trained model to Hugging Face Hub.""" - model = AutoModel.from_pretrained(model_path) - model.push_to_hub(repo_id, private=private) - - # Push tokenizer/processor if present - try: - tokenizer = AutoTokenizer.from_pretrained(model_path) - tokenizer.push_to_hub(repo_id, private=private) - except Exception: - pass - - logger.info("Model pushed to hub: {}", repo_id) - return f"https://huggingface.co/{repo_id}" - - -def create_model_card( - repo_id: str, model_name: str, dataset_name: str, metrics: dict[str, float] -) -> ModelCard: - """Create and push a model card (always include license, dataset, metrics).""" - card_data = ModelCardData( - language="en", - license="apache-2.0", - model_name=model_name, - datasets=[dataset_name], - metrics=list(metrics.keys()), - ) - card = ModelCard.from_template( - card_data, - model_id=repo_id, - model_description=f"Fine-tuned {model_name} on {dataset_name}.", - training_metrics=metrics, - ) - card.push_to_hub(repo_id) - return card -``` - -## Quantization and Optimization +## Conventions -### Model Quantization for Faster Inference - -```python -"""Model quantization with Hugging Face Optimum.""" - -from __future__ import annotations - -import torch -from transformers import AutoModelForImageClassification, BitsAndBytesConfig -from loguru import logger - - -def load_quantized_model( - model_name: str, - num_labels: int, - load_in_4bit: bool = True, -) -> AutoModelForImageClassification: - """Load a 4-bit or 8-bit quantized model.""" - bnb_config = BitsAndBytesConfig( - load_in_4bit=load_in_4bit, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - ) if load_in_4bit else BitsAndBytesConfig(load_in_8bit=True) - - model = AutoModelForImageClassification.from_pretrained( - model_name, - num_labels=num_labels, - quantization_config=bnb_config, - device_map="auto", - ) - - logger.info("Loaded quantized model ({} bit)", "4" if load_in_4bit else "8") - return model -``` +- Use `AutoModel*`, `AutoTokenizer`, `AutoImageProcessor` — never concrete class names. +- Pin `revision` on every `from_pretrained` call for reproducibility. +- Load models once at initialization; pass a `cache_dir` so repeated runs do not re-download. +- Preprocess with `dataset.map(..., batched=True)` and `set_format("torch")`. +- Drive `TrainingArguments` from a frozen Pydantic config so runs are typed and serializable. +- Prefer PEFT/LoRA over full fine-tuning when data is limited. +- Publish with a model card that carries license, dataset, and metrics. +- Log with Loguru; route Trainer telemetry with `report_to=["tensorboard"]` or `["wandb"]`. ## Anti-Patterns @@ -469,4 +117,14 @@ def load_quantized_model( ## Integration with Other Skills -Wrap HF models in a Lightning Trainer for custom loops; set `report_to=["wandb"]` for tracking; export via `optimum`/ONNX for inference; deploy on SageMaker (HF DLC) or behind FastAPI. +Wrap HF models in a Lightning Trainer for custom loops; set `report_to=["wandb"]` for tracking; +export via `optimum`/ONNX for inference; deploy on SageMaker (HF DLC) or behind FastAPI. + +## Deep dives + +- `references/transformers-models.md` — read when loading vision or text models with a typed config, or wiring `device_map`/`torch_dtype`. +- `references/datasets.md` — read when loading, preprocessing, or tokenizing datasets, or building one from a local image folder. +- `references/fine-tuning-with-trainer.md` — read when setting up `TrainingArguments`, `compute_metrics`, or a full `Trainer` run with early stopping. +- `references/peft-lora.md` — read when applying LoRA adapters or reloading a saved adapter onto a base model. +- `references/hub-publishing.md` — read when pushing a model to the Hub or generating a model card. +- `references/inference-optimization.md` — read when building task pipelines or loading 4-bit/8-bit quantized models. diff --git a/skills/huggingface/references/datasets.md b/skills/huggingface/references/datasets.md new file mode 100644 index 0000000..4184f09 --- /dev/null +++ b/skills/huggingface/references/datasets.md @@ -0,0 +1,77 @@ +# Hugging Face Datasets + +Scope: loading, preprocessing, and tokenizing datasets with the `datasets` library, including local image folders. + +## Loading and Processing Datasets + +```python +"""Dataset loading and preprocessing with Hugging Face datasets.""" + +from __future__ import annotations + +from datasets import Dataset, DatasetDict, load_dataset +from transformers import AutoImageProcessor, AutoTokenizer +from loguru import logger + + +def load_image_dataset( + dataset_name: str, + processor: AutoImageProcessor, + split: str | None = None, +) -> DatasetDict | Dataset: + """Load and preprocess an image classification dataset.""" + dataset = load_dataset(dataset_name, split=split) + logger.info("Loaded dataset: {} rows", len(dataset) if isinstance(dataset, Dataset) else sum(len(s) for s in dataset.values())) + + def preprocess(batch: dict) -> dict: + images = batch["image"] + inputs = processor(images=images, return_tensors="pt") + inputs["labels"] = batch["label"] + return inputs + + processed = dataset.map( + preprocess, + batched=True, + batch_size=32, + remove_columns=dataset.column_names if isinstance(dataset, Dataset) else dataset["train"].column_names, + ) + + processed.set_format("torch") + return processed + + +def load_text_dataset( + dataset_name: str, + tokenizer: AutoTokenizer, + max_length: int = 512, +) -> DatasetDict: + """Load and tokenize a text dataset.""" + dataset = load_dataset(dataset_name) + + def tokenize(batch: dict) -> dict: + return tokenizer( + batch["text"], + padding="max_length", + truncation=True, + max_length=max_length, + return_tensors="pt", + ) + + tokenized = dataset.map(tokenize, batched=True, remove_columns=["text"]) + tokenized.set_format("torch") + return tokenized + + +def create_custom_dataset( + data_dir: str, + image_column: str = "image", + label_column: str = "label", +) -> Dataset: + """Create a dataset from a local directory of images.""" + dataset = load_dataset( + "imagefolder", + data_dir=data_dir, + ) + logger.info("Created dataset from {}: {} images", data_dir, len(dataset["train"])) + return dataset["train"] +``` diff --git a/skills/huggingface/references/fine-tuning-with-trainer.md b/skills/huggingface/references/fine-tuning-with-trainer.md new file mode 100644 index 0000000..6e01791 --- /dev/null +++ b/skills/huggingface/references/fine-tuning-with-trainer.md @@ -0,0 +1,100 @@ +# Fine-Tuning with the Trainer API + +Scope: a typed `TrainingArguments` config, metric computation, and a complete `Trainer` fine-tuning run with early stopping. + +## Standard Trainer Configuration + +```python +"""Fine-tuning with Hugging Face Trainer API.""" + +from __future__ import annotations + +from pathlib import Path + +import evaluate +import numpy as np +from pydantic import BaseModel, Field +from transformers import ( + AutoModelForImageClassification, + AutoImageProcessor, + EarlyStoppingCallback, + Trainer, + TrainingArguments, +) +from loguru import logger + + +class FinetuneConfig(BaseModel, frozen=True): + """Fine-tuning configuration.""" + + model_name: str = "microsoft/resnet-50" + output_dir: str = "outputs/finetuned" + num_train_epochs: int = 10 + per_device_train_batch_size: int = 32 + per_device_eval_batch_size: int = 64 + learning_rate: float = 5e-5 + weight_decay: float = 0.01 + warmup_ratio: float = 0.1 + fp16: bool = True + eval_strategy: str = "epoch" + save_strategy: str = "epoch" + load_best_model_at_end: bool = True + metric_for_best_model: str = "accuracy" + push_to_hub: bool = False + hub_model_id: str | None = None + + +def create_training_args(config: FinetuneConfig) -> TrainingArguments: + """Map config fields directly onto TrainingArguments, plus fixed extras.""" + return TrainingArguments( + **config.model_dump(), + logging_steps=50, + dataloader_num_workers=4, + dataloader_pin_memory=True, + report_to=["tensorboard"], + ) + + +def compute_metrics(eval_pred) -> dict[str, float]: + """Compute accuracy for evaluation.""" + accuracy = evaluate.load("accuracy") + predictions, labels = eval_pred + predicted = np.argmax(predictions, axis=1) + return accuracy.compute(predictions=predicted, references=labels) + + +def finetune( + config: FinetuneConfig, + train_dataset, + eval_dataset, + num_labels: int, +) -> str: + """Fine-tune a pretrained model.""" + logger.info("Fine-tuning {} for {} epochs", config.model_name, config.num_train_epochs) + + model = AutoModelForImageClassification.from_pretrained( + config.model_name, + num_labels=num_labels, + ignore_mismatched_sizes=True, + ) + + training_args = create_training_args(config) + + trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + compute_metrics=compute_metrics, + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], + ) + + trainer.train() + + # Save final model + output_path = Path(config.output_dir) / "best" + trainer.save_model(str(output_path)) + logger.info("Best model saved to {}", output_path) + + return str(output_path) +``` diff --git a/skills/huggingface/references/hub-publishing.md b/skills/huggingface/references/hub-publishing.md new file mode 100644 index 0000000..74075c5 --- /dev/null +++ b/skills/huggingface/references/hub-publishing.md @@ -0,0 +1,58 @@ +# Publishing to the Hugging Face Hub + +Scope: pushing trained models (and their tokenizer/processor) to the Hub and generating a model card. + +## Pushing Models to the Hub + +```python +"""Publishing models and datasets to Hugging Face Hub.""" + +from __future__ import annotations + +from pathlib import Path + +from huggingface_hub import HfApi, ModelCard, ModelCardData +from transformers import AutoModel, AutoTokenizer +from loguru import logger + + +def push_model_to_hub( + model_path: str, + repo_id: str, + private: bool = True, +) -> str: + """Push a trained model to Hugging Face Hub.""" + model = AutoModel.from_pretrained(model_path) + model.push_to_hub(repo_id, private=private) + + # Push tokenizer/processor if present + try: + tokenizer = AutoTokenizer.from_pretrained(model_path) + tokenizer.push_to_hub(repo_id, private=private) + except Exception: + pass + + logger.info("Model pushed to hub: {}", repo_id) + return f"https://huggingface.co/{repo_id}" + + +def create_model_card( + repo_id: str, model_name: str, dataset_name: str, metrics: dict[str, float] +) -> ModelCard: + """Create and push a model card (always include license, dataset, metrics).""" + card_data = ModelCardData( + language="en", + license="apache-2.0", + model_name=model_name, + datasets=[dataset_name], + metrics=list(metrics.keys()), + ) + card = ModelCard.from_template( + card_data, + model_id=repo_id, + model_description=f"Fine-tuned {model_name} on {dataset_name}.", + training_metrics=metrics, + ) + card.push_to_hub(repo_id) + return card +``` diff --git a/skills/huggingface/references/inference-optimization.md b/skills/huggingface/references/inference-optimization.md new file mode 100644 index 0000000..f17f4d7 --- /dev/null +++ b/skills/huggingface/references/inference-optimization.md @@ -0,0 +1,76 @@ +# Inference: Pipelines and Quantization + +Scope: the `pipeline` API for quick task inference, and 4-bit/8-bit quantized model loading for faster, smaller inference. + +## Contents + +- [Using Pipeline API](#using-pipeline-api) +- [Model Quantization for Faster Inference](#model-quantization-for-faster-inference) + +## Using Pipeline API + +```python +"""Hugging Face pipeline patterns for quick inference.""" + +from __future__ import annotations + +from transformers import pipeline + + +def create_image_classifier( + model_name: str = "microsoft/resnet-50", + device: int = 0, +) -> pipeline: + """Create an image classification pipeline.""" + return pipeline("image-classification", model=model_name, device=device) + + +# Same pattern for other tasks — just change the task string and default model: +# "object-detection" (facebook/detr-resnet-50, add threshold=0.5) +# "zero-shot-image-classification" (openai/clip-vit-base-patch32) +def create_object_detector(model_name: str = "facebook/detr-resnet-50", device: int = 0) -> pipeline: + """Create an object detection pipeline.""" + return pipeline("object-detection", model=model_name, device=device, threshold=0.5) + + +# Usage: classifier(...) -> [{"label": "cat", "score": 0.97}, ...] +# detector(...) -> [{"label": "person", "score": 0.99, "box": {...}}, ...] +classifier = create_image_classifier() +results = classifier("path/to/image.jpg") +``` + +## Model Quantization for Faster Inference + +```python +"""Model quantization with Hugging Face Optimum.""" + +from __future__ import annotations + +import torch +from transformers import AutoModelForImageClassification, BitsAndBytesConfig +from loguru import logger + + +def load_quantized_model( + model_name: str, + num_labels: int, + load_in_4bit: bool = True, +) -> AutoModelForImageClassification: + """Load a 4-bit or 8-bit quantized model.""" + bnb_config = BitsAndBytesConfig( + load_in_4bit=load_in_4bit, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_use_double_quant=True, + ) if load_in_4bit else BitsAndBytesConfig(load_in_8bit=True) + + model = AutoModelForImageClassification.from_pretrained( + model_name, + num_labels=num_labels, + quantization_config=bnb_config, + device_map="auto", + ) + + logger.info("Loaded quantized model ({} bit)", "4" if load_in_4bit else "8") + return model +``` diff --git a/skills/huggingface/references/peft-lora.md b/skills/huggingface/references/peft-lora.md new file mode 100644 index 0000000..7c9c254 --- /dev/null +++ b/skills/huggingface/references/peft-lora.md @@ -0,0 +1,71 @@ +# PEFT / LoRA Adapters + +Scope: parameter-efficient fine-tuning — wrapping a base model with a LoRA adapter and reloading a saved adapter. + +## Parameter-Efficient Fine-Tuning + +```python +"""PEFT fine-tuning with LoRA adapters.""" + +from __future__ import annotations + +from peft import ( + LoraConfig, + TaskType, + get_peft_model, + PeftModel, +) +from transformers import AutoModelForSequenceClassification +from loguru import logger + + +def create_lora_model( + model_name: str, + num_labels: int, + lora_r: int = 16, + lora_alpha: int = 32, + lora_dropout: float = 0.1, + target_modules: list[str] | None = None, +) -> PeftModel: + """Create a LoRA-adapted model for fine-tuning.""" + base_model = AutoModelForSequenceClassification.from_pretrained( + model_name, + num_labels=num_labels, + ) + + lora_config = LoraConfig( + task_type=TaskType.SEQ_CLS, + r=lora_r, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + target_modules=target_modules or ["query", "value"], + bias="none", + ) + + model = get_peft_model(base_model, lora_config) + + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = sum(p.numel() for p in model.parameters()) + logger.info( + "LoRA model: {:.1f}M trainable / {:.1f}M total ({:.1f}%)", + trainable / 1e6, + total / 1e6, + 100 * trainable / total, + ) + + return model + + +def load_lora_model( + base_model_name: str, + adapter_path: str, + num_labels: int, +) -> PeftModel: + """Load a saved LoRA adapter on top of a base model.""" + base_model = AutoModelForSequenceClassification.from_pretrained( + base_model_name, + num_labels=num_labels, + ) + model = PeftModel.from_pretrained(base_model, adapter_path) + return model +``` diff --git a/skills/huggingface/references/transformers-models.md b/skills/huggingface/references/transformers-models.md new file mode 100644 index 0000000..c4fbdff --- /dev/null +++ b/skills/huggingface/references/transformers-models.md @@ -0,0 +1,71 @@ +# Loading Transformers Models + +Scope: the `Auto*` loading pattern for vision and text models, with a typed Pydantic model config. + +## AutoModel Pattern + +```python +"""Loading pretrained models with Hugging Face Transformers.""" + +from __future__ import annotations + +import torch +from transformers import ( + AutoConfig, + AutoModel, + AutoModelForImageClassification, + AutoModelForObjectDetection, + AutoModelForSequenceClassification, + AutoTokenizer, + AutoImageProcessor, +) +from pydantic import BaseModel, Field +from loguru import logger + + +class ModelConfig(BaseModel, frozen=True): + """Hugging Face model configuration.""" + + model_name: str = "microsoft/resnet-50" + revision: str = "main" + torch_dtype: str = "float32" + device_map: str | None = None + trust_remote_code: bool = False + cache_dir: str | None = None + + +def load_vision_model(config: ModelConfig) -> tuple: + """Load a vision model and its image processor.""" + logger.info("Loading model: {}", config.model_name) + + processor = AutoImageProcessor.from_pretrained( + config.model_name, + revision=config.revision, + cache_dir=config.cache_dir, + ) + + model = AutoModelForImageClassification.from_pretrained( + config.model_name, + revision=config.revision, + torch_dtype=getattr(torch, config.torch_dtype), + device_map=config.device_map, + trust_remote_code=config.trust_remote_code, + cache_dir=config.cache_dir, + ) + + logger.info("Model loaded: {} parameters", sum(p.numel() for p in model.parameters())) + return model, processor + + +def load_text_model(config: ModelConfig) -> tuple: + """Load a text model and tokenizer (same shape as the vision path).""" + tokenizer = AutoTokenizer.from_pretrained(config.model_name, revision=config.revision) + model = AutoModelForSequenceClassification.from_pretrained( + config.model_name, + revision=config.revision, + torch_dtype=getattr(torch, config.torch_dtype), + device_map=config.device_map, + cache_dir=config.cache_dir, + ) + return model, tokenizer +``` diff --git a/skills/huggingface/skill.toml b/skills/huggingface/skill.toml index 466b4de..6058cf9 100644 --- a/skills/huggingface/skill.toml +++ b/skills/huggingface/skill.toml @@ -2,6 +2,7 @@ name = "huggingface" version = "1.0.0" category = "cv-ml" +tier = "extra" tags = ["transformers", "datasets", "fine-tuning", "pretrained", "nlp", "vision"] [dependencies] diff --git a/skills/hydra-config/SKILL.md b/skills/hydra-config/SKILL.md index 939bfc6..7653492 100644 --- a/skills/hydra-config/SKILL.md +++ b/skills/hydra-config/SKILL.md @@ -11,7 +11,11 @@ description: > # Hydra Configuration Skill -Use Hydra for managing complex, hierarchical configurations in machine learning and computer vision projects. This skill covers structured configs, config composition, command-line overrides, multi-run sweeps, and integration with Pydantic validation. +Use Hydra to compose ML/CV experiment configuration from small, swappable config +groups, override any value from the command line, and sweep with `--multirun`. +This page holds the everyday core — a `conf/` tree, a `defaults` list, and a +`@hydra.main` entry point. The deep dives below carry structured configs, +composition rules, sweeps, instantiation, and framework integrations. ## Why Hydra @@ -26,131 +30,23 @@ Hydra solves these problems by providing: - **Automatic working directory management** -- each run gets its own output directory - **Config interpolation** -- reference other config values with `${}` syntax -## Integration with Pydantic +## Core pattern: config groups + `@hydra.main` -Hydra configs use OmegaConf under the hood, which provides basic type checking but lacks the rich validation that Pydantic offers. The recommended pattern is to define Hydra-compatible dataclasses for the config store, then convert to Pydantic models at runtime for full validation. - -```python -from dataclasses import dataclass -from hydra.core.config_store import ConfigStore -from omegaconf import MISSING, DictConfig -from pydantic import BaseModel, Field -import hydra - - -# Pydantic model for runtime validation -class TrainingConfig(BaseModel): - """Training configuration with validation.""" - learning_rate: float = Field(gt=0, default=1e-3) - batch_size: int = Field(ge=1, default=32) - epochs: int = Field(ge=1, default=100) - optimizer: str = Field(default="adamw") - weight_decay: float = Field(ge=0, default=0.01) - - -class DataConfig(BaseModel): - """Data configuration.""" - data_dir: str - train_split: float = Field(gt=0, lt=1, default=0.8) - val_split: float = Field(gt=0, lt=1, default=0.1) - test_split: float = Field(gt=0, lt=1, default=0.1) - num_workers: int = Field(ge=0, default=4) - pin_memory: bool = True - - -class ExperimentConfig(BaseModel): - """Full experiment configuration.""" - training: TrainingConfig - data: DataConfig - seed: int = Field(ge=0, default=42) - experiment_name: str = Field(min_length=1) - - -# Hydra dataclass for config store (structured configs) -@dataclass -class TrainingHydraConfig: - learning_rate: float = 1e-3 - batch_size: int = 32 - epochs: int = 100 - optimizer: str = "adamw" - weight_decay: float = 0.01 - - -@dataclass -class DataHydraConfig: - data_dir: str = MISSING - train_split: float = 0.8 - val_split: float = 0.1 - test_split: float = 0.1 - num_workers: int = 4 - pin_memory: bool = True - - -@dataclass -class ExperimentHydraConfig: - training: TrainingHydraConfig = TrainingHydraConfig() - data: DataHydraConfig = DataHydraConfig() - seed: int = 42 - experiment_name: str = MISSING - - -# Register with ConfigStore -cs = ConfigStore.instance() -cs.store(name="config", node=ExperimentHydraConfig) - - -def hydra_to_pydantic(cfg: DictConfig) -> ExperimentConfig: - """Convert Hydra config to Pydantic for validation.""" - from omegaconf import OmegaConf - raw = OmegaConf.to_container(cfg, resolve=True) - return ExperimentConfig(**raw) - - -@hydra.main(version_base=None, config_path="conf", config_name="config") -def main(cfg: DictConfig) -> None: - """Main entry point with Hydra.""" - # Validate with Pydantic - config = hydra_to_pydantic(cfg) - - # Use validated config - train(config) -``` - -The dual-layer approach gives you the best of both worlds: Hydra handles composition and CLI overrides, while Pydantic enforces constraints like `learning_rate > 0` or `batch_size >= 1` at runtime. - -## Directory Structure - -Organize configuration files by concern. Each subdirectory represents a config group that can be swapped via the defaults list or CLI overrides. +Organize configuration by concern. Each subdirectory of `conf/` is a config group +whose variants can be swapped from the defaults list or the CLI. ``` conf/ -├── config.yaml # Default config (top-level) -├── training/ -│ ├── default.yaml -│ ├── fast.yaml # Quick training for debugging -│ └── full.yaml # Full training run -├── data/ -│ ├── coco.yaml -│ ├── imagenet.yaml -│ └── custom.yaml -├── model/ -│ ├── resnet50.yaml -│ ├── efficientnet.yaml -│ └── yolov8.yaml -├── augmentation/ -│ ├── basic.yaml -│ ├── heavy.yaml -│ └── none.yaml -└── experiment/ - ├── debug.yaml - └── production.yaml +├── config.yaml # Top-level config +├── training/ # default.yaml, fast.yaml, full.yaml +├── data/ # coco.yaml, imagenet.yaml, custom.yaml +├── model/ # resnet50.yaml, efficientnet.yaml, yolov8.yaml +├── augmentation/ # basic.yaml, heavy.yaml, none.yaml +└── experiment/ # debug.yaml, production.yaml ``` -## Config Files - -### conf/config.yaml - -The top-level config uses the `defaults` list to compose from config groups. The `_self_` entry controls where this file's values are placed relative to the defaults. +The top-level `conf/config.yaml` picks one variant per group. `_self_` controls +where this file's own values land relative to the composed defaults. ```yaml defaults: @@ -162,12 +58,10 @@ defaults: seed: 42 experiment_name: "default_experiment" - -# Interpolation example output_dir: "outputs/${experiment_name}/${now:%Y-%m-%d_%H-%M-%S}" ``` -### conf/training/default.yaml +A group file is plain YAML — `conf/training/default.yaml`: ```yaml learning_rate: 1e-3 @@ -176,271 +70,42 @@ epochs: 100 optimizer: adamw weight_decay: 0.01 scheduler: cosine -warmup_epochs: 5 -gradient_clip_val: 1.0 -``` - -### conf/training/fast.yaml - -```yaml -learning_rate: 1e-3 -batch_size: 64 -epochs: 10 -optimizer: adam -weight_decay: 0.0 -scheduler: none -warmup_epochs: 0 -gradient_clip_val: null -``` - -### conf/training/full.yaml - -```yaml -learning_rate: 3e-4 -batch_size: 16 -epochs: 300 -optimizer: adamw -weight_decay: 0.05 -scheduler: cosine -warmup_epochs: 10 -gradient_clip_val: 1.0 -``` - -### conf/data/coco.yaml - -```yaml -data_dir: "/data/coco" -train_split: 0.8 -val_split: 0.1 -test_split: 0.1 -num_workers: 8 -pin_memory: true -image_size: 640 -format: "coco" -``` - -### conf/data/imagenet.yaml - -```yaml -data_dir: "/data/imagenet" -train_split: 0.9 -val_split: 0.05 -test_split: 0.05 -num_workers: 16 -pin_memory: true -image_size: 224 -format: "imagefolder" -``` - -### conf/model/resnet50.yaml - -```yaml -name: resnet50 -pretrained: true -num_classes: 80 -dropout: 0.1 -freeze_backbone: false -backbone_lr_factor: 0.1 -``` - -### conf/augmentation/heavy.yaml - -```yaml -horizontal_flip: 0.5 -vertical_flip: 0.0 -rotation_limit: 30 -brightness_limit: 0.3 -contrast_limit: 0.3 -hue_shift_limit: 20 -mosaic_prob: 0.5 -mixup_prob: 0.3 -cutout_prob: 0.2 -``` - -## Command-Line Overrides - -Hydra allows overriding any config value from the command line without editing files. - -```bash -# Override single value -python train.py training.learning_rate=1e-4 - -# Override multiple values -python train.py training.learning_rate=1e-4 training.batch_size=64 seed=123 - -# Use different config group -python train.py training=fast data=imagenet - -# Multi-run sweep over learning rates -python train.py --multirun training.learning_rate=1e-3,1e-4,1e-5 - -# Multi-run sweep over multiple parameters (grid) -python train.py --multirun \ - training.learning_rate=1e-3,1e-4 \ - training.batch_size=16,32,64 - -# Override nested values -python train.py model.num_classes=10 data.image_size=320 - -# Set experiment name -python train.py experiment_name="lr_sweep_v2" -``` - -## Advanced Patterns - -### Config Groups with Package Directive - -When you need to mount a config group at a specific path in the config tree, use the `@package` directive. - -```yaml -# conf/server/apache.yaml -# @package _group_ -host: localhost -port: 8080 -``` - -### Recursive Defaults - -Compose configs that themselves reference other defaults. - -```yaml -# conf/experiment/production.yaml -defaults: - - /training: full - - /data: imagenet - - /model: efficientnet - - /augmentation: heavy - -seed: 0 -experiment_name: "production_run" ``` -Then run with: - -```bash -python train.py +experiment=production -``` - -### Instantiate Pattern - -Use `hydra.utils.instantiate` to create objects directly from config. This is powerful for swapping model architectures, optimizers, or schedulers without changing code. +Entry point: decorate `main` with `@hydra.main`, then validate the composed +config with Pydantic before anything expensive runs. ```python -from hydra.utils import instantiate -from omegaconf import DictConfig import hydra +from omegaconf import DictConfig, OmegaConf +from pydantic import BaseModel, Field -@hydra.main(version_base=None, config_path="conf", config_name="config") -def main(cfg: DictConfig) -> None: - # Config specifies _target_ for instantiation - model = instantiate(cfg.model) - optimizer = instantiate(cfg.optimizer, params=model.parameters()) - scheduler = instantiate(cfg.scheduler, optimizer=optimizer) -``` - -With corresponding config: - -```yaml -# conf/optimizer/adamw.yaml -_target_: torch.optim.AdamW -lr: 1e-3 -weight_decay: 0.01 -betas: [0.9, 0.999] - -# conf/scheduler/cosine.yaml -_target_: torch.optim.lr_scheduler.CosineAnnealingLR -T_max: 100 -eta_min: 1e-6 -``` - -### Variable Interpolation - -Reference other config values using `${}` syntax. This eliminates duplication and keeps values consistent. - -```yaml -training: - epochs: 100 - warmup_epochs: 5 - -scheduler: - _target_: torch.optim.lr_scheduler.CosineAnnealingLR - T_max: ${training.epochs} - -output_dir: "outputs/${experiment_name}/${now:%Y-%m-%d}" -checkpoint_dir: "${output_dir}/checkpoints" -log_dir: "${output_dir}/logs" -``` - -### Environment Variable Resolution - -Read values from environment variables with fallbacks. - -```yaml -data: - data_dir: ${oc.env:DATA_DIR,/default/data/path} - cache_dir: ${oc.env:CACHE_DIR,/tmp/cache} - -wandb: - api_key: ${oc.env:WANDB_API_KEY} - project: ${oc.env:WANDB_PROJECT,my-project} -``` - -## Integration with Lightning - -Hydra integrates cleanly with PyTorch Lightning by passing validated config objects into Lightning modules and data modules. - -```python -import pytorch_lightning as pl -from omegaconf import DictConfig -import hydra +class ExperimentConfig(BaseModel): + seed: int = Field(ge=0, default=42) + experiment_name: str = Field(min_length=1) + # ... nested training/data/model models @hydra.main(version_base=None, config_path="conf", config_name="config") def main(cfg: DictConfig) -> None: - config = hydra_to_pydantic(cfg) - - model = MyModel(config.model) - datamodule = MyDataModule(config.data) - - trainer = pl.Trainer( - max_epochs=config.training.epochs, - accelerator="auto", - devices="auto", - gradient_clip_val=config.training.gradient_clip_val, - default_root_dir=cfg.output_dir, - ) - trainer.fit(model, datamodule) -``` - -## Integration with Experiment Tracking - -Log the resolved Hydra config to your experiment tracker so every run is fully reproducible. - -```python -from omegaconf import OmegaConf -import wandb - + config = ExperimentConfig(**OmegaConf.to_container(cfg, resolve=True)) + train(config) -@hydra.main(version_base=None, config_path="conf", config_name="config") -def main(cfg: DictConfig) -> None: - # Convert to dict for logging - config_dict = OmegaConf.to_container(cfg, resolve=True) - # Log to W&B - wandb.init( - project=cfg.experiment_name, - config=config_dict, - ) +if __name__ == "__main__": + main() +``` - # Also save as YAML artifact - with open("config_resolved.yaml", "w") as f: - OmegaConf.save(cfg, f, resolve=True) +Everyday CLI usage: - wandb.save("config_resolved.yaml") +```bash +python train.py training.learning_rate=1e-4 # override a value +python train.py training=fast data=imagenet # swap config groups +python train.py --multirun training.learning_rate=1e-3,1e-4,1e-5 # sweep ``` -## Best Practices +## Conventions 1. **Always validate with Pydantic** -- Hydra configs lack runtime validation constraints; use Pydantic to enforce value ranges and types at startup before any training begins. @@ -462,9 +127,17 @@ def main(cfg: DictConfig) -> None: 10. **Use interpolation for derived values** -- If `checkpoint_dir` depends on `output_dir`, use `${output_dir}/checkpoints` rather than duplicating the path. -## Common Pitfalls +## Anti-patterns - **Forgetting `_self_`** -- Without `_self_` in the defaults list, the order of config merging may surprise you. Always include it explicitly. - **Mutating OmegaConf objects** -- OmegaConf DictConfigs are not regular dicts. Convert to a container or Pydantic model before mutating. - **Relative paths** -- Hydra changes the working directory by default. Use absolute paths or `hydra.runtime.cwd` to resolve relative paths correctly. - **Missing `version_base`** -- Always set `version_base=None` (or a specific version) in `@hydra.main` to avoid deprecation warnings and ensure consistent behavior. + +## Deep dives + +- `references/structured-configs-and-pydantic.md` — read when defining dataclass configs, registering with the ConfigStore, or wiring the Hydra→Pydantic validation layer. +- `references/config-groups-and-composition.md` — read when laying out `conf/`, writing group YAML files, using `@package`, or building recursive experiment configs. +- `references/overrides-and-multirun.md` — read when running parameter sweeps, multirun jobs, or needing exact CLI override syntax (`+`, `++`, group swaps). +- `references/instantiate-and-interpolation.md` — read when building objects from `_target_` config, using `${}` interpolation, or reading env vars into config. +- `references/lightning-and-tracking-integration.md` — read when passing a Hydra config into PyTorch Lightning or logging the resolved config to W&B/MLflow. diff --git a/skills/hydra-config/references/config-groups-and-composition.md b/skills/hydra-config/references/config-groups-and-composition.md new file mode 100644 index 0000000..dcf027d --- /dev/null +++ b/skills/hydra-config/references/config-groups-and-composition.md @@ -0,0 +1,194 @@ +# Config Groups and Composition + +Scope: the `conf/` layout, the YAML files that make up each config group, the defaults list, `@package` directives, and recursive/experiment configs. + +## Contents + +- [Directory structure](#directory-structure) +- [conf/config.yaml](#confconfigyaml) +- [Training group](#training-group) +- [Data group](#data-group) +- [Model group](#model-group) +- [Augmentation group](#augmentation-group) +- [Config groups with package directive](#config-groups-with-package-directive) +- [Recursive defaults and experiment configs](#recursive-defaults-and-experiment-configs) + +## Directory structure + +Organize configuration files by concern. Each subdirectory represents a config group that can be swapped via the defaults list or CLI overrides. + +``` +conf/ +├── config.yaml # Default config (top-level) +├── training/ +│ ├── default.yaml +│ ├── fast.yaml # Quick training for debugging +│ └── full.yaml # Full training run +├── data/ +│ ├── coco.yaml +│ ├── imagenet.yaml +│ └── custom.yaml +├── model/ +│ ├── resnet50.yaml +│ ├── efficientnet.yaml +│ └── yolov8.yaml +├── augmentation/ +│ ├── basic.yaml +│ ├── heavy.yaml +│ └── none.yaml +└── experiment/ + ├── debug.yaml + └── production.yaml +``` + +## conf/config.yaml + +The top-level config uses the `defaults` list to compose from config groups. The `_self_` entry controls where this file's values are placed relative to the defaults. + +```yaml +defaults: + - training: default + - data: coco + - model: resnet50 + - augmentation: basic + - _self_ + +seed: 42 +experiment_name: "default_experiment" + +# Interpolation example +output_dir: "outputs/${experiment_name}/${now:%Y-%m-%d_%H-%M-%S}" +``` + +## Training group + +### conf/training/default.yaml + +```yaml +learning_rate: 1e-3 +batch_size: 32 +epochs: 100 +optimizer: adamw +weight_decay: 0.01 +scheduler: cosine +warmup_epochs: 5 +gradient_clip_val: 1.0 +``` + +### conf/training/fast.yaml + +```yaml +learning_rate: 1e-3 +batch_size: 64 +epochs: 10 +optimizer: adam +weight_decay: 0.0 +scheduler: none +warmup_epochs: 0 +gradient_clip_val: null +``` + +### conf/training/full.yaml + +```yaml +learning_rate: 3e-4 +batch_size: 16 +epochs: 300 +optimizer: adamw +weight_decay: 0.05 +scheduler: cosine +warmup_epochs: 10 +gradient_clip_val: 1.0 +``` + +## Data group + +### conf/data/coco.yaml + +```yaml +data_dir: "/data/coco" +train_split: 0.8 +val_split: 0.1 +test_split: 0.1 +num_workers: 8 +pin_memory: true +image_size: 640 +format: "coco" +``` + +### conf/data/imagenet.yaml + +```yaml +data_dir: "/data/imagenet" +train_split: 0.9 +val_split: 0.05 +test_split: 0.05 +num_workers: 16 +pin_memory: true +image_size: 224 +format: "imagefolder" +``` + +## Model group + +### conf/model/resnet50.yaml + +```yaml +name: resnet50 +pretrained: true +num_classes: 80 +dropout: 0.1 +freeze_backbone: false +backbone_lr_factor: 0.1 +``` + +## Augmentation group + +### conf/augmentation/heavy.yaml + +```yaml +horizontal_flip: 0.5 +vertical_flip: 0.0 +rotation_limit: 30 +brightness_limit: 0.3 +contrast_limit: 0.3 +hue_shift_limit: 20 +mosaic_prob: 0.5 +mixup_prob: 0.3 +cutout_prob: 0.2 +``` + +## Config groups with package directive + +When you need to mount a config group at a specific path in the config tree, use the `@package` directive. + +```yaml +# conf/server/apache.yaml +# @package _group_ +host: localhost +port: 8080 +``` + +## Recursive defaults and experiment configs + +Compose configs that themselves reference other defaults. + +```yaml +# conf/experiment/production.yaml +defaults: + - /training: full + - /data: imagenet + - /model: efficientnet + - /augmentation: heavy + +seed: 0 +experiment_name: "production_run" +``` + +Then run with: + +```bash +python train.py +experiment=production +``` + +Keep named experiment configs (e.g. `experiment/ablation_v3.yaml`) checked in so a specific combination of config groups can be reproduced by name. diff --git a/skills/hydra-config/references/instantiate-and-interpolation.md b/skills/hydra-config/references/instantiate-and-interpolation.md new file mode 100644 index 0000000..ec5b860 --- /dev/null +++ b/skills/hydra-config/references/instantiate-and-interpolation.md @@ -0,0 +1,74 @@ +# Instantiate and Interpolation + +Scope: building objects directly from config with `hydra.utils.instantiate`, referencing other config values with `${}`, and reading environment variables. + +## Contents + +- [Instantiate pattern](#instantiate-pattern) +- [Variable interpolation](#variable-interpolation) +- [Environment variable resolution](#environment-variable-resolution) + +## Instantiate pattern + +Use `hydra.utils.instantiate` to create objects directly from config. This is powerful for swapping model architectures, optimizers, or schedulers without changing code. + +```python +from hydra.utils import instantiate +from omegaconf import DictConfig +import hydra + + +@hydra.main(version_base=None, config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + # Config specifies _target_ for instantiation + model = instantiate(cfg.model) + optimizer = instantiate(cfg.optimizer, params=model.parameters()) + scheduler = instantiate(cfg.scheduler, optimizer=optimizer) +``` + +With corresponding config: + +```yaml +# conf/optimizer/adamw.yaml +_target_: torch.optim.AdamW +lr: 1e-3 +weight_decay: 0.01 +betas: [0.9, 0.999] + +# conf/scheduler/cosine.yaml +_target_: torch.optim.lr_scheduler.CosineAnnealingLR +T_max: 100 +eta_min: 1e-6 +``` + +## Variable interpolation + +Reference other config values using `${}` syntax. This eliminates duplication and keeps values consistent. + +```yaml +training: + epochs: 100 + warmup_epochs: 5 + +scheduler: + _target_: torch.optim.lr_scheduler.CosineAnnealingLR + T_max: ${training.epochs} + +output_dir: "outputs/${experiment_name}/${now:%Y-%m-%d}" +checkpoint_dir: "${output_dir}/checkpoints" +log_dir: "${output_dir}/logs" +``` + +## Environment variable resolution + +Read values from environment variables with fallbacks. + +```yaml +data: + data_dir: ${oc.env:DATA_DIR,/default/data/path} + cache_dir: ${oc.env:CACHE_DIR,/tmp/cache} + +wandb: + api_key: ${oc.env:WANDB_API_KEY} + project: ${oc.env:WANDB_PROJECT,my-project} +``` diff --git a/skills/hydra-config/references/lightning-and-tracking-integration.md b/skills/hydra-config/references/lightning-and-tracking-integration.md new file mode 100644 index 0000000..a77bb87 --- /dev/null +++ b/skills/hydra-config/references/lightning-and-tracking-integration.md @@ -0,0 +1,59 @@ +# Lightning and Experiment-Tracking Integration + +Scope: wiring a composed Hydra config into PyTorch Lightning and logging the fully resolved config to an experiment tracker. + +## Integration with Lightning + +Hydra integrates cleanly with PyTorch Lightning by passing validated config objects into Lightning modules and data modules. + +```python +import pytorch_lightning as pl +from omegaconf import DictConfig +import hydra + + +@hydra.main(version_base=None, config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + config = hydra_to_pydantic(cfg) + + model = MyModel(config.model) + datamodule = MyDataModule(config.data) + + trainer = pl.Trainer( + max_epochs=config.training.epochs, + accelerator="auto", + devices="auto", + gradient_clip_val=config.training.gradient_clip_val, + default_root_dir=cfg.output_dir, + ) + trainer.fit(model, datamodule) +``` + +## Integration with experiment tracking + +Log the resolved Hydra config to your experiment tracker so every run is fully reproducible. + +```python +from omegaconf import OmegaConf +import wandb + + +@hydra.main(version_base=None, config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + # Convert to dict for logging + config_dict = OmegaConf.to_container(cfg, resolve=True) + + # Log to W&B + wandb.init( + project=cfg.experiment_name, + config=config_dict, + ) + + # Also save as YAML artifact + with open("config_resolved.yaml", "w") as f: + OmegaConf.save(cfg, f, resolve=True) + + wandb.save("config_resolved.yaml") +``` + +Always log or save the *resolved* config (`resolve=True`), not the raw one — otherwise interpolations like `${training.epochs}` are stored unresolved and the record cannot be replayed. diff --git a/skills/hydra-config/references/overrides-and-multirun.md b/skills/hydra-config/references/overrides-and-multirun.md new file mode 100644 index 0000000..f1a1791 --- /dev/null +++ b/skills/hydra-config/references/overrides-and-multirun.md @@ -0,0 +1,37 @@ +# Command-Line Overrides and Multirun Sweeps + +Scope: changing any config value from the command line, swapping config groups, and running grid sweeps with `--multirun`. + +Hydra allows overriding any config value from the command line without editing files. + +```bash +# Override single value +python train.py training.learning_rate=1e-4 + +# Override multiple values +python train.py training.learning_rate=1e-4 training.batch_size=64 seed=123 + +# Use different config group +python train.py training=fast data=imagenet + +# Multi-run sweep over learning rates +python train.py --multirun training.learning_rate=1e-3,1e-4,1e-5 + +# Multi-run sweep over multiple parameters (grid) +python train.py --multirun \ + training.learning_rate=1e-3,1e-4 \ + training.batch_size=16,32,64 + +# Override nested values +python train.py model.num_classes=10 data.image_size=320 + +# Set experiment name +python train.py experiment_name="lr_sweep_v2" +``` + +Notes: + +- `key=value` overrides an existing key; `+key=value` adds a new one; `++key=value` adds or overrides. +- `--multirun` (`-m`) takes the cartesian product of every comma-separated list, so two lists of 2 and 3 values launch 6 runs. +- Each multirun job gets its own output directory under `multirun//