diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml index 045fc53..4bfa50c 100644 --- a/.github/workflows/quality.yml +++ b/.github/workflows/quality.yml @@ -30,11 +30,5 @@ jobs: - name: Install dependencies run: make setup - - name: Check dependency sync - run: | - echo "::group::Dependency sync check" - uv run python scripts/sync_example_deps.py --check - echo "::endgroup::" - - name: Run quality checks run: make ci-quality-github diff --git a/.github/workflows/test-examples.yml b/.github/workflows/test-examples.yml index 24d2af9..92a5d5e 100644 --- a/.github/workflows/test-examples.yml +++ b/.github/workflows/test-examples.yml @@ -38,4 +38,4 @@ jobs: run: make venv-info - name: Run quality checks - run: make quality-check-strict + run: make quality-check diff --git a/01_getting_started/01_hello_world/.env.example b/01_getting_started/01_hello_world/.env.example deleted file mode 100644 index 91af5f2..0000000 --- a/01_getting_started/01_hello_world/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# RUNPOD_API_KEY=your_api_key_here -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO diff --git a/01_getting_started/01_hello_world/.gitignore b/01_getting_started/01_hello_world/.gitignore index 5da3c4c..65aaf17 100644 --- a/01_getting_started/01_hello_world/.gitignore +++ b/01_getting_started/01_hello_world/.gitignore @@ -42,3 +42,4 @@ uv.lock # OS .DS_Store Thumbs.db +.flash/ diff --git a/01_getting_started/01_hello_world/README.md b/01_getting_started/01_hello_world/README.md index 3fa6e7d..1764184 100644 --- a/01_getting_started/01_hello_world/README.md +++ b/01_getting_started/01_hello_world/README.md @@ -26,21 +26,19 @@ Get your API key from [Runpod Settings](https://www.runpod.io/console/user/setti flash run ``` -Server starts at **http://localhost:8000** +Server starts at **http://localhost:8888** ### 4. Test the API -```bash -# Health check -curl http://localhost:8000/ping +Visit **http://localhost:8888/docs** for interactive API documentation. QB endpoints are auto-generated by `flash run` based on your `@remote` functions. -# GPU worker -curl -X POST http://localhost:8000/gpu/hello \ +```bash +curl -X POST http://localhost:8888/gpu_worker/run_sync \ -H "Content-Type: application/json" \ -d '{"message": "Hello GPU!"}' ``` -Visit **http://localhost:8000/docs** for interactive API documentation. +Visit **http://localhost:8888/docs** for interactive API documentation. ### Full CLI Documentation @@ -67,7 +65,9 @@ The worker demonstrates: ## API Endpoints -### POST /gpu/hello +QB (queue-based) endpoints are auto-generated from `@remote` functions. Visit `/docs` for the full API schema. + +### `gpu_hello` Executes a simple GPU worker and returns system/GPU information. @@ -100,9 +100,7 @@ Executes a simple GPU worker and returns system/GPU information. ``` 01_hello_world/ -├── main.py # FastAPI application ├── gpu_worker.py # GPU worker with @remote decorator -├── mothership.py # Mothership endpoint configuration ├── pyproject.toml # Project metadata ├── requirements.txt # Dependencies ├── .env.example # Environment variables template diff --git a/01_getting_started/01_hello_world/__init__.py b/01_getting_started/01_hello_world/__init__.py deleted file mode 100644 index 72afaa4..0000000 --- a/01_getting_started/01_hello_world/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Hello world example.""" diff --git a/01_getting_started/01_hello_world/gpu_worker.py b/01_getting_started/01_hello_world/gpu_worker.py index fe8b0cd..6fbd837 100644 --- a/01_getting_started/01_hello_world/gpu_worker.py +++ b/01_getting_started/01_hello_world/gpu_worker.py @@ -1,46 +1,20 @@ -## Hello world: GPU serverless workers -# In this part of the example code, we provision a GPU-based worker and have it -# execute code. We can run the worker directly, or have it handle API requests -# to the router function. It's registered to a subrouter in the __init__.py -# file in this folder, and subsequently imported by main.py and attached to the -# FastAPI app there. +# GPU serverless worker -- detects available GPU hardware. +# Run with: flash run +# Test directly: python gpu_worker.py +from runpod_flash import GpuGroup, LiveServerless, remote -# Scaling behavior is controlled by configuration passed to the -# `LiveServerless` class. -from fastapi import APIRouter -from pydantic import BaseModel -from runpod_flash import ( - GpuGroup, - LiveServerless, - remote, -) - -# Here, we'll define several variables that change the -# default behavior of our serverless endpoint. `workersMin` sets our endpoint -# to scale to 0 active containers; `workersMax` will allow our endpoint to run -# up to 3 workers in parallel as the endpoint receives more work. We also set -# an idle timeout of 5 minutes so that any active worker stays alive for 5 -# minutes after completing a request. gpu_config = LiveServerless( name="01_01_gpu_worker", - gpus=[GpuGroup.ANY], # Run on any GPU + gpus=[GpuGroup.ANY], workersMin=0, workersMax=3, idleTimeout=5, ) -# Decorating our function with `remote` will package up the function code and -# deploy it on the infrastructure according to the passed input config. The -# results from the worker will be returned to your terminal. In this example -# the function will return a greeting to the input string passed in the `name` -# key. The code itself will run on a GPU worker, and information about the GPU -# the worker has access to will be included in the response. @remote(resource_config=gpu_config) -async def gpu_hello( - input_data: dict, -) -> dict: - """Simple GPU worker example with GPU detection.""" +async def gpu_hello(input_data: dict) -> dict: + """Simple GPU worker that returns GPU hardware info.""" import platform from datetime import datetime @@ -51,10 +25,7 @@ async def gpu_hello( gpu_count = torch.cuda.device_count() gpu_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3) - message = input_data.get( - "message", - "Hello from GPU worker!", - ) + message = input_data.get("message", "Hello from GPU worker!") return { "status": "success", @@ -64,10 +35,7 @@ async def gpu_hello( "available": gpu_available, "name": gpu_name, "count": gpu_count, - "memory_gb": round( - gpu_memory, - 2, - ), + "memory_gb": round(gpu_memory, 2), }, "timestamp": datetime.now().isoformat(), "platform": platform.system(), @@ -75,29 +43,6 @@ async def gpu_hello( } -# We define a subrouter for our gpu worker so that our main router in `main.py` -# can attach it for routing gpu-specific requests. -gpu_router = APIRouter() - - -class MessageRequest(BaseModel): - """Request model for GPU worker.""" - - message: str = "Hello from GPU!" - - -@gpu_router.post("/hello") -async def hello( - request: MessageRequest, -): - """Simple GPU worker endpoint.""" - result = await gpu_hello({"message": request.message}) - return result - - -# This code is packaged up as a "worker" that will handle requests sent to the -# endpoint at /gpu/hello, but you can also trigger it directly by running -# python -m workers.gpu.endpoint if __name__ == "__main__": import asyncio diff --git a/01_getting_started/01_hello_world/main.py b/01_getting_started/01_hello_world/main.py deleted file mode 100644 index e0e51c2..0000000 --- a/01_getting_started/01_hello_world/main.py +++ /dev/null @@ -1,71 +0,0 @@ -## Example 1: Hello world -# This is an example of a simple Flash application. -# It consists of an API router (this file) that routes requests to a local -# endpoint on your machine to worker code. Worker code is executed on Runpod -# infrastructure on serverless workers with GPUs. - -# Subrouters and associated worker function code are defined in the ./workers/ -# dir and attached to the main router in this file. By default, running -# `flash run` will start the local API server on your machine serving requests -# from port 8888. - -# We'll define a resource configuration for a GPU worker on a Runpod serverless -# endpoint. The GPU worker will return information about the infrastructure -# it executes on. - -import logging -import os - -from fastapi import FastAPI -from gpu_worker import gpu_router - -logger = logging.getLogger(__name__) - -# We define a simple FastAPI app to serve requests from localhost. -app = FastAPI( - title="Flash Application", - description="Distributed GPU computing with Runpod Flash", - version="0.1.0", -) - -# Attach gpu worker subrouters - this will route any requests to our -# app with the prefix /gpu to the gpu subrouter. To see the subrouter in action, -# start the app and execute the following command in another terminal window: -# curl -X POST http://localhost:8888/gpu/hello -d '{"input": "hello"}' -H "Content-Type: application/json" -app.include_router( - gpu_router, - prefix="/gpu", - tags=["GPU Workers"], -) - - -# The homepage for our main endpoint will just return a plaintext json object -# containing the endpoints defined in this app. -@app.get("/") -def home(): - return { - "message": "Flash Application", - "docs": "/docs", - "endpoints": { - "gpu_hello": "/gpu/hello", - }, - } - - -@app.get("/ping") -def ping(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting Flash server on {host}:{port}") - - uvicorn.run( - app, - host=host, - port=port, - ) diff --git a/01_getting_started/01_hello_world/mothership.py b/01_getting_started/01_hello_world/mothership.py deleted file mode 100644 index e417f98..0000000 --- a/01_getting_started/01_hello_world/mothership.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="01_01_hello_world-mothership", - # workersMin=1, - # workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership-01_01_hello_world", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership-01_01_hello_world", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/01_getting_started/01_hello_world/requirements.txt b/01_getting_started/01_hello_world/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/01_getting_started/01_hello_world/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/01_getting_started/02_cpu_worker/.env.example b/01_getting_started/02_cpu_worker/.env.example deleted file mode 100644 index 91af5f2..0000000 --- a/01_getting_started/02_cpu_worker/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# RUNPOD_API_KEY=your_api_key_here -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO diff --git a/01_getting_started/02_cpu_worker/README.md b/01_getting_started/02_cpu_worker/README.md index 68857b5..28741f4 100644 --- a/01_getting_started/02_cpu_worker/README.md +++ b/01_getting_started/02_cpu_worker/README.md @@ -26,22 +26,18 @@ Get your API key from [Runpod Settings](https://www.runpod.io/console/user/setti flash run ``` -Server starts at **http://localhost:8000** +Server starts at **http://localhost:8888** ### 4. Test the API -```bash -# Health check -curl http://localhost:8000/ping +Visit **http://localhost:8888/docs** for interactive API documentation. QB endpoints are auto-generated by `flash run` based on your `@remote` functions. -# CPU worker -curl -X POST http://localhost:8000/cpu/hello \ +```bash +curl -X POST http://localhost:8888/cpu_worker/run_sync \ -H "Content-Type: application/json" \ -d '{"name": "Flash User"}' ``` -Visit **http://localhost:8000/docs** for interactive API documentation. - ### Full CLI Documentation For complete CLI usage including deployment, environment management, and troubleshooting: @@ -67,7 +63,9 @@ The worker demonstrates: ## API Endpoints -### POST /cpu/hello +QB (queue-based) endpoints are auto-generated from `@remote` functions. Visit `/docs` for the full API schema. + +### `cpu_hello` Executes a simple CPU worker and returns a greeting with system information. @@ -94,9 +92,7 @@ Executes a simple CPU worker and returns a greeting with system information. ``` 02_cpu_worker/ -├── main.py # FastAPI application ├── cpu_worker.py # CPU worker with @remote decorator -├── mothership.py # Mothership endpoint configuration ├── pyproject.toml # Project metadata ├── requirements.txt # Dependencies ├── .env.example # Environment variables template diff --git a/01_getting_started/02_cpu_worker/__init__.py b/01_getting_started/02_cpu_worker/__init__.py deleted file mode 100644 index f927b6b..0000000 --- a/01_getting_started/02_cpu_worker/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""CPU worker example.""" diff --git a/01_getting_started/02_cpu_worker/cpu_worker.py b/01_getting_started/02_cpu_worker/cpu_worker.py index 8c9f281..429d1dc 100644 --- a/01_getting_started/02_cpu_worker/cpu_worker.py +++ b/01_getting_started/02_cpu_worker/cpu_worker.py @@ -1,33 +1,20 @@ -## Hello world: CPU serverless workers -# In this part of the example code, we provision a CPU-based worker (no GPUs) and have it execute code. -# We can run the worker directly, or have it handle API requests to the router function. -# It's registered to a subrouter in the __init__.py file in this folder, and subsequently -# imported by main.py and attached to the FastAPI app there. -from fastapi import APIRouter -from pydantic import BaseModel +# CPU serverless worker -- lightweight processing without GPU. +# Run with: flash run +# Test directly: python cpu_worker.py from runpod_flash import CpuInstanceType, CpuLiveServerless, remote -# Scaling behavior is controlled by configuration passed to the `CpuLiveServerless` class. -# Here, we'll define several variables that change the default behavior of our serverless endpoint. -# `workersMin` sets our endpoint to scale to 0 active containers; `workersMax` will allow our endpoint -# to run up to 5 workers in parallel as the endpoint receives more work. -# We also set an idle timeout of 5 minutes so that any active worker stays alive for 5 minutes after completing a request. cpu_config = CpuLiveServerless( name="01_02_cpu_worker", instanceIds=[CpuInstanceType.CPU3C_1_2], - workersMin=0, # Scale to zero + workersMin=0, workersMax=3, - idleTimeout=5, # Leave workers alive for 5 minutes after they serve a request + idleTimeout=5, ) -# Decorating our function with `remote` will package up the function code and deploy it on the infrastructure -# according to the passed input config. -# In this example the function will return a greeting to the input string passed in the `name` key. -# The results are displayed in your terminal, but the work was performed by CPU workers on runpod infra. @remote(resource_config=cpu_config) async def cpu_hello(input_data: dict) -> dict: - """Simple CPU worker example.""" + """Simple CPU worker that returns a greeting.""" import platform from datetime import datetime @@ -43,24 +30,6 @@ async def cpu_hello(input_data: dict) -> dict: } -cpu_router = APIRouter() - - -class MessageRequest(BaseModel): - """Request model for CPU worker.""" - - name: str = "Flash expert" - - -@cpu_router.post("/hello") -async def hello(request: MessageRequest): - """Simple CPU worker endpoint.""" - result = await cpu_hello({"message": request.name}) - return result - - -# This code is packaged up as a "worker" that will handle requests sent to the endpoint at -# /cpu/hello, but you can also trigger it locally by running python -m workers.cpu.endpoint. if __name__ == "__main__": import asyncio diff --git a/01_getting_started/02_cpu_worker/main.py b/01_getting_started/02_cpu_worker/main.py deleted file mode 100644 index 1ea67d6..0000000 --- a/01_getting_started/02_cpu_worker/main.py +++ /dev/null @@ -1,60 +0,0 @@ -## Example 2: Hello world (with CPU workers) -# This is an example of a simple Flash application. -# It consists of an API router (this file) that routes requests to a local endpoint on your machine to -# worker code. Worker code is executed on Runpod infrastructure on low-cost, cpu-based serverless workers. - -# Subrouters and associated worker function code are defined in the ./workers/ dir -# and attached to the main router in this file. By default, running `flash run` -# will start the local API server on your machine serving requests from port 8888. - -# There are two worker resource configurations for this app: a gpu and a cpu config. -# A CPU-only configured worker allows you to execute code that doesn't require GPUs on -# runpod infrastructure for a lower cost. It will return a simple greeting when you invoke the code directly -# or send a request to its corresponding endpoint. The GPU worker will return information about the infrastructure -# it executes on. - -import logging -import os - -from cpu_worker import cpu_router -from fastapi import FastAPI - -logger = logging.getLogger(__name__) - -# We define a simple FastAPI app to serve requests from localhost. -app = FastAPI( - title="Flash Application", - description="Distributed GPU and CPU computing with Runpod Flash", - version="0.1.0", -) - -# Attach gpu and cpu worker subrouters - this will route any requests to our app -# with the prefix /cpu to our CPU subrouter. Try out the following command -# in a separate terminal window after starting your app: -# curl -X POST http://localhost:8888/cpu/hello -d '{"input": "hello"}' -H "Content-Type: application/json" -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"]) - - -# The homepage for our main endpoint will just return a plaintext json object containing the endpoints defined in this app. -@app.get("/") -def home(): - return { - "message": "Flash Application", - "docs": "/docs", - "endpoints": {"cpu_hello": "/cpu/hello"}, - } - - -@app.get("/ping") -def ping(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting Flash server on {host}:{port}") - - uvicorn.run(app, host=host, port=port) diff --git a/01_getting_started/02_cpu_worker/mothership.py b/01_getting_started/02_cpu_worker/mothership.py deleted file mode 100644 index 106109a..0000000 --- a/01_getting_started/02_cpu_worker/mothership.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="01_02_cpu_worker-mothership", - # workersMin=1, - # workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership-01_02_cpu_worker", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership-01_02_cpu_worker", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/01_getting_started/02_cpu_worker/requirements.txt b/01_getting_started/02_cpu_worker/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/01_getting_started/02_cpu_worker/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/01_getting_started/03_mixed_workers/.env.example b/01_getting_started/03_mixed_workers/.env.example deleted file mode 100644 index 91af5f2..0000000 --- a/01_getting_started/03_mixed_workers/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# RUNPOD_API_KEY=your_api_key_here -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO diff --git a/01_getting_started/03_mixed_workers/README.md b/01_getting_started/03_mixed_workers/README.md index 370fa5b..dba3533 100644 --- a/01_getting_started/03_mixed_workers/README.md +++ b/01_getting_started/03_mixed_workers/README.md @@ -8,26 +8,25 @@ Learn the production pattern of combining CPU and GPU workers for cost-effective - **Cost optimization** - Using GPU only when necessary - **Pipeline orchestration** - Coordinating multiple worker types - **Production patterns** - Real-world ML service architecture -- **Input validation** - Pydantic validation at each pipeline stage ## Architecture ``` User Request - ↓ + | CPU Worker (Preprocessing) -- Input validation -- Text cleaning +- Text normalization +- Text cleaning - Tokenization - ↓ + | GPU Worker (Inference) - ML model inference - Classification - ↓ + | CPU Worker (Postprocessing) - Result formatting - Aggregation - ↓ + | Response ``` @@ -76,11 +75,11 @@ curl -X POST http://localhost:8888/classify \ -d '{"text": "This product is amazing! I love it!"}' # Individual stages -curl -X POST http://localhost:8888/cpu/preprocess \ +curl -X POST http://localhost:8888/cpu_worker/run_sync \ -H "Content-Type: application/json" \ -d '{"text": "Test message"}' -curl -X POST http://localhost:8888/gpu/inference \ +curl -X POST http://localhost:8888/gpu_worker/run_sync \ -H "Content-Type: application/json" \ -d '{"cleaned_text": "Test message", "word_count": 2}' ``` @@ -96,7 +95,7 @@ For complete CLI usage including deployment, environment management, and trouble ## Why This Pattern? -### GPU-Only Pipeline (❌ Expensive) +### GPU-Only Pipeline (Expensive) ``` Every operation on GPU = $0.0015/sec - Preprocessing: $0.0015/sec @@ -105,7 +104,7 @@ Every operation on GPU = $0.0015/sec Total: $0.0045/sec ``` -### Mixed Pipeline (✅ Cost-Effective) +### Mixed Pipeline (Cost-Effective) ``` Use right tool for each job - Preprocessing: $0.0002/sec (CPU) @@ -116,236 +115,18 @@ Total: $0.0019/sec ## When to Use This Pattern -✅ **Use mixed workers when:** +**Use mixed workers when:** - ML inference is only part of your workflow - You have preprocessing/postprocessing logic - You want to minimize GPU costs - You need input validation before expensive operations - You process data after inference -❌ **Don't use when:** +**Don't use when:** - Your entire workflow needs GPU (image processing, video, etc.) - Overhead of orchestration > cost savings - Very simple operations (single worker is fine) -## Input Validation - -This example demonstrates production-grade input validation using Pydantic at each pipeline stage. - -### Why Validation in Pipelines? - -**Key principle: Fail fast before expensive operations** - -- Catch errors **before** GPU inference (saves money) -- Validate at API layer (prevents bad data reaching workers) -- Provide clear error messages (better developer experience) -- Type-safe data structures (prevent runtime errors) - -### Validation at Each Stage - -**1. Pipeline Endpoint (/classify)** - -```python -class ClassifyRequest(BaseModel): - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - if not v or not v.strip(): - raise ValueError("Text cannot be empty") - if len(v) < 3: - raise ValueError("Text too short (minimum 3 characters)") - if len(v) > 10000: - raise ValueError("Text too long (maximum 10,000 characters)") - return v -``` - -**2. Preprocessing Endpoint (/cpu/preprocess)** - -```python -class PreprocessRequest(BaseModel): - text: str # Same validation as ClassifyRequest -``` - -Validates: -- Text is not empty or whitespace -- Minimum 3 characters -- Maximum 10,000 characters - -**3. Inference Endpoint (/gpu/inference)** - -```python -class InferenceRequest(BaseModel): - cleaned_text: str - word_count: int = Field(ge=0) # >= 0 - - @field_validator("cleaned_text") - @classmethod - def validate_text_not_empty(cls, v): - if not v or not v.strip(): - raise ValueError("Cleaned text cannot be empty") - return v -``` - -Validates: -- Cleaned text is not empty -- Word count is non-negative - -**4. Postprocessing Endpoint (/cpu/postprocess)** - -```python -class Prediction(BaseModel): - label: str - confidence: float - - @field_validator("confidence") - @classmethod - def validate_confidence(cls, v): - if not 0.0 <= v <= 1.0: - raise ValueError(f"Confidence must be between 0.0 and 1.0") - return v - -class PostprocessRequest(BaseModel): - predictions: list[Prediction] # Validates structure! - original_text: str - metadata: dict -``` - -Validates: -- Predictions is a list of `Prediction` objects -- Each prediction has `label` (str) and `confidence` (float 0.0-1.0) -- Rejects invalid structures like `["string"]` - -### Remote Serialization Pattern - -**Problem:** Pydantic models can't be directly serialized for remote workers. - -**Solution:** Convert models to dicts using `.model_dump()`: - -```python -# ❌ BAD - Pydantic models cause serialization errors -result = await postprocess_results({ - "predictions": request.predictions, # List[Prediction] objects -}) - -# ✅ GOOD - Convert to dicts first -result = await postprocess_results({ - "predictions": [pred.model_dump() for pred in request.predictions], -}) -``` - -This pattern: -1. Validates at API layer (type safety + error messages) -2. Converts to plain dicts before remote call (avoids serialization issues) -3. Remote worker receives clean, validated data - -### Testing Validation - -**Valid requests:** - -```bash -# Pipeline endpoint -curl -X POST http://localhost:8888/classify \ - -H "Content-Type: application/json" \ - -d '{"text": "This product is amazing!"}' - -# Postprocessing endpoint -curl -X POST http://localhost:8888/cpu/postprocess \ - -H "Content-Type: application/json" \ - -d '{ - "predictions": [ - {"label": "positive", "confidence": 0.9}, - {"label": "negative", "confidence": 0.1} - ], - "original_text": "This is great!", - "metadata": {"word_count": 3} - }' -``` - -**Invalid requests (rejected with 422):** - -```bash -# Too short -curl -X POST http://localhost:8888/classify \ - -H "Content-Type: application/json" \ - -d '{"text": "Hi"}' -# Error: Text too short (minimum 3 characters) - -# Invalid predictions structure -curl -X POST http://localhost:8888/cpu/postprocess \ - -H "Content-Type: application/json" \ - -d '{ - "predictions": ["string"], - "original_text": "Test", - "metadata": {} - }' -# Error: Input should be a valid dictionary or instance of Prediction - -# Confidence out of range -curl -X POST http://localhost:8888/cpu/postprocess \ - -H "Content-Type: application/json" \ - -d '{ - "predictions": [{"label": "test", "confidence": 1.5}], - "original_text": "Test", - "metadata": {} - }' -# Error: Confidence must be between 0.0 and 1.0 -``` - -### Validation Best Practices - -**1. Validate early** - At API layer, not in worker functions - -```python -# ✅ GOOD - Validation in Pydantic model -class Request(BaseModel): - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - if len(v) < 3: - raise ValueError("Too short") - return v - -# ❌ BAD - Validation in worker function -@remote(resource_config=config) -async def process(data: dict) -> dict: - if len(data["text"]) < 3: - return {"error": "Too short"} # Wastes remote call -``` - -**2. Save costs** - Reject bad requests before GPU operations - -```python -# Pipeline validates input → rejects early → GPU never called → money saved -``` - -**3. Clear errors** - Help API consumers fix issues - -```python -# ✅ GOOD -raise ValueError("Text too long (maximum 10,000 characters). Got 15000 characters.") - -# ❌ BAD -raise ValueError("Invalid text") -``` - -**4. Type safety** - Use structured models, not raw dicts - -```python -# ✅ GOOD - Type-safe, validated -predictions: list[Prediction] - -# ❌ BAD - No validation, error-prone -predictions: list -``` - -### Resources - -See also: [04_dependencies](../04_dependencies/) for more validation patterns - ## Worker Configurations ### CPU Preprocessing Worker @@ -392,28 +173,32 @@ postprocess_config = CpuLiveServerless( ## Pipeline Orchestration -The `/classify` endpoint orchestrates all workers: +The `/classify` load-balanced endpoint orchestrates all workers: ```python -@app.post("/classify") -async def classify_text(request: ClassifyRequest): - # Stage 1: CPU Preprocessing - preprocess_result = await preprocess_text({"text": request.text}) - - # Stage 2: GPU Inference - gpu_result = await gpu_inference({ - "cleaned_text": preprocess_result["cleaned_text"], - "word_count": preprocess_result["word_count"], - }) - - # Stage 3: CPU Postprocessing - final_result = await postprocess_results({ - "predictions": gpu_result["predictions"], - "original_text": request.text, - "metadata": metadata, - }) - - return final_result +@remote(resource_config=pipeline_config, method="POST", path="/classify") +async def classify(text: str) -> dict: + """Complete ML pipeline: CPU preprocess -> GPU inference -> CPU postprocess.""" + preprocess_result = await preprocess_text({"text": text}) + + gpu_result = await gpu_inference( + { + "cleaned_text": preprocess_result["cleaned_text"], + "word_count": preprocess_result["word_count"], + } + ) + + return await postprocess_results( + { + "predictions": gpu_result["predictions"], + "original_text": text, + "metadata": { + "word_count": preprocess_result["word_count"], + "sentence_count": preprocess_result["sentence_count"], + "model": gpu_result["model_info"], + }, + } + ) ``` ## Cost Analysis @@ -427,15 +212,15 @@ Assumptions: **Mixed Pipeline:** ``` -Preprocessing: 0.05 × $0.0002 × 10,000 = $0.10/day -Inference: 0.2 × $0.0015 × 10,000 = $3.00/day -Postprocessing: 0.03 × $0.0002 × 10,000 = $0.06/day +Preprocessing: 0.05 x $0.0002 x 10,000 = $0.10/day +Inference: 0.2 x $0.0015 x 10,000 = $3.00/day +Postprocessing: 0.03 x $0.0002 x 10,000 = $0.06/day Total: $3.16/day = $94.80/month ``` **GPU-Only Pipeline:** ``` -All stages: 0.28 × $0.0015 × 10,000 = $4.20/day +All stages: 0.28 x $0.0015 x 10,000 = $4.20/day Total: $126/month ``` @@ -445,31 +230,7 @@ For higher volumes, savings multiply significantly. ## Production Best Practices -### 1. Input Validation - -Always validate input at API layer **before** pipeline execution: - -```python -class ClassifyRequest(BaseModel): - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - # Validation happens here - fails fast before GPU - if not v or len(v) < 3 or len(v) > 10000: - raise ValueError("Invalid text") - return v - -@app.post("/classify") -async def classify(request: ClassifyRequest): - # Input already validated by Pydantic - # GPU won't be called for invalid requests - result = await run_pipeline(request.text) - return result -``` - -### 2. Error Handling +### 1. Error Handling ```python try: @@ -488,18 +249,18 @@ except Exception as e: raise HTTPException(status_code=500, detail=str(e)) ``` -### 3. Timeouts +### 2. Timeouts Set appropriate timeouts for each stage: ```python # CPU stages: short timeouts preprocess_config.executionTimeout = 30 # seconds -# GPU stage: longer timeout +# GPU stage: longer timeout gpu_config.executionTimeout = 120 # seconds ``` -### 4. Monitoring +### 3. Monitoring Track costs per stage: ```python @@ -519,51 +280,6 @@ flash deploy new production flash deploy send production ``` -## Advanced Patterns - -### Conditional GPU Usage - -Only use GPU when necessary: - -```python -@app.post("/classify-smart") -async def smart_classify(request: ClassifyRequest): - # Preprocess - preprocess_result = await preprocess_text({"text": request.text}) - - # Check if GPU is needed - if preprocess_result["word_count"] < 5: - # Simple rule-based classification (no GPU) - return {"classification": "neutral", "method": "rules"} - - # Use GPU for complex cases - gpu_result = await gpu_inference(preprocess_result) - return await postprocess_results(gpu_result) -``` - -### Batch Processing - -Process multiple inputs together: - -```python -@app.post("/classify-batch") -async def batch_classify(requests: list[ClassifyRequest]): - # Preprocess all on CPU - preprocessed = await asyncio.gather(*[ - preprocess_text({"text": r.text}) for r in requests - ]) - - # Batch GPU inference - gpu_results = await gpu_batch_inference(preprocessed) - - # Postprocess all on CPU - final_results = await asyncio.gather(*[ - postprocess_results(r) for r in gpu_results - ]) - - return final_results -``` - ## Troubleshooting ### Pipeline Failing at GPU Stage diff --git a/01_getting_started/03_mixed_workers/__init__.py b/01_getting_started/03_mixed_workers/__init__.py deleted file mode 100644 index 0e7f92c..0000000 --- a/01_getting_started/03_mixed_workers/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Mixed workers example.""" diff --git a/01_getting_started/03_mixed_workers/workers/cpu/endpoint.py b/01_getting_started/03_mixed_workers/cpu_worker.py similarity index 72% rename from 01_getting_started/03_mixed_workers/workers/cpu/endpoint.py rename to 01_getting_started/03_mixed_workers/cpu_worker.py index 1137e2a..4a5a621 100644 --- a/01_getting_started/03_mixed_workers/workers/cpu/endpoint.py +++ b/01_getting_started/03_mixed_workers/cpu_worker.py @@ -1,36 +1,28 @@ +# CPU workers for text preprocessing and postprocessing. +# Part of the mixed CPU/GPU pipeline example. +# Run with: flash run +# Test directly: python cpu_worker.py from runpod_flash import CpuInstanceType, CpuLiveServerless, remote -# Preprocessing CPU worker (fast, cheap) cpu_config = CpuLiveServerless( name="01_03_mixed_workers_cpu", - instanceIds=[CpuInstanceType.CPU3G_2_8], # Small instance - 2 vCPU, 8GB + instanceIds=[CpuInstanceType.CPU3G_2_8], idleTimeout=3, ) @remote(resource_config=cpu_config) async def preprocess_text(input_data: dict) -> dict: - """ - Preprocessing on CPU: cleaning and tokenization. - - Why CPU: - - No ML operations - - Fast text operations - - 85% cheaper than GPU - - Note: Input validation handled by Pydantic at API layer - """ + """Preprocess text: cleaning and tokenization (cheap CPU work).""" import re from datetime import datetime text = input_data.get("text", "") - # Text cleaning cleaned_text = text.strip() - cleaned_text = re.sub(r"\s+", " ", cleaned_text) # Remove extra whitespace - cleaned_text = re.sub(r"[^\w\s.,!?-]", "", cleaned_text) # Remove special chars + cleaned_text = re.sub(r"\s+", " ", cleaned_text) + cleaned_text = re.sub(r"[^\w\s.,!?-]", "", cleaned_text) - # Simple tokenization (word count, sentence count) words = cleaned_text.split() sentences = len(re.split(r"[.!?]+", cleaned_text)) @@ -48,21 +40,13 @@ async def preprocess_text(input_data: dict) -> dict: @remote(resource_config=cpu_config) async def postprocess_results(input_data: dict) -> dict: - """ - Postprocessing on CPU: formatting, aggregation, logging. - - Why CPU: - - No ML operations - - Simple data formatting - - 85% cheaper than GPU - """ + """Postprocess GPU results: formatting and aggregation (cheap CPU work).""" from datetime import datetime predictions = input_data.get("predictions", []) original_text = input_data.get("original_text", "") metadata = input_data.get("metadata", {}) - # Find top prediction if predictions: top_prediction = max(predictions, key=lambda x: x["confidence"]) confidence_level = ( @@ -76,10 +60,11 @@ async def postprocess_results(input_data: dict) -> dict: top_prediction = None confidence_level = "none" - # Format response - formatted_result = { + return { "status": "success", - "text_preview": original_text[:100] + "..." if len(original_text) > 100 else original_text, + "text_preview": original_text[:100] + "..." + if len(original_text) > 100 + else original_text, "classification": { "label": top_prediction["label"] if top_prediction else "unknown", "confidence": top_prediction["confidence"] if top_prediction else 0, @@ -94,17 +79,10 @@ async def postprocess_results(input_data: dict) -> dict: "worker_type": "CPU Postprocessing", } - return formatted_result - -# Test locally if __name__ == "__main__": import asyncio - from dotenv import find_dotenv, load_dotenv - - load_dotenv(find_dotenv()) # Find and load root .env file - async def test_workers(): print("\n=== Testing Preprocessing ===") preprocess_result = await preprocess_text( diff --git a/01_getting_started/03_mixed_workers/workers/gpu/endpoint.py b/01_getting_started/03_mixed_workers/gpu_worker.py similarity index 75% rename from 01_getting_started/03_mixed_workers/workers/gpu/endpoint.py rename to 01_getting_started/03_mixed_workers/gpu_worker.py index 2c2ff89..064adfb 100644 --- a/01_getting_started/03_mixed_workers/workers/gpu/endpoint.py +++ b/01_getting_started/03_mixed_workers/gpu_worker.py @@ -1,9 +1,12 @@ +# GPU worker for ML inference (sentiment classification). +# Part of the mixed CPU/GPU pipeline example. +# Run with: flash run +# Test directly: python gpu_worker.py from runpod_flash import GpuGroup, LiveServerless, remote -# GPU worker for ML inference (expensive, powerful) gpu_config = LiveServerless( name="01_03_mixed_inference", - gpus=[GpuGroup.ADA_24], # RTX 4090 - 24GB + gpus=[GpuGroup.ADA_24], workersMin=0, workersMax=3, idleTimeout=5, @@ -12,14 +15,7 @@ @remote(resource_config=gpu_config, dependencies=["torch"]) async def gpu_inference(input_data: dict) -> dict: - """ - GPU inference: classification with mock model. - - Why GPU: - - ML model inference - - Matrix operations - - Worth the cost for compute-intensive tasks - """ + """GPU inference: mock sentiment classification.""" import random from datetime import datetime @@ -28,7 +24,6 @@ async def gpu_inference(input_data: dict) -> dict: cleaned_text = input_data.get("cleaned_text", "") word_count = input_data.get("word_count", 0) - # GPU info gpu_available = torch.cuda.is_available() if gpu_available: gpu_name = torch.cuda.get_device_name(0) @@ -37,21 +32,22 @@ async def gpu_inference(input_data: dict) -> dict: gpu_name = "No GPU (running locally)" gpu_memory = 0 - # Mock model inference (in real scenario, load actual model) - # Simulating sentiment analysis classification predictions = [] - - # Positive sentiment indicators - positive_words = ["good", "great", "excellent", "love", "best", "happy", "wonderful"] + positive_words = [ + "good", + "great", + "excellent", + "love", + "best", + "happy", + "wonderful", + ] negative_words = ["bad", "terrible", "worst", "hate", "poor", "awful", "horrible"] - text_lower = cleaned_text.lower() - # Simple sentiment scoring positive_score = sum(1 for word in positive_words if word in text_lower) negative_score = sum(1 for word in negative_words if word in text_lower) - # Create predictions based on word presence if positive_score > negative_score: predictions = [ {"label": "positive", "confidence": 0.75 + random.uniform(0, 0.24)}, @@ -71,12 +67,10 @@ async def gpu_inference(input_data: dict) -> dict: {"label": "negative", "confidence": random.uniform(0.10, 0.30)}, ] - # Normalize confidences to sum to 1.0 total = sum(p["confidence"] for p in predictions) for p in predictions: p["confidence"] = round(p["confidence"] / total, 4) - # Simulate GPU tensor operations (in real scenario, this would be model forward pass) if gpu_available: dummy_tensor = torch.randn(100, 100, device="cuda") _ = torch.matmul(dummy_tensor, dummy_tensor) @@ -91,7 +85,9 @@ async def gpu_inference(input_data: dict) -> dict: "gpu_memory_gb": round(gpu_memory, 2) if gpu_memory else 0, }, "input_stats": { - "cleaned_text": cleaned_text[:100] + "..." if len(cleaned_text) > 100 else cleaned_text, + "cleaned_text": cleaned_text[:100] + "..." + if len(cleaned_text) > 100 + else cleaned_text, "word_count": word_count, }, "timestamp": datetime.now().isoformat(), @@ -99,14 +95,9 @@ async def gpu_inference(input_data: dict) -> dict: } -# Test locally if __name__ == "__main__": import asyncio - from dotenv import find_dotenv, load_dotenv - - load_dotenv(find_dotenv()) # Find and load root .env file - test_payload = { "cleaned_text": "This is a great product! I love it.", "word_count": 8, diff --git a/01_getting_started/03_mixed_workers/main.py b/01_getting_started/03_mixed_workers/main.py deleted file mode 100644 index 8c29aa8..0000000 --- a/01_getting_started/03_mixed_workers/main.py +++ /dev/null @@ -1,127 +0,0 @@ -import logging -import os - -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel, field_validator -from workers.cpu import cpu_router -from workers.cpu.endpoint import postprocess_results, preprocess_text -from workers.gpu import gpu_router -from workers.gpu.endpoint import gpu_inference - -logger = logging.getLogger(__name__) - - -app = FastAPI( - title="Mixed GPU/CPU Flash Application", - description="Cost-effective ML pipeline combining CPU preprocessing with GPU inference", - version="0.1.0", -) - -# Include individual worker routers -app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"]) -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"]) - - -class ClassifyRequest(BaseModel): - """Request model for complete classification pipeline.""" - - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - if not v or not v.strip(): - raise ValueError("Text cannot be empty") - if len(v) < 3: - raise ValueError( - 'Text too short (minimum 3 characters). Example: {"text": "Hello world"}' - ) - if len(v) > 10000: - raise ValueError(f"Text too long (maximum 10,000 characters). Got {len(v)} characters.") - return v - - -@app.post("/classify") -async def classify_text(request: ClassifyRequest): - """ - Complete ML pipeline: CPU → GPU → CPU - - Pipeline stages: - 1. CPU Preprocessing: Validate and clean text (fast, cheap) - 2. GPU Inference: Run ML model (expensive, necessary) - 3. CPU Postprocessing: Format results (fast, cheap) - - This architecture minimizes GPU usage and maximizes cost-effectiveness. - """ - try: - # Stage 1: Preprocess on CPU (fast, cheap) - # Note: Input validation already done by Pydantic at API layer - preprocess_result = await preprocess_text({"text": request.text}) - - # Stage 2: GPU inference (expensive, necessary) - gpu_result = await gpu_inference( - { - "cleaned_text": preprocess_result["cleaned_text"], - "word_count": preprocess_result["word_count"], - } - ) - - # Stage 3: Postprocess on CPU (fast, cheap) - final_result = await postprocess_results( - { - "predictions": gpu_result["predictions"], - "original_text": request.text, - "metadata": { - "word_count": preprocess_result["word_count"], - "sentence_count": preprocess_result["sentence_count"], - "model": gpu_result["model_info"], - }, - } - ) - - return final_result - - except Exception as e: - logger.error(f"Pipeline error: {e}") - raise HTTPException(status_code=500, detail=f"Processing error: {e!s}") from e - - -@app.get("/", tags=["Info"]) -def home(): - return { - "message": "Mixed GPU/CPU Flash Application", - "description": "Cost-effective ML pipeline combining CPU and GPU workers", - "docs": "/docs", - "pipeline": { - "classify": "POST /classify - Complete pipeline (CPU → GPU → CPU)", - "cpu_preprocess": "POST /cpu/preprocess - CPU text preprocessing", - "gpu_inference": "POST /gpu/inference - GPU ML inference", - "cpu_postprocess": "POST /cpu/postprocess - CPU result formatting", - }, - "architecture": { - "pattern": "CPU (cheap) → GPU (expensive) → CPU (cheap)", - "cost_savings": "85% vs GPU-only pipeline", - }, - } - - -@app.get("/health", tags=["Info"]) -def health(): - return { - "status": "healthy", - "workers": { - "cpu_preprocess": "ready", - "gpu_inference": "ready", - "cpu_postprocess": "ready", - }, - } - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting Mixed GPU/CPU Flash server on {host}:{port}") - - uvicorn.run(app, host=host, port=port) diff --git a/01_getting_started/03_mixed_workers/mothership.py b/01_getting_started/03_mixed_workers/mothership.py deleted file mode 100644 index 513d841..0000000 --- a/01_getting_started/03_mixed_workers/mothership.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="01_03_mixed_workers-mothership", - # workersMin=1, - # workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership-01_03_mixed_workers", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership-01_03_mixed_workers", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/01_getting_started/03_mixed_workers/pipeline.py b/01_getting_started/03_mixed_workers/pipeline.py new file mode 100644 index 0000000..62b6869 --- /dev/null +++ b/01_getting_started/03_mixed_workers/pipeline.py @@ -0,0 +1,36 @@ +# Classification pipeline: CPU preprocess -> GPU inference -> CPU postprocess. +# Demonstrates cross-worker orchestration via a load-balanced endpoint. +# Run with: flash run +from cpu_worker import postprocess_results, preprocess_text +from gpu_worker import gpu_inference +from runpod_flash import CpuLiveLoadBalancer, remote + +pipeline_config = CpuLiveLoadBalancer( + name="01_03_classify_pipeline", + workersMin=1, +) + + +@remote(resource_config=pipeline_config, method="POST", path="/classify") +async def classify(text: str) -> dict: + """Complete ML pipeline: CPU preprocess -> GPU inference -> CPU postprocess.""" + preprocess_result = await preprocess_text({"text": text}) + + gpu_result = await gpu_inference( + { + "cleaned_text": preprocess_result["cleaned_text"], + "word_count": preprocess_result["word_count"], + } + ) + + return await postprocess_results( + { + "predictions": gpu_result["predictions"], + "original_text": text, + "metadata": { + "word_count": preprocess_result["word_count"], + "sentence_count": preprocess_result["sentence_count"], + "model": gpu_result["model_info"], + }, + } + ) diff --git a/01_getting_started/03_mixed_workers/requirements.txt b/01_getting_started/03_mixed_workers/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/01_getting_started/03_mixed_workers/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/01_getting_started/03_mixed_workers/workers/__init__.py b/01_getting_started/03_mixed_workers/workers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/01_getting_started/03_mixed_workers/workers/cpu/__init__.py b/01_getting_started/03_mixed_workers/workers/cpu/__init__.py deleted file mode 100644 index a522189..0000000 --- a/01_getting_started/03_mixed_workers/workers/cpu/__init__.py +++ /dev/null @@ -1,85 +0,0 @@ -from fastapi import APIRouter -from pydantic import BaseModel, field_validator - -from .endpoint import postprocess_results, preprocess_text - -cpu_router = APIRouter() - - -class PreprocessRequest(BaseModel): - """Request model for text preprocessing.""" - - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - if not v or not v.strip(): - raise ValueError("Text cannot be empty") - if len(v) < 3: - raise ValueError( - 'Text too short (minimum 3 characters). Example: {"text": "Hello world"}' - ) - if len(v) > 10000: - raise ValueError(f"Text too long (maximum 10,000 characters). Got {len(v)} characters.") - return v - - -@cpu_router.post("/preprocess") -async def preprocess_endpoint(request: PreprocessRequest): - """ - Preprocess text before GPU inference. - - Fast, cheap CPU operation for validation and cleaning. - """ - result = await preprocess_text({"text": request.text}) - return result - - -class Prediction(BaseModel): - """Individual prediction with label and confidence.""" - - label: str - confidence: float - - @field_validator("confidence") - @classmethod - def validate_confidence(cls, v): - if not 0.0 <= v <= 1.0: - raise ValueError(f"Confidence must be between 0.0 and 1.0, got {v}") - return v - - -class PostprocessRequest(BaseModel): - """Request model for result postprocessing.""" - - predictions: list[Prediction] - original_text: str - metadata: dict - - @field_validator("predictions") - @classmethod - def validate_predictions_not_empty(cls, v): - if not v: - raise ValueError( - 'Predictions cannot be empty. Example: [{"label": "positive", "confidence": 0.9}]' - ) - return v - - -@cpu_router.post("/postprocess") -async def postprocess_endpoint(request: PostprocessRequest): - """ - Postprocess GPU inference results. - - Fast, cheap CPU operation for formatting and aggregation. - """ - # Convert Pydantic models to dicts for remote serialization - result = await postprocess_results( - { - "predictions": [pred.model_dump() for pred in request.predictions], - "original_text": request.original_text, - "metadata": request.metadata, - } - ) - return result diff --git a/01_getting_started/03_mixed_workers/workers/gpu/__init__.py b/01_getting_started/03_mixed_workers/workers/gpu/__init__.py deleted file mode 100644 index 01fe478..0000000 --- a/01_getting_started/03_mixed_workers/workers/gpu/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -from fastapi import APIRouter -from pydantic import BaseModel, Field, field_validator - -from .endpoint import gpu_inference - -gpu_router = APIRouter() - - -class InferenceRequest(BaseModel): - """Request model for GPU inference.""" - - cleaned_text: str - word_count: int = Field(ge=0) - - @field_validator("cleaned_text") - @classmethod - def validate_text_not_empty(cls, v): - if not v or not v.strip(): - raise ValueError("Cleaned text cannot be empty") - return v - - -@gpu_router.post("/inference") -async def inference_endpoint(request: InferenceRequest): - """ - Run ML inference on GPU. - - Expensive GPU operation - only use after CPU preprocessing. - """ - result = await gpu_inference( - { - "cleaned_text": request.cleaned_text, - "word_count": request.word_count, - } - ) - return result diff --git a/01_getting_started/04_dependencies/.env.example b/01_getting_started/04_dependencies/.env.example deleted file mode 100644 index 91af5f2..0000000 --- a/01_getting_started/04_dependencies/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# RUNPOD_API_KEY=your_api_key_here -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO diff --git a/01_getting_started/04_dependencies/README.md b/01_getting_started/04_dependencies/README.md index 93c30b9..eaa5a8f 100644 --- a/01_getting_started/04_dependencies/README.md +++ b/01_getting_started/04_dependencies/README.md @@ -8,7 +8,6 @@ Learn how to manage Python packages and system dependencies in Flash workers. - **System dependencies** - Installing apt packages (ffmpeg, libgl1, etc.) - **Version pinning** - Reproducible builds with exact versions - **Dependency optimization** - Minimizing cold start time -- **Input validation** - Using Pydantic field validators for data quality ## Quick Start @@ -75,10 +74,10 @@ Install apt packages: async def process_video(data: dict) -> dict: import cv2 import subprocess - + # FFmpeg available subprocess.run(["ffmpeg", "-version"]) - + # OpenCV works (needs libgl1) cap = cv2.VideoCapture("video.mp4") ``` @@ -97,293 +96,6 @@ async def simple_function(data: dict) -> dict: return {"result": "processed"} ``` -## Input Validation with Pydantic - -Flash uses FastAPI and Pydantic for request validation. Validate inputs at the API layer before they reach your worker functions. - -### Why Validate? - -- **Prevent errors** - Catch invalid data before processing -- **Better error messages** - Clear feedback to API consumers -- **Type safety** - Enforce data structure and types -- **Documentation** - Pydantic models auto-generate API docs - -### Basic Validation - -Define request models with type hints: - -```python -from pydantic import BaseModel - -class DataRequest(BaseModel): - """Request model with automatic validation.""" - data: list[list[int]] # List of lists of integers - threshold: float = 0.5 # Optional with default -``` - -FastAPI automatically: -- Validates types (returns 422 if invalid) -- Generates OpenAPI docs -- Provides helpful error messages - -### Field Validators - -Use `@field_validator` for custom validation logic: - -```python -from pydantic import BaseModel, field_validator - -class DataRequest(BaseModel): - data: list[list[int]] - - @field_validator("data") - @classmethod - def validate_two_columns(cls, v): - if not v: - raise ValueError("Data cannot be empty") - - # Require at least 2 rows for statistics - if len(v) < 2: - raise ValueError( - f"Need at least 2 rows to compute statistics, got {len(v)}. " - f'Example: {{"data": [[1, 2], [3, 4]]}}' - ) - - # Check each row has exactly 2 columns - for i, row in enumerate(v): - if len(row) != 2: - raise ValueError( - f"Row {i} has {len(row)} columns, expected exactly 2. " - f'Example: {{"data": [[1, 2], [3, 4]]}}' - ) - - return v -``` - -This example (from `workers/cpu/__init__.py:14-30`) validates: -1. Data is not empty -2. At least 2 rows (prevents NaN in statistics) -3. Each row has exactly 2 columns - -### Validation in FastAPI Router - -Connect request models to endpoints: - -```python -from fastapi import APIRouter -from pydantic import BaseModel, field_validator - -router = APIRouter() - -class DataRequest(BaseModel): - data: list[list[int]] - - @field_validator("data") - @classmethod - def validate_structure(cls, v): - # Custom validation logic - return v - -@router.post("/data") -async def process_endpoint(request: DataRequest): - """FastAPI validates request automatically.""" - result = await process_data({"data": request.data}) - return result -``` - -### Testing Validation - -Valid requests: -```bash -curl -X POST http://localhost:8888/cpu/data \ - -H "Content-Type: application/json" \ - -d '{"data": [[1, 2], [3, 4], [5, 6]]}' -``` - -Invalid requests return 422 with helpful errors: - -```bash -# Too few rows -curl -X POST http://localhost:8888/cpu/data \ - -H "Content-Type: application/json" \ - -d '{"data": [[1, 2]]}' - -# Response: -{ - "detail": [ - { - "type": "value_error", - "msg": "Value error, Need at least 2 rows to compute statistics, got 1. Example: {\"data\": [[1, 2], [3, 4]]}" - } - ] -} -``` - -```bash -# Wrong column count -curl -X POST http://localhost:8888/cpu/data \ - -H "Content-Type: application/json" \ - -d '{"data": [[1, 2, 3], [4, 5, 6]]}' - -# Response: -{ - "detail": [ - { - "type": "value_error", - "msg": "Value error, Row 0 has 3 columns, expected exactly 2. Example: {\"data\": [[1, 2], [3, 4]]}" - } - ] -} -``` - -### Validation Best Practices - -**1. Validate early** - At the API layer, not in worker functions - -```python -# ✅ GOOD - Validation in Pydantic model -class DataRequest(BaseModel): - data: list[list[int]] - - @field_validator("data") - @classmethod - def validate_data(cls, v): - # Validation logic here - return v - -@router.post("/process") -async def endpoint(request: DataRequest): - result = await worker(request.data) # Already validated - return result - -# ❌ BAD - Validation in worker function -@remote(resource_config=config) -async def worker(input_data: dict): - data = input_data["data"] - if not data or not all(len(row) == 2 for row in data): - return {"status": "error", "error": "Invalid data"} - # Process data... -``` - -**2. Provide helpful error messages** - -```python -# ✅ GOOD - Clear, actionable message -raise ValueError( - f"Row {i} has {len(row)} columns, expected exactly 2. " - f'Example: {{"data": [[1, 2], [3, 4]]}}' -) - -# ❌ BAD - Vague message -raise ValueError("Invalid data format") -``` - -**3. Validate constraints, not just types** - -```python -from pydantic import BaseModel, field_validator, Field - -class ImageRequest(BaseModel): - width: int = Field(gt=0, le=4096) # 1-4096 - height: int = Field(gt=0, le=4096) # 1-4096 - quality: int = Field(ge=1, le=100) # 1-100 - - @field_validator("width", "height") - @classmethod - def validate_dimensions(cls, v): - if v % 8 != 0: - raise ValueError(f"Dimension must be divisible by 8, got {v}") - return v -``` - -**4. Use multiple validators for complex validation** - -```python -class DataRequest(BaseModel): - data: list[list[float]] - normalize: bool = False - - @field_validator("data") - @classmethod - def validate_not_empty(cls, v): - if not v: - raise ValueError("Data cannot be empty") - return v - - @field_validator("data") - @classmethod - def validate_dimensions(cls, v): - # Check all rows have same length - if len(set(len(row) for row in v)) > 1: - raise ValueError("All rows must have same length") - return v -``` - -### Common Validation Patterns - -**Range validation:** -```python -from pydantic import Field - -class Request(BaseModel): - temperature: float = Field(ge=0.0, le=1.0) # 0.0 to 1.0 - max_tokens: int = Field(gt=0, le=4096) # 1 to 4096 -``` - -**String validation:** -```python -from pydantic import field_validator -import re - -class TextRequest(BaseModel): - text: str - - @field_validator("text") - @classmethod - def validate_text(cls, v): - if len(v) < 10: - raise ValueError("Text must be at least 10 characters") - if len(v) > 10000: - raise ValueError("Text must not exceed 10,000 characters") - return v -``` - -**List validation:** -```python -class BatchRequest(BaseModel): - items: list[str] - - @field_validator("items") - @classmethod - def validate_batch_size(cls, v): - if len(v) == 0: - raise ValueError("Batch cannot be empty") - if len(v) > 100: - raise ValueError("Batch size cannot exceed 100 items") - return v -``` - -**Enum validation:** -```python -from enum import Enum -from pydantic import BaseModel - -class OutputFormat(str, Enum): - JSON = "json" - CSV = "csv" - PARQUET = "parquet" - -class ExportRequest(BaseModel): - data: list[dict] - format: OutputFormat # Only accepts "json", "csv", or "parquet" -``` - -### Resources - -- [Pydantic Documentation](https://docs.pydantic.dev/) -- [FastAPI Request Validation](https://fastapi.tiangolo.com/tutorial/body/) -- [Field Validators Guide](https://docs.pydantic.dev/latest/concepts/validators/) - ## Version Constraints ### Exact Version (==) @@ -485,14 +197,14 @@ system_dependencies=["ffmpeg", "libgl1", "wget"] ### 1. Pin Versions for Production ```python -# ✅ GOOD - Reproducible +# Good - Reproducible dependencies=[ "torch==2.1.0", "transformers==4.35.2", "numpy==1.26.2", ] -# ❌ BAD - Unpredictable +# Bad - Unpredictable dependencies=[ "torch", # Version changes over time "transformers", @@ -503,7 +215,7 @@ dependencies=[ ### 2. Minimize Dependencies ```python -# ✅ GOOD - Only what's needed +# Good - Only what's needed @remote( dependencies=["requests"] # Just one package ) @@ -511,7 +223,7 @@ async def fetch_data(url: str): import requests return requests.get(url).json() -# ❌ BAD - Unnecessary bloat +# Bad - Unnecessary bloat @remote( dependencies=[ "requests", @@ -529,8 +241,8 @@ async def fetch_data(url: str): ```bash # Test locally first -python -m workers.gpu.endpoint -python -m workers.cpu.endpoint +python gpu_worker.py +python cpu_worker.py ``` ### 4. Document Dependencies diff --git a/01_getting_started/04_dependencies/__init__.py b/01_getting_started/04_dependencies/__init__.py deleted file mode 100644 index f8afef5..0000000 --- a/01_getting_started/04_dependencies/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Dependencies example.""" diff --git a/01_getting_started/04_dependencies/workers/cpu/endpoint.py b/01_getting_started/04_dependencies/cpu_worker.py similarity index 96% rename from 01_getting_started/04_dependencies/workers/cpu/endpoint.py rename to 01_getting_started/04_dependencies/cpu_worker.py index 980b104..9abac4e 100644 --- a/01_getting_started/04_dependencies/workers/cpu/endpoint.py +++ b/01_getting_started/04_dependencies/cpu_worker.py @@ -1,3 +1,6 @@ +# CPU workers demonstrating data science and zero-dependency patterns. +# Run with: flash run +# Test directly: python cpu_worker.py from runpod_flash import CpuInstanceType, CpuLiveServerless, remote # Worker with data science dependencies @@ -119,10 +122,6 @@ async def minimal_process(input_data: dict) -> dict: if __name__ == "__main__": import asyncio - from dotenv import find_dotenv, load_dotenv - - load_dotenv(find_dotenv()) # Find and load root .env file - async def test(): print("\n=== Testing Data Dependencies ===") data_result = await process_data({"data": [[1, 2], [3, 4], [5, 6]]}) diff --git a/01_getting_started/04_dependencies/workers/gpu/endpoint.py b/01_getting_started/04_dependencies/gpu_worker.py similarity index 96% rename from 01_getting_started/04_dependencies/workers/gpu/endpoint.py rename to 01_getting_started/04_dependencies/gpu_worker.py index 624acf3..64970bf 100644 --- a/01_getting_started/04_dependencies/workers/gpu/endpoint.py +++ b/01_getting_started/04_dependencies/gpu_worker.py @@ -1,3 +1,6 @@ +# GPU workers demonstrating Python and system dependency management. +# Run with: flash run +# Test directly: python gpu_worker.py from runpod_flash import GpuGroup, LiveServerless, remote # Worker with ML dependencies (versioned) @@ -109,10 +112,6 @@ async def process_with_system_deps(input_data: dict) -> dict: if __name__ == "__main__": import asyncio - from dotenv import find_dotenv, load_dotenv - - load_dotenv(find_dotenv()) # Find and load root .env file - async def test(): print("\n=== Testing ML Dependencies ===") ml_result = await process_with_ml_libs({}) diff --git a/01_getting_started/04_dependencies/main.py b/01_getting_started/04_dependencies/main.py deleted file mode 100644 index 11d07d0..0000000 --- a/01_getting_started/04_dependencies/main.py +++ /dev/null @@ -1,62 +0,0 @@ -import logging -import os - -from fastapi import FastAPI -from workers.cpu import cpu_router -from workers.gpu import gpu_router - -logger = logging.getLogger(__name__) - - -app = FastAPI( - title="Dependency Management Examples", - description="Learn how to manage Python and system dependencies in Flash workers", - version="0.1.0", -) - -app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"]) -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"]) - - -@app.get("/", tags=["Info"]) -def home(): - return { - "message": "Flash Dependency Management Examples", - "description": "Examples of Python and system dependency management", - "docs": "/docs", - "examples": { - "ml_deps": "POST /gpu/ml-deps - ML dependencies (torch, pillow, numpy)", - "system_deps": "POST /gpu/system-deps - System dependencies (ffmpeg, libgl1)", - "data_deps": "POST /cpu/data - Data science dependencies (pandas, scipy)", - "minimal": "POST /cpu/minimal - No dependencies (fastest)", - }, - "concepts": [ - "Version pinning (torch==2.1.0)", - "Version constraints (>=, <, ~=)", - "System packages via apt", - "Minimal dependencies for fast cold start", - ], - } - - -@app.get("/health", tags=["Info"]) -def health(): - return { - "status": "healthy", - "workers": { - "ml_deps": "GPU worker with torch, pillow, numpy", - "system_deps": "GPU worker with ffmpeg, libgl1", - "data_deps": "CPU worker with pandas, scipy", - "minimal": "CPU worker with no dependencies", - }, - } - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting Dependency Management server on {host}:{port}") - - uvicorn.run(app, host=host, port=port) diff --git a/01_getting_started/04_dependencies/mothership.py b/01_getting_started/04_dependencies/mothership.py deleted file mode 100644 index dcc91aa..0000000 --- a/01_getting_started/04_dependencies/mothership.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="01_04_dependencies-mothership", - # workersMin=1, - # workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership-01_04_dependencies", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership-01_04_dependencies", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/01_getting_started/04_dependencies/requirements.txt b/01_getting_started/04_dependencies/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/01_getting_started/04_dependencies/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/01_getting_started/04_dependencies/workers/__init__.py b/01_getting_started/04_dependencies/workers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/01_getting_started/04_dependencies/workers/cpu/__init__.py b/01_getting_started/04_dependencies/workers/cpu/__init__.py deleted file mode 100644 index 180925f..0000000 --- a/01_getting_started/04_dependencies/workers/cpu/__init__.py +++ /dev/null @@ -1,50 +0,0 @@ -from fastapi import APIRouter -from pydantic import BaseModel, field_validator - -from .endpoint import minimal_process, process_data - -cpu_router = APIRouter() - - -class DataRequest(BaseModel): - """Request model for data processing.""" - - data: list[list[int]] - - @field_validator("data") - @classmethod - def validate_two_columns(cls, v): - if not v: - raise ValueError("Data cannot be empty") - if len(v) < 2: - raise ValueError( - f"Need at least 2 rows to compute statistics, got {len(v)}. " - f'Example: {{"data": [[1, 2], [3, 4]]}}' - ) - for i, row in enumerate(v): - if len(row) != 2: - raise ValueError( - f"Row {i} has {len(row)} columns, expected exactly 2. " - f'Example: {{"data": [[1, 2], [3, 4]]}}' - ) - return v - - -class TextRequest(BaseModel): - """Request model for text processing.""" - - text: str - - -@cpu_router.post("/data") -async def data_endpoint(request: DataRequest): - """Test worker with data science dependencies (pandas, numpy, scipy).""" - result = await process_data({"data": request.data}) - return result - - -@cpu_router.post("/minimal") -async def minimal_endpoint(request: TextRequest): - """Test worker with NO dependencies (fastest cold start).""" - result = await minimal_process({"text": request.text}) - return result diff --git a/01_getting_started/04_dependencies/workers/gpu/__init__.py b/01_getting_started/04_dependencies/workers/gpu/__init__.py deleted file mode 100644 index c47de1e..0000000 --- a/01_getting_started/04_dependencies/workers/gpu/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from fastapi import APIRouter - -from .endpoint import process_with_ml_libs, process_with_system_deps - -gpu_router = APIRouter() - - -@gpu_router.post("/ml-deps") -async def ml_deps_endpoint(): - """Test worker with ML dependencies (torch, pillow, numpy).""" - result = await process_with_ml_libs({}) - return result - - -@gpu_router.post("/system-deps") -async def system_deps_endpoint(): - """Test worker with system dependencies (ffmpeg, libgl1).""" - result = await process_with_system_deps({}) - return result diff --git a/01_getting_started/README.md b/01_getting_started/README.md index 881e275..9729a00 100644 --- a/01_getting_started/README.md +++ b/01_getting_started/README.md @@ -12,12 +12,11 @@ The simplest Flash application with GPU workers - Creating GPU workers - Using the `@remote` decorator - Running Flash applications locally -- Testing endpoints with FastAPI docs +- Testing endpoints with Swagger docs **Concepts:** - `LiveServerless` configuration for GPU workers - Worker auto-scaling (min/max workers) -- FastAPI router integration ### [02_cpu_worker](./02_cpu_worker/) CPU-only worker example for non-GPU workloads. @@ -35,14 +34,11 @@ Mixed GPU/CPU workers for cost-effective ML pipelines. - Mixed worker architecture (CPU preprocessing → GPU inference → CPU postprocessing) - Cost optimization (85% savings vs GPU-only pipeline) - Pipeline orchestration patterns -- Pydantic input validation at each stage - Fail-fast validation before expensive GPU operations -- Remote serialization with .model_dump() **Concepts:** - `CpuLiveServerless` for preprocessing and postprocessing -- Pipeline orchestration with FastAPI -- Validation patterns for production APIs +- Pipeline orchestration with load-balanced endpoints ### [04_dependencies](./04_dependencies/) Managing Python packages and system dependencies. @@ -50,7 +46,6 @@ Managing Python packages and system dependencies. **What you'll learn:** - Python dependency versioning and constraints - System package installation (ffmpeg, libgl1) -- Input validation with Pydantic field validators - Version constraints (==, >=, <, ~=) - Minimizing cold start time - Best practices for reproducible builds @@ -59,7 +54,6 @@ Managing Python packages and system dependencies. - `dependencies` parameter for Python packages - `system_dependencies` parameter for apt packages - Version pinning for reproducibility -- Pydantic `@field_validator` for request validation - Dependency optimization strategies ## Learning Path diff --git a/02_ml_inference/01_text_to_speech/.env.example b/02_ml_inference/01_text_to_speech/.env.example deleted file mode 100644 index 8360712..0000000 --- a/02_ml_inference/01_text_to_speech/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO -# RUNPOD_API_KEY=your_api_key_here diff --git a/02_ml_inference/01_text_to_speech/README.md b/02_ml_inference/01_text_to_speech/README.md index b5b565b..c669660 100644 --- a/02_ml_inference/01_text_to_speech/README.md +++ b/02_ml_inference/01_text_to_speech/README.md @@ -39,39 +39,29 @@ First run provisions the endpoint (~1 min). Server starts at http://localhost:88 ### Test the Endpoint -**Get WAV file directly:** -```bash -curl -X POST http://localhost:8888/gpu/tts/audio \ - -H "Content-Type: application/json" \ - -d '{"text": "Hello world!", "speaker": "Ryan", "language": "English"}' \ - --output speech.wav -``` +Visit http://localhost:8888/docs for interactive API documentation. QB endpoints are auto-generated by `flash run` based on your `@remote` functions. -**Get JSON with base64 audio:** +**Generate speech (JSON with base64 audio):** ```bash -curl -X POST http://localhost:8888/gpu/tts \ +curl -X POST http://localhost:8888/gpu_worker/run_sync \ -H "Content-Type: application/json" \ -d '{"text": "Hello world!", "speaker": "Ryan", "language": "English"}' ``` -**With emotion/style control:** +**List available voices:** ```bash -curl -X POST http://localhost:8888/gpu/tts/audio \ +curl -X POST http://localhost:8888/gpu_worker/run_sync \ -H "Content-Type: application/json" \ - -d '{"text": "I have great news!", "speaker": "Ryan", "language": "English", "instruct": "Speak with excitement and joy"}' \ - --output excited.wav + -d '{}' ``` -**List available voices:** -```bash -curl http://localhost:8888/gpu/voices -``` +Check `/docs` for the exact auto-generated endpoint paths and schemas. -Visit http://localhost:8888/docs for interactive API documentation. +## API Functions -## API Endpoints +QB (queue-based) endpoints are auto-generated from `@remote` functions. Visit `/docs` for the full API schema. -### POST /gpu/tts +### `generate_speech` Generate speech and return JSON with base64-encoded WAV audio. @@ -96,13 +86,7 @@ Generate speech and return JSON with base64-encoded WAV audio. } ``` -### POST /gpu/tts/audio - -Generate speech and return raw WAV file directly. - -Same request body as `/gpu/tts`. Returns `audio/wav` content type. - -### GET /gpu/voices +### `get_voices` List available voices and supported languages. @@ -153,7 +137,7 @@ flash deploy send production - **Cold start delay**: First request after idle takes 20-30s to load the model. Use `flash run --auto-provision` during development. - **Out of memory**: The model requires 24GB+ VRAM. Ensure `GpuGroup.ADA_24` or higher is configured. -- **Invalid speaker/language**: Check `/gpu/voices` for valid options. +- **Invalid speaker/language**: Use `get_voices` to check valid options. ## References diff --git a/02_ml_inference/01_text_to_speech/__init__.py b/02_ml_inference/01_text_to_speech/__init__.py deleted file mode 100644 index 1a418c3..0000000 --- a/02_ml_inference/01_text_to_speech/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Text-to-speech example using Qwen3-TTS.""" diff --git a/02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py b/02_ml_inference/01_text_to_speech/gpu_worker.py similarity index 97% rename from 02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py rename to 02_ml_inference/01_text_to_speech/gpu_worker.py index 83bd818..87eaff9 100644 --- a/02_ml_inference/01_text_to_speech/workers/gpu/endpoint.py +++ b/02_ml_inference/01_text_to_speech/gpu_worker.py @@ -1,3 +1,7 @@ +# Qwen3-TTS text-to-speech GPU worker. +# Run with: flash run +# Test directly: python gpu_worker.py + from runpod_flash import GpuGroup, LiveServerless, remote # GPU config for Qwen3-TTS - needs 24GB+ VRAM for 1.7B model @@ -162,7 +166,7 @@ async def get_voices(input_data: dict) -> dict: } -# Test locally with: python -m workers.gpu.endpoint +# Test locally with: python gpu_worker.py if __name__ == "__main__": import asyncio diff --git a/02_ml_inference/01_text_to_speech/main.py b/02_ml_inference/01_text_to_speech/main.py deleted file mode 100644 index 2852d8e..0000000 --- a/02_ml_inference/01_text_to_speech/main.py +++ /dev/null @@ -1,44 +0,0 @@ -import logging -import os - -from fastapi import FastAPI -from workers.gpu import gpu_router - -logger = logging.getLogger(__name__) - - -app = FastAPI( - title="Qwen3-TTS API", - description="Text-to-Speech API using Qwen3-TTS on RunPod serverless GPUs", - version="1.0.0", -) - -app.include_router(gpu_router, prefix="/gpu", tags=["TTS"]) - - -@app.get("/") -def home(): - return { - "message": "Qwen3-TTS API", - "docs": "/docs", - "endpoints": { - "tts": "/gpu/tts", - "tts_audio": "/gpu/tts/audio", - "voices": "/gpu/voices", - }, - } - - -@app.get("/ping") -def ping(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting Flash server on {host}:{port}") - - uvicorn.run(app, host=host, port=port) diff --git a/02_ml_inference/01_text_to_speech/mothership.py b/02_ml_inference/01_text_to_speech/mothership.py deleted file mode 100644 index 8b2390d..0000000 --- a/02_ml_inference/01_text_to_speech/mothership.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below - -Documentation: https://docs.runpod.io/flash/mothership -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="02_01_text_to_speech-mothership", - workersMin=1, - workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/02_ml_inference/01_text_to_speech/pyproject.toml b/02_ml_inference/01_text_to_speech/pyproject.toml index 4396186..332c836 100644 --- a/02_ml_inference/01_text_to_speech/pyproject.toml +++ b/02_ml_inference/01_text_to_speech/pyproject.toml @@ -5,6 +5,4 @@ description = "Text-to-Speech using Qwen3-TTS on RunPod serverless GPUs" requires-python = ">=3.10" dependencies = [ "runpod-flash", - "fastapi>=0.104.0", - "uvicorn>=0.24.0", ] diff --git a/02_ml_inference/01_text_to_speech/requirements.txt b/02_ml_inference/01_text_to_speech/requirements.txt deleted file mode 100644 index a73ed1a..0000000 --- a/02_ml_inference/01_text_to_speech/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash diff --git a/02_ml_inference/01_text_to_speech/workers/__init__.py b/02_ml_inference/01_text_to_speech/workers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py b/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py deleted file mode 100644 index 6c8f26d..0000000 --- a/02_ml_inference/01_text_to_speech/workers/gpu/__init__.py +++ /dev/null @@ -1,81 +0,0 @@ -import base64 - -from fastapi import APIRouter -from fastapi.responses import Response -from pydantic import BaseModel, Field - -from .endpoint import generate_speech, get_voices - -gpu_router = APIRouter() - - -class TTSRequest(BaseModel): - """Request model for text-to-speech generation.""" - - text: str = Field(..., description="The text to synthesize into speech") - speaker: str = Field( - default="Ryan", - description="Voice to use. Options: Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee", - ) - language: str = Field( - default="Auto", - description="Language. Options: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian, Auto", - ) - instruct: str | None = Field( - default=None, - description="Optional emotion/style instruction (e.g., 'Speak happily', 'Say it slowly and calmly')", - ) - - -@gpu_router.post("/tts") -async def text_to_speech(request: TTSRequest) -> dict: - """ - Generate speech from text using Qwen3-TTS. - - Returns JSON with base64-encoded WAV audio. - """ - payload = { - "text": request.text, - "speaker": request.speaker, - "language": request.language, - } - if request.instruct: - payload["instruct"] = request.instruct - - result = await generate_speech(payload) - return result - - -@gpu_router.post("/tts/audio") -async def text_to_speech_audio(request: TTSRequest) -> Response: - """ - Generate speech from text using Qwen3-TTS. - - Returns raw WAV audio file directly. - """ - payload = { - "text": request.text, - "speaker": request.speaker, - "language": request.language, - } - if request.instruct: - payload["instruct"] = request.instruct - - result = await generate_speech(payload) - - if result["status"] == "error": - return result - - audio_bytes = base64.b64decode(result["audio_base64"]) - return Response( - content=audio_bytes, - media_type="audio/wav", - headers={"Content-Disposition": "attachment; filename=speech.wav"}, - ) - - -@gpu_router.get("/voices") -async def list_voices() -> dict: - """Get available voices and languages.""" - result = await get_voices({}) - return result diff --git a/03_advanced_workers/05_load_balancer/.env.example b/03_advanced_workers/05_load_balancer/.env.example deleted file mode 100644 index 8360712..0000000 --- a/03_advanced_workers/05_load_balancer/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -# FLASH_HOST=localhost -# FLASH_PORT=8888 -# LOG_LEVEL=INFO -# RUNPOD_API_KEY=your_api_key_here diff --git a/03_advanced_workers/05_load_balancer/README.md b/03_advanced_workers/05_load_balancer/README.md index 845a6a3..8004c92 100644 --- a/03_advanced_workers/05_load_balancer/README.md +++ b/03_advanced_workers/05_load_balancer/README.md @@ -81,15 +81,6 @@ curl -X POST http://localhost:8888/05_load_balancer/cpu/transform \ -d '{"text": "hello", "operation": "uppercase"}' ``` -### 5. Run Standalone (for this example only) - -```bash -cd 03_advanced_workers/05_load_balancer -python main.py -``` - -This runs the example on **http://localhost:8000** (default standalone port) without the example name prefix. The unified app uses port 8888, but standalone mode defaults to 8000 unless FLASH_PORT is set. - ## How Load-Balancer Endpoints Work ### Defining Routes with @remote Decorator @@ -160,17 +151,11 @@ The following paths are reserved and cannot be used: ``` 05_load_balancer/ -├── main.py # FastAPI application -├── workers/ -│ ├── gpu/ # GPU load-balancer endpoints -│ │ ├── __init__.py # Router with endpoints -│ │ └── endpoint.py # @remote functions -│ └── cpu/ # CPU load-balancer endpoints -│ ├── __init__.py # Router with endpoints -│ └── endpoint.py # @remote functions -├── .env.example # Environment template -├── requirements.txt # Dependencies -└── README.md # This file +├── gpu_lb.py # GPU load-balancer endpoints with @remote +├── cpu_lb.py # CPU load-balancer endpoints with @remote +├── .env.example # Environment template +├── requirements.txt # Dependencies +└── README.md # This file ``` ## GPU Service Endpoints @@ -334,10 +319,10 @@ async def process(data: dict) -> dict: ```bash # Test GPU worker -python -m workers.gpu.endpoint +python gpu_lb.py # Test CPU worker -python -m workers.cpu.endpoint +python cpu_lb.py ``` ## Deployment @@ -427,7 +412,7 @@ RUNPOD_API_KEY=your_api_key_here # Optional FLASH_HOST=localhost # Server host (default: localhost) -FLASH_PORT=8000 # Server port (default: 8000) +FLASH_PORT=8888 # Server port (default: 8888) LOG_LEVEL=INFO # Logging level (default: INFO) ``` @@ -485,14 +470,6 @@ For current pricing, see [RunPod Pricing](https://www.runpod.io/pricing). - Adjust `workersMin` to keep workers warm if consistent low latency is critical - Consider using `idleTimeout` to reduce cold starts -### Endpoint discovery not working - -**Problem**: Example doesn't load in unified app with `flash run` -- Ensure routers are named `gpu_router` and `cpu_router` -- Verify routers are properly exported in `__init__.py` files -- Check that `main.py` includes routers with `app.include_router()` -- Run `flash run` from the repository root, not the example directory - ## Next Steps 1. Explore the endpoints via Swagger UI (`/docs`) diff --git a/03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py b/03_advanced_workers/05_load_balancer/cpu_lb.py similarity index 96% rename from 03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py rename to 03_advanced_workers/05_load_balancer/cpu_lb.py index 2dd86a5..55949c4 100644 --- a/03_advanced_workers/05_load_balancer/workers/cpu/endpoint.py +++ b/03_advanced_workers/05_load_balancer/cpu_lb.py @@ -1,3 +1,6 @@ +# CPU load-balanced endpoints with custom HTTP routes. +# Run with: flash run +# Test directly: python cpu_lb.py from runpod_flash import CpuLiveLoadBalancer, remote cpu_config = CpuLiveLoadBalancer( @@ -95,7 +98,6 @@ async def transform_data(text: str, operation: str = "uppercase") -> dict: } -# Test locally with: python -m workers.cpu.endpoint if __name__ == "__main__": import asyncio diff --git a/03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py b/03_advanced_workers/05_load_balancer/gpu_lb.py similarity index 82% rename from 03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py rename to 03_advanced_workers/05_load_balancer/gpu_lb.py index f8c8d5c..28001c6 100644 --- a/03_advanced_workers/05_load_balancer/workers/gpu/endpoint.py +++ b/03_advanced_workers/05_load_balancer/gpu_lb.py @@ -1,3 +1,6 @@ +# GPU load-balanced endpoints with custom HTTP routes. +# Run with: flash run +# Test directly: python gpu_lb.py from runpod_flash import LiveLoadBalancer, remote gpu_config = LiveLoadBalancer( @@ -13,11 +16,11 @@ async def gpu_health() -> dict: @remote(gpu_config, method="POST", path="/compute") -async def compute_intensive(request: dict) -> dict: +async def compute_intensive(numbers: list[float]) -> dict: """Perform compute-intensive operation on GPU. Args: - request: Request dict with numbers to process + numbers: List of numbers to process Returns: Computation results @@ -25,14 +28,13 @@ async def compute_intensive(request: dict) -> dict: import time from datetime import datetime, timezone - numbers = request.get("numbers", []) start_time = time.time() # Simulate GPU-intensive computation result = sum(x**2 for x in numbers) - mean = sum(numbers) / len(numbers) - max_val = max(numbers) - min_val = min(numbers) + mean = sum(numbers) / len(numbers) if numbers else 0 + max_val = max(numbers) if numbers else None + min_val = min(numbers) if numbers else None compute_time = (time.time() - start_time) * 1000 @@ -68,7 +70,6 @@ async def gpu_info() -> dict: return info -# Test locally with: python -m workers.gpu.endpoint if __name__ == "__main__": import asyncio @@ -80,8 +81,7 @@ async def test(): print(f" {result}\n") print("2. Compute intensive:") - request_data = {"numbers": [1, 2, 3, 4, 5]} - result = await compute_intensive(request_data) + result = await compute_intensive([1.0, 2.0, 3.0, 4.0, 5.0]) print(f" Sum of squares: {result['sum_of_squares']}") print(f" Mean: {result['mean']}\n") diff --git a/03_advanced_workers/05_load_balancer/main.py b/03_advanced_workers/05_load_balancer/main.py deleted file mode 100644 index 7115e75..0000000 --- a/03_advanced_workers/05_load_balancer/main.py +++ /dev/null @@ -1,55 +0,0 @@ -import logging -import os - -from fastapi import FastAPI -from workers.cpu import cpu_router -from workers.gpu import gpu_router - -logger = logging.getLogger(__name__) - - -app = FastAPI( - title="Load Balancer Example", - description="Demonstrates load-balancer endpoints with custom HTTP routes using Flash", - version="0.1.0", -) - -# Include routers for unified app discovery -app.include_router(gpu_router, prefix="/gpu", tags=["GPU Compute Service"]) -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Data Service"]) - - -@app.get("/") -def home(): - return { - "message": "Load Balancer Example API", - "description": "Demonstrates load-balancer endpoints with custom HTTP routes", - "note": "Load-balancer endpoints are defined with @remote(resource, method=..., path=...)", - "docs": "/docs", - "gpu_endpoints": { - "health": "GET /gpu/health", - "compute": "POST /gpu/compute", - "info": "GET /gpu/info", - }, - "cpu_endpoints": { - "health": "GET /cpu/health", - "validate": "POST /cpu/validate", - "transform": "POST /cpu/transform", - }, - } - - -@app.get("/ping") -def ping(): - """Health check endpoint for load balancer.""" - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8000)) - logger.info(f"Starting Load Balancer Example on {host}:{port}") - - uvicorn.run(app, host=host, port=port) diff --git a/03_advanced_workers/05_load_balancer/mothership.py b/03_advanced_workers/05_load_balancer/mothership.py deleted file mode 100644 index 3e620be..0000000 --- a/03_advanced_workers/05_load_balancer/mothership.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Mothership Endpoint Configuration - -The mothership endpoint serves your FastAPI application routes. -It is automatically deployed as a CPU-optimized load-balanced endpoint. - -To customize this configuration: -- Modify worker scaling: change workersMin and workersMax values -- Use GPU load balancer: import LiveLoadBalancer instead of CpuLiveLoadBalancer -- Change endpoint name: update the 'name' parameter - -To disable mothership deployment: -- Delete this file, or -- Comment out the 'mothership' variable below -""" - -from runpod_flash import CpuLiveLoadBalancer - -# Mothership endpoint configuration -# This serves your FastAPI app routes from main.py -mothership = CpuLiveLoadBalancer( - name="03_05_load_balancer-mothership", - # workersMin=1, - # workersMax=3, -) - -# Examples of customization: - -# Increase scaling for high traffic -# mothership = CpuLiveLoadBalancer( -# name="mothership-03_05_load_balancer", -# workersMin=2, -# workersMax=10, -# ) - -# Use GPU-based load balancer instead of CPU -# (requires importing LiveLoadBalancer) -# from runpod_flash import LiveLoadBalancer -# mothership = LiveLoadBalancer( -# name="mothership-03_05_load_balancer", -# gpus=[GpuGroup.ANY], -# ) - -# Custom endpoint name -# mothership = CpuLiveLoadBalancer( -# name="my-api-gateway", -# workersMin=1, -# workersMax=3, -# ) - -# To disable mothership: -# - Delete this entire file, or -# - Comment out the 'mothership' variable above diff --git a/03_advanced_workers/05_load_balancer/requirements.txt b/03_advanced_workers/05_load_balancer/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/03_advanced_workers/05_load_balancer/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/03_advanced_workers/05_load_balancer/workers/__init__.py b/03_advanced_workers/05_load_balancer/workers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py b/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py deleted file mode 100644 index e4d1cb5..0000000 --- a/03_advanced_workers/05_load_balancer/workers/cpu/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -"""CPU Load-Balancer Endpoints - -Load-balancer endpoints use the @remote decorator with method and path parameters. -These decorators automatically create HTTP routes that are registered with the -Load Balancer runtime. - -For the unified app discovery, we export a router that documents the endpoints. -The actual HTTP handling is managed by the runpod-flash-lb framework. -""" - -from fastapi import APIRouter -from pydantic import BaseModel - -from .endpoint import cpu_health, transform_data, validate_data - - -# Pydantic models for request validation -class ValidateRequest(BaseModel): - """Request model for text validation.""" - - text: str - - -class TransformRequest(BaseModel): - """Request model for text transformation.""" - - text: str - operation: str = "uppercase" - - -# Export for unified app discovery -cpu_router = APIRouter() - - -@cpu_router.get("/health") -async def get_cpu_health(): - """CPU service health check.""" - return await cpu_health() - - -@cpu_router.post("/validate") -async def post_validate(request: ValidateRequest): - """Validate and analyze text data.""" - return await validate_data(request.text) - - -@cpu_router.post("/transform") -async def post_transform(request: TransformRequest): - """Transform text data with specified operation.""" - return await transform_data(request.text, request.operation) - - -__all__ = ["cpu_router"] diff --git a/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py b/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py deleted file mode 100644 index 1fe8c10..0000000 --- a/03_advanced_workers/05_load_balancer/workers/gpu/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -"""GPU Load-Balancer Endpoints - -Load-balancer endpoints use the @remote decorator with method and path parameters. -These decorators automatically create HTTP routes that are registered with the -Load Balancer runtime. - -For the unified app discovery, we export a router that documents the endpoints. -The actual HTTP handling is managed by the runpod-flash-lb framework. -""" - -from fastapi import APIRouter -from pydantic import BaseModel, field_validator - -from .endpoint import compute_intensive, gpu_health, gpu_info - - -class ComputeRequest(BaseModel): - """Request model for compute-intensive operations.""" - - numbers: list[int] - - @field_validator("numbers") - @classmethod - def validate_numbers(cls, v: list[int]) -> list[int]: - """Validate that numbers list is not empty.""" - if not v: - raise ValueError("numbers list cannot be empty") - return v - - -# Export for unified app discovery -gpu_router = APIRouter() - - -@gpu_router.get("/health") -async def get_gpu_health(): - """GPU service health check.""" - return await gpu_health() - - -@gpu_router.post("/compute") -async def post_gpu_compute(request: ComputeRequest): - """Perform compute-intensive operation on GPU.""" - return await compute_intensive(request.model_dump()) - - -@gpu_router.get("/info") -async def get_gpu_info(): - """Get GPU device information.""" - return await gpu_info() - - -__all__ = ["gpu_router"] diff --git a/05_data_workflows/01_network_volumes/README.md b/05_data_workflows/01_network_volumes/README.md index 511171d..d0b28bf 100644 --- a/05_data_workflows/01_network_volumes/README.md +++ b/05_data_workflows/01_network_volumes/README.md @@ -34,26 +34,25 @@ Server starts at `http://localhost:8888` ### 4. Test the API +**Generate an image (GPU worker):** ```bash -# Health check -curl http://localhost:8888/ping - -# Generate an image on the GPU worker -curl -X POST http://localhost:8888/gpu/generate \ +curl -X POST http://localhost:8888/gpu_worker/run_sync \ -H "Content-Type: application/json" \ - -d '{"prompt": "a lighthouse on a cliff at sunrise"}' + -d '{"prompt": "a sunset over mountains"}' +``` -# List generated images from the CPU worker -curl http://localhost:8888/cpu/image +**List generated images (CPU worker):** +```bash +curl http://localhost:8888/images +``` -# Fetch a single image by filename -curl http://localhost:8888/cpu/image/ --output image.png +**Get a specific image (CPU worker):** +```bash +curl http://localhost:8888/images/sd_generated_20240101_120000.png ``` Visit `http://localhost:8888/docs` for interactive API documentation. -Browse the image gallery at `http://localhost:8888/cpu/`. - ## What You'll Learn - How to attach a shared `NetworkVolume` to GPU and CPU workers @@ -69,26 +68,21 @@ Browse the image gallery at `http://localhost:8888/cpu/`. ``` 01_network_volumes/ -├── main.py -├── workers/ -│ ├── __init__.py # Network volume definition -│ ├── gpu/ -│ │ ├── __init__.py # GPU router -│ │ └── endpoint.py # Stable Diffusion worker -│ └── cpu/ -│ ├── __init__.py # CPU router -│ └── endpoint.py # List and serve images +├── gpu_worker.py # Stable Diffusion worker with @remote +├── cpu_worker.py # List and serve images with @remote ├── requirements.txt └── README.md ``` ## API Endpoints -### POST /gpu/generate +### POST /gpu_worker/run_sync + +GPU worker (QB, class-based `@remote`). Generates an image and saves it to the shared volume. **Request**: ```json -{ "prompt": "string" } +{ "prompt": "a sunset over mountains" } ``` **Response**: @@ -107,20 +101,22 @@ Browse the image gallery at `http://localhost:8888/cpu/`. } ``` -### GET /cpu/image +### GET /images + +CPU worker (LB, explicit path). Lists all generated images on the shared volume. **Response**: ```json -{ "status": "success", "images": ["file.png"] } +{ "status": "success", "images": ["sd_generated_20240101_120000.png"] } ``` -### GET /cpu/image/{file_id} +### GET /images/{file_name} -Returns the PNG file as `image/png`. +CPU worker (LB, explicit path). Returns metadata and base64-encoded content for a single image. ## Notes -- The network volume is defined in `workers/__init__.py` and attached to both workers. +- The network volume is defined inline in each worker file and attached to both workers. - Stable Diffusion weights are cached in the volume so cold starts are faster after the first run. ## Deployment diff --git a/05_data_workflows/01_network_volumes/cpu_worker.py b/05_data_workflows/01_network_volumes/cpu_worker.py new file mode 100644 index 0000000..d947c48 --- /dev/null +++ b/05_data_workflows/01_network_volumes/cpu_worker.py @@ -0,0 +1,60 @@ +# CPU worker with network volume for listing and serving generated images. +# Run with: flash run +# Test directly: python cpu_worker.py +from runpod_flash import CpuLiveLoadBalancer, NetworkVolume, remote + +volume = NetworkVolume( + name="flash-05-volume", + size=50, +) + +cpu_config = CpuLiveLoadBalancer( + name="05_01_cpu_worker", + workersMin=1, + networkVolume=volume, +) + + +@remote(resource_config=cpu_config, path="/images", method="GET") +async def list_images_in_volume() -> dict: + """List generated images from the shared volume.""" + import os + + image_dir = "/runpod-volume/generated_images" + if not os.path.isdir(image_dir): + return {"status": "success", "images": []} + + return { + "status": "success", + "images": os.listdir(image_dir), + } + + +@remote(resource_config=cpu_config, path="/images/{file_name}", method="GET") +async def get_image_from_volume(file_name: str) -> dict: + """Get image metadata from the shared volume.""" + import base64 + from pathlib import Path + + image_path = Path(f"/runpod-volume/generated_images/{file_name}") + + if not image_path.is_file(): + return {"status": "error", "error": "file not found"} + + image_bytes = image_path.read_bytes() + return { + "status": "success", + "filename": image_path.name, + "size_bytes": len(image_bytes), + "image_base64": base64.b64encode(image_bytes).decode("utf-8"), + } + + +if __name__ == "__main__": + import asyncio + + print( + "Checking cpu worker by listing image files in /runpod-volume/generated_images/" + ) + result = asyncio.run(list_images_in_volume()) + print(f"Result: {result}") diff --git a/05_data_workflows/01_network_volumes/workers/gpu/endpoint.py b/05_data_workflows/01_network_volumes/gpu_worker.py similarity index 71% rename from 05_data_workflows/01_network_volumes/workers/gpu/endpoint.py rename to 05_data_workflows/01_network_volumes/gpu_worker.py index e63c480..c948228 100644 --- a/05_data_workflows/01_network_volumes/workers/gpu/endpoint.py +++ b/05_data_workflows/01_network_volumes/gpu_worker.py @@ -1,17 +1,21 @@ -## GPU worker with network volumes -# In this example, a GPU worker runs Stable Diffusion and writes outputs to the shared volume. +# GPU worker with network volume for Stable Diffusion image generation. +# Run with: flash run +# Test directly: python gpu_worker.py import logging -from runpod_flash import GpuGroup, LiveServerless, remote - -from .. import volume +from runpod_flash import GpuGroup, LiveServerless, NetworkVolume, remote logger = logging.getLogger(__name__) MODEL_PATH = "/runpod-volume/models" +volume = NetworkVolume( + name="flash-05-volume", + size=50, +) + gpu_config = LiveServerless( - name="gpu_worker", + name="05_01_gpu_worker", gpus=[GpuGroup.ANY], workersMin=0, workersMax=3, @@ -39,22 +43,22 @@ def __init__(self): self.pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, - safety_checker=None, # Disable to save memory + safety_checker=None, use_safetensors=True, requires_safety_checker=False, - low_cpu_mem_usage=True, # Additional memory optimization + low_cpu_mem_usage=True, ) - # Move to GPU and enable small memory optimizations. self.pipe = self.pipe.to("cuda") - self.pipe.enable_attention_slicing() # Additional memory saving + self.pipe.enable_attention_slicing() - # Clean up any leftover memory gc.collect() torch.cuda.empty_cache() self.logger.info("Compact Stable Diffusion initialized successfully!") - self.logger.info(f"Model weights stored in {model_path}: {os.listdir(model_path)}") + self.logger.info( + f"Model weights stored in {model_path}: {os.listdir(model_path)}" + ) async def generate_image(self, prompt: str) -> dict: """Generate a single image from prompt.""" @@ -68,22 +72,19 @@ async def generate_image(self, prompt: str) -> dict: height=512, ).images[0] - # Save output into the shared volume for the CPU worker to read. import datetime import os output_dir = "/runpod-volume/generated_images" os.makedirs(output_dir, exist_ok=True) - # Save image locally with timestamp. timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") image_filename = f"sd_generated_{timestamp}.png" image_path = os.path.join(output_dir, image_filename) image.save(image_path) self.logger.info(f"Image saved to: {image_path}") - # Create response data - response_data = { + return { "prompt": prompt, "image_path": image_path, "timestamp": timestamp, @@ -95,14 +96,14 @@ async def generate_image(self, prompt: str) -> dict: }, "message": "Image generated and saved locally!", } - return response_data -# Test locally with: python -m workers.gpu.endpoint if __name__ == "__main__": import asyncio test_payload = {"message": "Testing GPU worker"} logger.info(f"Testing GPU worker with payload: {test_payload}") sd = SimpleSD() - asyncio.run(sd.generate_image("make an image of a cute labrador retriever surfing a wave")) + asyncio.run( + sd.generate_image("make an image of a cute labrador retriever surfing a wave") + ) diff --git a/05_data_workflows/01_network_volumes/main.py b/05_data_workflows/01_network_volumes/main.py deleted file mode 100644 index a00d80b..0000000 --- a/05_data_workflows/01_network_volumes/main.py +++ /dev/null @@ -1,64 +0,0 @@ -import logging -import os -from contextlib import asynccontextmanager - -from fastapi import FastAPI -from workers.cpu import cpu_router -from workers.gpu import gpu_router -from workers.gpu.endpoint import SimpleSD - -logger = logging.getLogger(__name__) - - -@asynccontextmanager -async def lifespan(app: FastAPI): - # Load the GPU model once and share it across requests. - logger.info("Loading SimpleSD model...") - app.state.sd_model = SimpleSD() - logger.info("Model loaded successfully") - - yield - - # Clean up resources on shutdown. - logger.info("Cleaning up resources...") - if hasattr(app.state, "sd_model"): - del app.state.sd_model - logger.info("Cleanup complete") - - -app = FastAPI( - lifespan=lifespan, - title="Flash Application", - description="Distributed GPU and CPU computing with Runpod Flash", - version="0.1.0", -) - -# Wire GPU and CPU routers into the app. -app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"]) -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"]) - - -@app.get("/") -def home(): - return { - "message": "Flash Application", - "docs": "/docs", - } - - -@app.get("/ping") -def ping(): - return {"status": "healthy"} - - -if __name__ == "__main__": - import uvicorn - - port = int(os.getenv("PORT", 8888)) - logger.info(f"Starting Flash server on port {port}") - logger.info( - "Try generating an image with a prompt by sending a POST request to http://localhost:8888/generate" - ) - logger.info("List generated images by querying /images") - - uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/05_data_workflows/01_network_volumes/requirements.txt b/05_data_workflows/01_network_volumes/requirements.txt deleted file mode 100644 index d252e70..0000000 --- a/05_data_workflows/01_network_volumes/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -runpod-flash \ No newline at end of file diff --git a/05_data_workflows/01_network_volumes/workers/__init__.py b/05_data_workflows/01_network_volumes/workers/__init__.py deleted file mode 100644 index ff2a525..0000000 --- a/05_data_workflows/01_network_volumes/workers/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from runpod_flash import NetworkVolume - -# Shared volume used by both GPU and CPU workers. -volume = NetworkVolume( - name="flash-05-volume", - size=50, # in GB -) diff --git a/05_data_workflows/01_network_volumes/workers/cpu/__init__.py b/05_data_workflows/01_network_volumes/workers/cpu/__init__.py deleted file mode 100644 index fd2e234..0000000 --- a/05_data_workflows/01_network_volumes/workers/cpu/__init__.py +++ /dev/null @@ -1,52 +0,0 @@ -from fastapi import APIRouter -from fastapi.responses import HTMLResponse - -from .endpoint import get_image_from_volume, list_images_in_volume - -cpu_router = APIRouter() - - -@cpu_router.get("/", response_class=HTMLResponse) -async def browse_images() -> HTMLResponse: - result = await list_images_in_volume() - images = result.get("images", []) - - items = "\n".join(f'
  • {image}
    • ' for image in images) - body = items or "
  • No images found yet.
    • " - - html = f""" - - - - - Network Volume Images - - - -

    Generated Images

    - - -""" - - return HTMLResponse(content=html) - - -@cpu_router.get("/image") -async def list_images(): - # Simple index of files produced by the GPU worker. - result = await list_images_in_volume() - return result - - -@cpu_router.get("/image/{file_id}") -async def get_image(file_id: str): - # Serve a single image file from the shared volume. - result = await get_image_from_volume(file_id) - return result diff --git a/05_data_workflows/01_network_volumes/workers/cpu/endpoint.py b/05_data_workflows/01_network_volumes/workers/cpu/endpoint.py deleted file mode 100644 index 46f6064..0000000 --- a/05_data_workflows/01_network_volumes/workers/cpu/endpoint.py +++ /dev/null @@ -1,54 +0,0 @@ -from fastapi.exceptions import HTTPException -from fastapi.responses import Response -from runpod_flash import CpuLiveServerless, remote - -from .. import volume - -cpu_config = CpuLiveServerless( - name="cpu_worker", - workersMin=0, - workersMax=2, - networkVolume=volume, -) - - -@remote(resource_config=cpu_config) -async def list_images_in_volume() -> dict: - import os - - """List generated images from the shared volume.""" - image_dir = "/runpod-volume/generated_images" - images = os.listdir(image_dir) - - return { - "status": "success", - "images": images, - } - - -@remote(resource_config=cpu_config, dependencies=["fastapi"]) -async def get_image_from_volume(file_id: str) -> "Response": - from pathlib import Path - - from fastapi.responses import Response - - image_path = Path(f"/runpod-volume/generated_images/{file_id}") - - if not image_path.is_file(): - raise HTTPException(status_code=404, detail="file not found") - - image_bytes = image_path.read_bytes() - return Response( - content=image_bytes, - media_type="image/png", - headers={"Content-Disposition": f'inline; filename="{image_path.name}"'}, - ) - - -# Test locally with: python -m workers.cpu.endpoint -if __name__ == "__main__": - import asyncio - - print("checking cpu worker by listing image files in /runpod-volume/generated_images/") - result = asyncio.run(list_images_in_volume()) - print(f"Result: {result}") diff --git a/05_data_workflows/01_network_volumes/workers/gpu/__init__.py b/05_data_workflows/01_network_volumes/workers/gpu/__init__.py deleted file mode 100644 index 79a3ee5..0000000 --- a/05_data_workflows/01_network_volumes/workers/gpu/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from fastapi import APIRouter, Request -from pydantic import BaseModel - -gpu_router = APIRouter() - - -class GenerateRequest(BaseModel): - prompt: str - - -@gpu_router.post("/generate") -async def generate(request: GenerateRequest, req: Request): - """Simple GPU worker endpoint.""" - # Use the shared model loaded in app startup. - sd_model = req.app.state.sd_model - result = await sd_model.generate_image(request.prompt) - return result diff --git a/CLAUDE.md b/CLAUDE.md index c8f25ce..a767730 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,560 +1,114 @@ # Flash Examples: AI Coding Assistant Guidelines -This document provides instructions for AI coding assistants (Claude Code, Cursor, GitHub Copilot, etc.) working on the flash-examples repository. +Instructions for AI coding assistants working on the flash-examples repository. ## Project Overview -The flash-examples repository contains production-ready examples demonstrating Flash framework capabilities. Examples are organized by category and automatically discovered by a unified FastAPI application through pattern matching. +Production-ready examples demonstrating Flash framework capabilities. Each example is a flat directory of standalone worker files auto-discovered by `flash run`. -**Key Architecture**: The root `main.py` scans category directories (`01_getting_started/`, `02_ml_inference/`, etc.) and dynamically loads all examples via APIRouter exports, creating a single unified app with auto-generated documentation. +## Architecture: Flat-File Pattern -**Important**: For full contribution guidelines, see [CONTRIBUTING.md](./CONTRIBUTING.md). +No FastAPI boilerplate, no routers, no `main.py`, no `mothership.py`. Each worker is a self-contained file with a `@remote` decorator. -## Critical: Use flash init for New Examples ⚠️ - -### The Rule -**Always use `flash init` to create new examples. Never copy-paste or duplicate existing example directories.** - -### Why -- `flash init` generates the correct, current project structure -- Copy-pasting perpetuates outdated patterns and structure issues -- Ensures consistency across all examples -- Provides clean boilerplate aligned with Flash's latest conventions - -### Workflow - -When creating a new example: - -```bash -# 1. Navigate to the appropriate category -cd 03_advanced_workers - -# 2. Use flash init to create clean project structure -flash init my_new_example -cd my_new_example - -# 3. Review existing examples in this category to understand patterns -# (don't copy code - understand the patterns) - -# 4. Implement your example with fresh code - -# 5. Run make consolidate-deps to update root dependencies -cd ../.. -make consolidate-deps +``` +example_name/ +├── README.md +├── gpu_worker.py # @remote decorated functions +├── cpu_worker.py # Optional CPU worker +└── pipeline.py # Optional cross-worker orchestration ``` -### Study Without Copying - -- Review existing examples to understand patterns and best practices -- Study how `@remote` decorator is configured -- Examine APIRouter setup and error handling -- Look at input validation patterns -- **But**: Generate your own implementation, don't duplicate code - -## Flash-Specific Patterns - -### Remote Worker Decorator +### Worker File Pattern ```python -from runpod_flash import remote, LiveServerless, GpuGroup +from runpod_flash import GpuGroup, LiveServerless, remote -# Configure resource requirements -# Naming convention: {category}_{example}_{worker_type} gpu_config = LiveServerless( - name="01_01_getting_started_gpu", + name="01_01_gpu_worker", gpus=[GpuGroup.ANY], workersMin=0, workersMax=3, - idleTimeout=300, + idleTimeout=5, ) @remote(resource_config=gpu_config) -async def process_image(input_data: dict) -> dict: - """Process an image with GPU support.""" - image_url = input_data.get("image_url") +async def my_function(input_data: dict) -> dict: + """All imports inside the function body.""" + import torch # implementation - return {"status": "success", "result": "..."} + return {"status": "success"} ``` -Key points: -- Imports are from `runpod_flash`, not `flash` -- Create a config object (`LiveServerless` or `CpuLiveServerless`) for GPU/CPU workers -- Pass config via `resource_config` parameter to `@remote` decorator -- Use `async/await` for all worker functions -- Return serializable data (dict, list, str, etc.) -- Include comprehensive docstrings - -### APIRouter Export Pattern - -**IMPORTANT**: Routers must follow the naming pattern `{worker_type}_router` (e.g., `gpu_router`, `cpu_router`). The unified app discovery system explicitly looks for this pattern. - -**Single-file workers** (e.g., `gpu_worker.py`): -```python -from fastapi import APIRouter -from pydantic import BaseModel +Key rules: +- Imports from `runpod_flash`, not `flash` +- Config object at module level (`LiveServerless`, `CpuLiveLoadBalancer`, `CpuLiveServerless`) +- All runtime imports inside the `@remote` function body +- Return serializable data (dict, list, str) +- Worker naming convention: `{category}_{example}_{worker_type}` (e.g., `01_01_gpu_worker`) -class MyRequest(BaseModel): - input_data: str +### Resource Types -class MyResponse(BaseModel): - status: str - result: str +| Type | Import | Use Case | +|------|--------|----------| +| `LiveServerless` | `from runpod_flash import LiveServerless, GpuGroup` | GPU workers | +| `CpuLiveLoadBalancer` | `from runpod_flash import CpuLiveLoadBalancer` | CPU workers, pipelines | +| `CpuLiveServerless` | `from runpod_flash import CpuLiveServerless` | CPU serverless | -# Router must be named gpu_router (for gpu_worker.py) -gpu_router = APIRouter() +### Cross-Worker Orchestration -@gpu_router.post("/endpoint") -async def my_endpoint(request: MyRequest) -> MyResponse: - """Handle endpoint request.""" - # implementation - return MyResponse(status="success", result="...") -``` +Pipeline files import functions from other workers and chain them: -**Directory-based workers** (e.g., `workers/gpu/__init__.py`): ```python -from fastapi import APIRouter -from .endpoint import my_endpoint -from pydantic import BaseModel - -class MyRequest(BaseModel): - input_data: str - -# Router must be named gpu_router (for workers/gpu/ directory) -gpu_router = APIRouter() - -@gpu_router.post("/endpoint") -async def handle_endpoint(request: MyRequest): - """Handle endpoint request.""" - return await my_endpoint(request) -``` - -The unified app auto-discovery looks for: -- **Single-file**: `{worker_type}_router` in `{worker_type}_worker.py` (e.g., `gpu_router` in `gpu_worker.py`) -- **Directory**: `{worker_type}_router` in `workers/{worker_type}/__init__.py` (e.g., `gpu_router` in `workers/gpu/__init__.py`) - -### Mothership Endpoint Configuration - -Every example includes a `mothership.py` file that configures the mothership endpoint - the load-balanced endpoint serving your FastAPI application routes. +from cpu_worker import preprocess_text +from gpu_worker import gpu_inference +from runpod_flash import CpuLiveLoadBalancer, remote -```python -from runpod_flash import CpuLiveLoadBalancer +pipeline_config = CpuLiveLoadBalancer(name="pipeline", workersMin=1) -# Mothership endpoint configuration -mothership = CpuLiveLoadBalancer( - name="01_01_hello_world-mothership", - workersMin=1, - workersMax=3, -) +@remote(resource_config=pipeline_config, method="POST", path="/classify") +async def classify(text: str) -> dict: + result = await preprocess_text({"text": text}) + return await gpu_inference(result) ``` -**To customize:** -- Change `workersMin` and `workersMax` for scaling behavior -- Use `LiveLoadBalancer` instead of `CpuLiveLoadBalancer` for GPU -- Update the `name` parameter with appropriate endpoint name - -**To disable mothership deployment:** -- Delete the `mothership.py` file, or -- Comment out the `mothership` variable - -See the template `mothership.py` in your example for examples of customization. +## Creating New Examples -### Input Validation with Pydantic +Always use `flash init`: -Always use Pydantic for request validation: - -```python -from pydantic import BaseModel, Field, EmailStr - -class ProcessRequest(BaseModel): - image_url: str = Field(..., description="URL of image to process") - quality: int = Field(default=90, ge=1, le=100) - email: EmailStr - -class ProcessResponse(BaseModel): - status: str - result: dict - processing_time: float -``` - -Benefits: -- Automatic request validation -- Type safety -- OpenAPI documentation auto-generation -- Clear error messages - -### Error Handling - -```python -from fastapi import HTTPException, status - -@router.post("/process") -async def process(request: ProcessRequest) -> ProcessResponse: - try: - result = await execute_processing(request) - return ProcessResponse(status="success", result=result) - except ValueError as e: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail=f"Invalid input: {str(e)}" - ) - except Exception as e: - logger.error(f"Processing failed", error=str(e)) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Processing failed. Check server logs." - ) -``` - -Guidelines: -- Catch specific exceptions, not bare `except` -- Provide actionable error messages -- Use appropriate HTTP status codes -- Log errors with context -- Never expose internal implementation details - -## Dependency Management - -### Adding Dependencies - -1. Add to your example's `pyproject.toml`: -```toml -[project] -dependencies = [ - "torch>=2.0.0", - "pillow>=10.0.0", -] -``` - -2. Pin specific versions for reproducibility -3. Document why each dependency is needed in your example's README - -### Consolidating Dependencies - -**Critical**: After adding dependencies, run: ```bash -cd /path/to/flash-examples -make consolidate-deps -``` - -This: -- Extracts all example dependencies to the root `pyproject.toml` -- Updates `uv.lock` with pinned versions -- Ensures all examples can be run in the unified app environment - -**Never** directly edit the root `pyproject.toml` - it's auto-generated by `make consolidate-deps`. - -## Code Standards - -### Type Hints (Mandatory) - -Every function must have type hints: - -```python -# ✅ GOOD -def calculate_features(image: np.ndarray, threshold: float) -> dict[str, float]: - """Extract image features.""" - pass - -# ❌ BAD -def calculate_features(image, threshold): - """Extract image features.""" - pass -``` - -### Early Returns / Guard Clauses - -```python -# ✅ GOOD - Handle edge cases first -async def process_image(url: str) -> dict: - if not url: - raise ValueError("URL is required") - - if not url.startswith(("http://", "https://")): - raise ValueError("Invalid URL scheme") - - # Main logic follows - image = await download_image(url) - return extract_features(image) - -# ❌ BAD - Nested conditions -async def process_image(url: str) -> dict: - if url: - if url.startswith(("http://", "https://")): - image = await download_image(url) - return extract_features(image) - else: - raise ValueError("Invalid URL scheme") - else: - raise ValueError("URL is required") -``` - -### Logging (Not Print Statements) - -```python -import logging - -logger = logging.getLogger(__name__) - -# ✅ GOOD -logger.info("Processing started", extra={"image_url": url, "user_id": user_id}) -logger.error("Download failed", exc_info=True, extra={"url": url}) - -# ❌ BAD -print(f"Processing {url}") -print("ERROR: Download failed") -``` - -### No Hardcoded Credentials - -```python -# ✅ GOOD - Use environment variables -import os -api_key = os.getenv("API_KEY") - -# ❌ BAD - Hardcoded -api_key = "sk-1234567890abcdef" +cd 01_getting_started +flash init my_new_example ``` -## Testing Requirements +Never copy-paste existing examples. -### Must Pass `flash run` +## Development Workflow -Your example must run successfully in the unified app: ```bash -flash run -# Visit http://localhost:8888/docs to test all endpoints -``` - -### Test in Unified Context - -When testing locally, ensure your routes work with the unified app's auto-discovery: -- Your routes are prefixed with your example directory name -- Example: `/03_advanced_workers/my_example/endpoint` -- Test via the Swagger UI at `/docs` - -### Async Testing with pytest-asyncio - -```python -import pytest - -@pytest.mark.asyncio -async def test_process_image(): - # Arrange - url = "https://example.com/image.jpg" - - # Act - result = await process_image(url) - - # Assert - assert result["status"] == "success" - assert "features" in result +flash run # Start local dev server (localhost:8888) +# Visit http://localhost:8888/docs for interactive API docs +python gpu_worker.py # Test a single worker directly ``` -### VS Code Debugging - -Examples include `.vscode/launch.json` for debugging endpoints directly in VS Code. - -## Documentation Standards - -### README.md Structure - -Create a comprehensive `README.md` with these sections: - -```markdown -# Example Name - -Brief description of what this example demonstrates. - -## Overview - -Longer explanation of the use case and key learnings. - -## What You'll Learn - -- Key concept 1 -- Key concept 2 -- Key concept 3 - -## Architecture - -Diagram or description of how this example works. - -## Quick Start - -### Prerequisites -- Python 3.10+ -- Flash SDK - -### Installation +## Quality Gates ```bash -cd path/to/this/example -flash run -``` - -### Test the Endpoint - -```bash -curl -X POST http://localhost:8888/endpoint \ - -H "Content-Type: application/json" \ - -d '{"input": "value"}' -``` - -## API Endpoints - -### POST /endpoint - -**Description**: What this endpoint does. - -**Request**: -```json -{ - "input": "string" -} -``` - -**Response**: -```json -{ - "status": "success", - "result": "..." -} +make quality-check # Required before every commit +make lint # Ruff linter +make format-check # Ruff format check ``` -**Error Handling**: -- 400 Bad Request: When input validation fails -- 500 Internal Server Error: When processing fails - -## Deployment - -Instructions for deploying to Runpod or other platforms. - -## Cost Estimates - -Approximate monthly costs for running this example in production. - -## Common Issues - -Troubleshooting section with solutions. - -## References - -- Flash documentation -- External libraries documentation - - -### Code Comments - -- Comment complex logic only -- Prefer clear code over clever code -- Document non-obvious design decisions -- Docstrings for all functions and classes - -## Project Structure - -Your example directory should follow one of these patterns: - -### Pattern 1: Single-file Workers - -Use for simple examples with a single worker type and minimal logic. - -``` -example_name/ -├── README.md -├── main.py # FastAPI app for local development -├── mothership.py # Mothership endpoint configuration -├── gpu_worker.py # Exports router (GPU worker) -├── cpu_worker.py # Exports router (CPU worker) [optional] -├── pyproject.toml # Dependencies and metadata -├── requirements.txt # Pin dependencies for reproducibility -├── .env.example # Environment variables template -├── .gitignore # Git ignore patterns -├── .flashignore # Flash build ignore patterns -└── .python-version # Python version (optional) -``` - -**Example**: `01_getting_started/01_hello_world/`, `01_getting_started/02_cpu_worker/` - -### Pattern 2: Directory-based Workers - -Use for complex examples with multiple worker types, shared utilities, or extensive logic. - -``` -example_name/ -├── README.md -├── main.py # FastAPI app for local development -├── mothership.py # Mothership endpoint configuration -├── pyproject.toml # Dependencies and metadata -├── requirements.txt # Pin dependencies for reproducibility -├── .env.example # Environment variables template -├── .gitignore # Git ignore patterns -├── .flashignore # Flash build ignore patterns -├── .python-version # Python version (optional) -└── workers/ - ├── __init__.py - ├── gpu/ - │ ├── __init__.py # Exports gpu_router - │ └── endpoint.py # @remote functions - └── cpu/ - ├── __init__.py # Exports cpu_router - └── endpoint.py # @remote functions -``` - -**Example**: `01_getting_started/03_mixed_workers/`, `03_advanced_workers/05_load_balancer/` - -### Files That Should NOT Be Committed - -These files are auto-generated and should be ignored: - -- `flash_manifest.json` - Generated by flash build -- `uv.lock` - Generated by uv sync (included in root uv.lock) - -These are already added to `.gitignore` patterns. - -## Pre-Commit Checklist - -Before submitting an example: - -- [ ] Used `flash init` to create the structure -- [ ] `mothership.py` is present (or intentionally deleted if not deploying mothership) -- [ ] `.gitignore` includes Flash auto-generated files (flash_manifest.json, uv.lock) -- [ ] All functions have type hints -- [ ] All endpoints have input validation (Pydantic models) -- [ ] Error handling is implemented -- [ ] No hardcoded credentials or secrets -- [ ] Using logging, not print statements -- [ ] **@remote functions only access local scope** - - All imports are inside the function - - All helper functions defined inside the function - - No references to module-level variables or functions -- [ ] `flash run` executes without errors -- [ ] All endpoints tested via `/docs` Swagger UI -- [ ] README.md is comprehensive and accurate -- [ ] Dependencies added to pyproject.toml -- [ ] `make consolidate-deps` has been run -- [ ] No auto-generated files are tracked by git (flash_manifest.json, uv.lock) -- [ ] Code follows the standards in this guide - -## Common Mistakes to Avoid +## Code Standards -1. **Accessing external scope in @remote functions (CRITICAL)**: Only local variables, parameters, and internal imports work - - ❌ Don't call functions or access variables defined outside the function - - ❌ Don't use module-level imports - - ✅ Import and define everything inside the function -2. **Copying existing examples**: Always use `flash init` -3. **Missing type hints**: Every parameter and return value -4. **Hardcoded values**: Use environment variables -5. **No error handling**: All external operations need try/except -6. **Print instead of logging**: Use the logging module -7. **Bare except clauses**: Always catch specific exceptions -8. **No input validation**: Use Pydantic models -9. **Not running consolidate-deps**: Critical for shared environment -10. **Routes not exporting router**: Unified app won't discover them -11. **Mutable default arguments**: Use None and initialize in function +- Type hints on all function signatures +- Early returns / guard clauses over nested conditions +- No hardcoded credentials (use `os.getenv()`) +- No `print()` in production code (logging module or skip for examples) +- Catch specific exceptions, never bare `except:` -## Getting Help +## Common Mistakes -1. Check existing examples in the same category -2. Review CONTRIBUTING.md for additional guidelines -3. See Flash documentation at https://docs.runpod.io -4. Check repository issues and discussions +1. **Accessing external scope in @remote functions** -- only local variables, parameters, and internal imports work +2. **Module-level imports of heavy libraries** -- import torch, numpy, etc. inside the function body +3. **Missing `if __name__ == "__main__"` test block** -- each worker should be independently testable +4. **Mutable default arguments** -- use `None` and initialize in function body diff --git a/CLI-REFERENCE.md b/CLI-REFERENCE.md index 6116cd9..9990e0b 100644 --- a/CLI-REFERENCE.md +++ b/CLI-REFERENCE.md @@ -101,8 +101,6 @@ flash init my-api --force ### What It Creates -- `main.py` - FastAPI application entry point -- `mothership.py` - Mothership endpoint configuration - `gpu_worker.py` - GPU worker template with `@remote` decorator - `pyproject.toml` - Project dependencies - `.env.example` - Environment variable template @@ -177,8 +175,8 @@ flash run ### What It Does -1. Loads `main.py` FastAPI application -2. Discovers all `@remote` decorated functions +1. Discovers all `.py` files (excluding `.venv/`, `.flash/`, `.runpod/`, `__init__.py`) and finds `@remote` decorated functions via AST parsing +2. Generates a FastAPI application with routes for each discovered function 3. Starts uvicorn development server with hot reload 4. Provides interactive API documentation at `/docs` 5. Optionally provisions remote resources if `--auto-provision` is enabled diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 60612b3..b75869c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -44,9 +44,9 @@ All examples must meet these standards: - [ ] All endpoints return correct responses - [ ] Error handling is implemented - [ ] Environment variables are documented -- [ ] Dependencies are pinned in pyproject.toml -- [ ] Dependencies consolidated to root with `make consolidate-deps` -- [ ] Example loads in unified app (`flash run` from root) +- [ ] Dependencies are declared in pyproject.toml +- [ ] Runtime deps declared in `@remote(dependencies=[...])` +- [ ] Example discovered by `flash run` from project root ### 2. Code Quality - [ ] Clear, readable code @@ -130,26 +130,17 @@ flash run # Test all endpoints ``` -### 5. Consolidate Dependencies to Root +### 5. Verify Discovery -**Important**: The unified app loads all examples dynamically, so all example dependencies must be available in the root environment. - -After adding your example with its dependencies, consolidate them to the root: +`flash run` auto-discovers all `.py` files containing `@remote` functions. Verify your example loads: ```bash # From the repository root -make consolidate-deps # Consolidates your example's deps to root pyproject.toml -uv sync # Installs the new dependencies +flash run +# Check http://localhost:8888/docs for your new endpoints ``` -The consolidation script: -- Automatically scans your example's `pyproject.toml` -- Merges all dependencies into the root configuration -- Lets pip/uv handle version resolution - -If pip/uv reports version conflicts, resolve them manually in the root `pyproject.toml`. - -**Verification**: Run `flash run` from the repository root to ensure your example loads without import errors. +Runtime dependencies (torch, transformers, etc.) are declared in `@remote(dependencies=[...])` and installed on the remote worker, not locally. ### 6. Create Pull Request @@ -170,69 +161,28 @@ Create a PR with: ### Standard Structure +Each example is a flat directory with self-contained worker files (named `*_worker.py` by convention): + ``` your_example/ ├── README.md # Required: comprehensive documentation -├── main.py # Required: FastAPI application -├── workers/ # Required: remote worker functions -│ ├── gpu/ -│ │ ├── __init__.py -│ │ └── endpoint.py -│ └── cpu/ -│ ├── __init__.py -│ └── endpoint.py -├── requirements.txt # Required: pinned dependencies -├── pyproject.toml # Required: project metadata -├── .env.example # Required: environment template -├── .gitignore # Required: ignore .env, .venv, etc. -├── .flashignore # Optional: files to exclude from deployment -├── tests/ # Recommended: test files -│ ├── test_endpoints.py +├── gpu_worker.py # Required: GPU worker with @remote decorator +├── cpu_worker.py # Optional: CPU worker with @remote decorator +├── pyproject.toml # Required: project metadata and dependencies +├── .flashignore # Optional: files to exclude from deployment +├── tests/ # Recommended: test files +│ ├── test_workers.py │ └── conftest.py -└── assets/ # Optional: images, diagrams, etc. +└── assets/ # Optional: images, diagrams, etc. └── architecture.png ``` -### Minimal main.py - -```python -from fastapi import FastAPI -from workers.gpu import gpu_router -from workers.cpu import cpu_router - -app = FastAPI( - title="Your Example", - description="Brief description", - version="0.1.0", -) - -app.include_router(gpu_router, prefix="/gpu", tags=["GPU Workers"]) -app.include_router(cpu_router, prefix="/cpu", tags=["CPU Workers"]) - -@app.get("/") -def home(): - return { - "message": "Your Example API", - "docs": "/docs", - } - -@app.get("/health") -def health(): - return {"status": "healthy"} - -if __name__ == "__main__": - import uvicorn - import os - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - uvicorn.run(app, host=host, port=port) -``` +`flash run` discovers all `.py` files with `@remote` functions automatically -- no `main.py`, no `workers/` directories, no router wiring. -### Minimal Worker (endpoint.py) +### Minimal Worker (`gpu_worker.py`) ```python -from runpod_flash import remote, LiveServerless, GpuGroup +from runpod_flash import remote, LiveServerless config = LiveServerless(name="your_worker") @@ -377,8 +327,8 @@ Two debug configurations are available: - **Python Debugger: Current File** - Debug any Python file - **Flash Worker: Debug Endpoint** - Debug worker endpoint files with async support -To debug an endpoint: -1. Open any `endpoint.py` file (e.g., `01_getting_started/01_hello_world/workers/gpu/endpoint.py`) +To debug a worker: +1. Open any `*_worker.py` file (e.g., `01_getting_started/01_hello_world/hello_worker.py`) 2. Set breakpoints in your worker functions 3. Press F5 or select "Debug: Start Debugging" 4. Choose the appropriate debug configuration @@ -391,9 +341,9 @@ The `.env` file is automatically loaded, so your `RUNPOD_API_KEY` is available d Add tests for your worker functions: ```python -# tests/test_endpoints.py +# tests/test_workers.py import pytest -from workers.gpu.endpoint import your_function +from your_example.gpu_worker import your_function @pytest.mark.asyncio async def test_your_function(): @@ -457,67 +407,51 @@ RUNPOD_API_KEY = "hardcoded_key" # Never do this! ### Error Handling ```python -# Good -from fastapi import HTTPException +# Good - handle errors within @remote functions +from runpod_flash import remote, LiveServerless + +config = LiveServerless(name="processor") -@router.post("/process") -async def process(data: dict): +@remote(resource_config=config) +async def process(data: dict) -> dict: try: - result = await worker_function(data) - return result + result = do_work(data) + return {"status": "success", "result": result} except ValueError as e: - raise HTTPException(status_code=400, detail=str(e)) - except Exception as e: - logger.error(f"Unexpected error: {e}") - raise HTTPException(status_code=500, detail="Internal server error") + return {"status": "error", "detail": str(e)} # Bad -@router.post("/process") -async def process(data: dict): - result = await worker_function(data) # No error handling +@remote(resource_config=config) +async def process(data: dict) -> dict: + result = do_work(data) # No error handling return result ``` ## Dependencies -### Requirements.txt - -Pin all dependencies: - -```txt -runpod_flash==1.2.3 -fastapi==0.104.1 -uvicorn[standard]==0.24.0 -torch==2.1.0 -transformers==4.35.0 -``` - -Generate with: -```bash -pip freeze > requirements.txt -``` - ### pyproject.toml -Include project metadata: +Each example declares only its local development dependencies in `pyproject.toml`. Runtime deps needed on the GPU (torch, transformers, etc.) go in `@remote(dependencies=[...])`: ```toml [project] name = "your-example" version = "0.1.0" description = "Brief description" -requires-python = ">=3.12" +requires-python = ">=3.10" dependencies = [ "runpod-flash", ] ``` +The root `requirements.txt` is a generated lockfile -- do not add per-example `requirements.txt` files. + ## Security Best Practices 1. **Never commit secrets** - Use `.env` files (gitignored) - - Provide `.env.example` templates - - Document all required environment variables + - The root `.env.example` documents required variables + - Do not add per-example `.env.example` files 2. **Validate inputs** - Use Pydantic models diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md index 934aecd..4c14d40 100644 --- a/DEVELOPMENT.md +++ b/DEVELOPMENT.md @@ -451,33 +451,32 @@ Then review and test changes before committing. ### Creating New Examples -**1. Create directory structure:** +**1. Create directory and worker files:** ```bash -mkdir -p 01_getting_started/05_new_example/workers/{gpu,cpu} +mkdir 01_getting_started/05_new_example cd 01_getting_started/05_new_example ``` **2. Create files:** ```bash -touch README.md main.py .env.example -touch workers/gpu/__init__.py workers/gpu/endpoint.py -touch workers/cpu/__init__.py workers/cpu/endpoint.py +touch README.md gpu_worker.py pyproject.toml ``` -**3. Generate dependencies:** +Each worker file (named `*_worker.py` by convention) is self-contained with `@remote` decorated functions. `flash run` discovers all `.py` files with `@remote` functions automatically -- no `main.py`, no `workers/` directories needed. + +**3. Declare dependencies:** + +Add a `pyproject.toml` with `runpod-flash` as the only local dependency. Runtime deps (torch, etc.) go in `@remote(dependencies=[...])`. + +**4. Verify discovery:** ```bash -# Create pyproject.toml with dependencies -# Then generate requirements.txt cd ../../ # Back to root -make requirements.txt -cp requirements.txt 01_getting_started/05_new_example/ +flash run # Discovers all .py files with @remote functions ``` -**4. Follow example structure** (see README.md) - ### Cleaning Up **Remove build artifacts:** diff --git a/Makefile b/Makefile index 57cc80d..a702ca6 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: help venv-info setup dev verify-setup consolidate-deps check-deps sync-deps update-deps clean clean-venv lint lint-fix format format-check typecheck quality-check quality-check-strict ci-quality-github +.PHONY: help venv-info setup dev verify-setup clean clean-venv lint lint-fix format format-check quality-check ci-quality-github # ============================================================================ # Environment Manager Auto-Detection @@ -234,61 +234,6 @@ endif dev: setup # Alias for 'make setup' (backward compatibility) -# ============================================================================ -# Dependency File Generation -# ============================================================================ - -requirements.txt: # Generate requirements.txt from pyproject.toml - @echo "Generating requirements.txt from pyproject.toml..." -ifeq ($(PKG_MANAGER),uv) - uv pip compile pyproject.toml -o requirements.txt -else ifdef HAS_UV - uv pip compile pyproject.toml -o requirements.txt -else - @echo "runpod-flash" > requirements.txt - @echo "✓ Basic requirements.txt created (install 'uv' for dependency resolution)" -endif - -environment.yml: # Generate conda environment.yml from pyproject.toml - @echo "Generating environment.yml for conda..." - @echo "name: flash-examples" > environment.yml - @echo "channels:" >> environment.yml - @echo " - conda-forge" >> environment.yml - @echo " - defaults" >> environment.yml - @echo "dependencies:" >> environment.yml - @echo " - python>=3.11" >> environment.yml - @echo " - pip" >> environment.yml - @echo " - pip:" >> environment.yml - @echo " - runpod-flash" >> environment.yml - @echo "✓ environment.yml created" - -consolidate-deps: # Consolidate example dependencies to root pyproject.toml - @echo "Consolidating example dependencies..." - $(PYTHON) scripts/sync_example_deps.py - -check-deps: # Verify example dependencies are consolidated (CI mode) - @echo "Verifying dependencies are consolidated..." - $(PYTHON) scripts/sync_example_deps.py --check - -sync-deps: requirements.txt environment.yml # Generate all dependency files - @echo "✓ All dependency files synced" - -update-deps: # Update dependencies to latest versions - @echo "Updating dependencies with $(PKG_MANAGER)..." -ifeq ($(PKG_MANAGER),uv) - uv sync --upgrade - uv pip compile --upgrade pyproject.toml -o requirements.txt -else ifeq ($(PKG_MANAGER),poetry) - poetry update -else ifeq ($(PKG_MANAGER),pipenv) - pipenv update -else ifeq ($(PKG_MANAGER),conda) - conda run -p ./.venv pip install --upgrade runpod-flash -else ifeq ($(PKG_MANAGER),pip) - .venv/bin/pip install --upgrade runpod-flash -endif - @echo "✓ Dependencies updated" - # ============================================================================ # Cleanup # ============================================================================ @@ -309,7 +254,7 @@ clean-venv: # Remove virtual environment directory fi # ============================================================================ -# Code Quality - Linting +# Code Quality # ============================================================================ lint: # Check code with ruff @@ -320,10 +265,6 @@ lint-fix: # Fix code issues with ruff @echo "Fixing code issues with ruff..." $(PYTHON_RUN) ruff check . --fix -# ============================================================================ -# Code Quality - Formatting -# ============================================================================ - format: # Format code with ruff @echo "Formatting code with ruff..." $(PYTHON_RUN) ruff format . @@ -332,28 +273,9 @@ format-check: # Check code formatting @echo "Checking code formatting..." $(PYTHON_RUN) ruff format --check . -# ============================================================================ -# Code Quality - Type Checking -# ============================================================================ - -typecheck: # Check types with mypy - @echo "Running mypy type checker..." - @$(PYTHON_RUN) mypy . || { [ $$? -eq 2 ] && echo "No Python files found for type checking"; } - -# ============================================================================ -# Quality Gates (used in CI) -# ============================================================================ - quality-check: format-check lint # Essential quality gate for CI @echo "✓ Quality checks passed" -quality-check-strict: format-check lint typecheck check-deps # Strict quality gate with type checking - @echo "✓ Strict quality checks passed" - -# ============================================================================ -# GitHub Actions Specific -# ============================================================================ - ci-quality-github: # Quality checks with GitHub Actions formatting @echo "::group::Code formatting check" $(PYTHON_RUN) ruff format --check . diff --git a/main.py b/main.py deleted file mode 100644 index 50d5fc3..0000000 --- a/main.py +++ /dev/null @@ -1,761 +0,0 @@ -""" -Unified Flash Examples Application - -This file automatically discovers and consolidates all Flash examples into one FastAPI application. -Run `flash run` from the root directory to access all examples from a single server. - -The discovery system automatically finds: -1. Single-file worker patterns (e.g., gpu_worker.py, cpu_worker.py) -2. Directory-based worker patterns (e.g., workers/gpu/__init__.py) -""" - -import importlib.util -import logging -import os -import sys -from pathlib import Path -from typing import Any -from urllib.parse import quote - -from fastapi import APIRouter, FastAPI -from fastapi.responses import HTMLResponse - -logger = logging.getLogger(__name__) - -BASE_DIR = Path(__file__).parent -EXAMPLES_DIRS = [ - BASE_DIR / "01_getting_started", - BASE_DIR / "02_ml_inference", - BASE_DIR / "03_advanced_workers", - BASE_DIR / "04_scaling_performance", - BASE_DIR / "05_data_workflows", - BASE_DIR / "06_real_world", -] - -# Route discovery constants -SKIP_HEALTH_CHECK_PATHS = ("/", "/health") -SKIP_OPENAPI_PATHS = ("/openapi.json", "/docs", "/docs/oauth2-redirect", "/redoc") -SKIP_WORKER_TYPES = ("gpu", "cpu", "workers") - -app = FastAPI( - title="Runpod Flash Examples - Unified Demo", - description="All Flash examples automatically discovered and unified in one FastAPI application", - version="1.0.0", -) - - -def load_module_from_path(module_name: str, file_path: Path) -> Any: - """Dynamically load a Python module from a file path.""" - spec = importlib.util.spec_from_file_location(module_name, file_path) - if spec is None or spec.loader is None: - return None - - module = importlib.util.module_from_spec(spec) - sys.modules[module_name] = module - spec.loader.exec_module(module) - return module - - -def discover_single_file_routers( - example_path: Path, example_name: str, example_tag: str = "" -) -> list[dict[str, Any]]: - """ - Discover routers from single-file worker patterns. - - Looks for files like gpu_worker.py, cpu_worker.py that export APIRouters. - """ - routers: list[dict[str, Any]] = [] - worker_files = list(example_path.glob("*_worker.py")) - - for worker_file in worker_files: - worker_type = worker_file.stem.replace("_worker", "") # e.g., 'gpu' or 'cpu' - module_name = f"{example_name}_{worker_type}_worker" - - try: - module = load_module_from_path(module_name, worker_file) - if module is None: - continue - - # Look for router (common naming: gpu_router, cpu_router, etc.) - router_name = f"{worker_type}_router" - if hasattr(module, router_name): - router = getattr(module, router_name) - if isinstance(router, APIRouter): - # Extract route information for deep linking - routes = [] - for route in router.routes: - if ( - hasattr(route, "endpoint") - and hasattr(route, "path") - and hasattr(route, "name") - ): - routes.append( - { - "path": str(route.path), - "name": str(route.name), - } - ) - - routers.append( - { - "router": router, - "prefix": f"/{example_name}/{worker_type}", - "tags": [example_tag], - "worker_type": worker_type, - "routes": routes, - } - ) - logger.info(f"Loaded {example_name}/{worker_type} from {worker_file.name}") - except Exception as e: - logger.warning(f"Could not load {worker_file}: {e}") - - return routers - - -def discover_directory_routers( - example_path: Path, example_name: str, example_tag: str = "" -) -> list[dict[str, Any]]: - """ - Discover routers from directory-based worker patterns. - - Looks for workers/gpu/__init__.py, workers/cpu/__init__.py that export APIRouters. - """ - routers: list[dict[str, Any]] = [] - workers_dir = example_path / "workers" - - if not workers_dir.exists() or not workers_dir.is_dir(): - return routers - - # Add workers directory to path for imports - workers_dir_str = str(workers_dir.parent) - if workers_dir_str not in sys.path: - sys.path.insert(0, workers_dir_str) - - # Look for worker type directories (gpu, cpu, etc.) - for worker_dir in workers_dir.iterdir(): - if not worker_dir.is_dir() or worker_dir.name.startswith("_"): - continue - - init_file = worker_dir / "__init__.py" - if not init_file.exists(): - continue - - worker_type = worker_dir.name - module_name = f"{example_name}_workers_{worker_type}" - - try: - module = load_module_from_path(module_name, init_file) - if module is None: - continue - - # Look for router (common naming: gpu_router, cpu_router, etc.) - router_name = f"{worker_type}_router" - if hasattr(module, router_name): - router = getattr(module, router_name) - if isinstance(router, APIRouter): - # Extract route information for deep linking - routes = [] - for route in router.routes: - if ( - hasattr(route, "endpoint") - and hasattr(route, "path") - and hasattr(route, "name") - ): - routes.append( - { - "path": str(route.path), - "name": str(route.name), - } - ) - - routers.append( - { - "router": router, - "prefix": f"/{example_name}/{worker_type}", - "tags": [example_tag], - "worker_type": worker_type, - "routes": routes, - } - ) - logger.info(f"Loaded {example_name}/{worker_type} from workers/{worker_type}") - except Exception as e: - logger.warning(f"Could not load {worker_dir}: {e}") - - return routers - - -def discover_main_app_routes( - example_path: Path, example_name: str, example_tag: str = "" -) -> list[dict[str, Any]]: - """ - Discover context routes from main.py FastAPI app. - - Extracts only direct routes from the app's router (not included routers). - Skips health check, info endpoints, and auto-generated OpenAPI routes. - Returns routes as a single router grouped with the example (no separate prefix). - """ - routers: list[dict[str, Any]] = [] - main_file = example_path / "main.py" - - if not main_file.exists(): - return routers - - module_name = f"{example_name}_main_context" - - try: - module = load_module_from_path(module_name, main_file) - if module is None: - return routers - - # Look for FastAPI app instance - if not hasattr(module, "app"): - return routers - - main_app = module.app - if not hasattr(main_app, "routes"): - return routers - - from fastapi.routing import Mount - - # We want to extract routes that were directly added to the app (via @app.post, etc.) - # not routes from included routers. We'll check the app's router directly. - routes_list = [] - context_router = APIRouter() - - # Look at direct routes from the main app's router - if hasattr(main_app, "router") and hasattr(main_app.router, "routes"): - for route in main_app.router.routes: - # Skip Mount routes (these are included routers like /gpu, /cpu) - if isinstance(route, Mount): - continue - - # Skip health check and info endpoints - if hasattr(route, "path") and route.path in SKIP_HEALTH_CHECK_PATHS: - continue - - # Skip auto-generated OpenAPI documentation routes - if hasattr(route, "path") and route.path in SKIP_OPENAPI_PATHS: - continue - - # Skip routes from included routers (routes with /gpu/, /cpu/, etc.) - if hasattr(route, "path"): - path = str(route.path) - # Skip routes that start with common worker types or look like they're from included routers - if any(f"/{wt}/" in path for wt in SKIP_WORKER_TYPES): - continue - - # Include everything else - if hasattr(route, "endpoint") and hasattr(route, "path") and hasattr(route, "name"): - routes_list.append( - { - "path": str(route.path), - "name": str(route.name), - } - ) - context_router.routes.append(route) - - if context_router.routes: - routers.append( - { - "router": context_router, - "prefix": f"/{example_name}", - "tags": [example_tag], - "worker_type": "api", - "routes": routes_list, - } - ) - logger.info( - f"Loaded {example_name} context routes from main.py ({len(routes_list)} routes)" - ) - - except Exception as e: - logger.debug(f"Could not load context routes from {main_file}: {e}") - - return routers - - -def discover_example_routers( - example_path: Path, category_name: str = "", example_name: str = "" -) -> list[dict[str, Any]]: - """ - Discover all routers from an example directory. - - Tries single-file, directory-based, and main.py app patterns. - Groups all routes under a single example tag: "Category > Example" - Includes context routes from main.py (like pipelines) grouped with the example. - """ - if not example_name: - example_name = example_path.name - - # Build example tag: "01 Getting Started > 01 Hello World" - example_tag = ( - f"{category_name} > {example_name.replace('_', ' ').title()}" - if category_name - else example_name.replace("_", " ").title() - ) - - routers: list[dict[str, Any]] = [] - - # Try single-file pattern - routers.extend(discover_single_file_routers(example_path, example_name, example_tag)) - - # Try directory-based pattern - routers.extend(discover_directory_routers(example_path, example_name, example_tag)) - - # Extract context routes (main.py app routes like pipelines, classifiers) - # These are grouped with the example, not given special tagging - routers.extend(discover_main_app_routes(example_path, example_name, example_tag)) - - return routers - - -def register_all_examples() -> dict[str, dict[str, Any]]: - """ - Discover and register all examples. - - Returns a dictionary of category -> examples metadata for the home endpoint. - """ - examples_by_category: dict[str, dict[str, Any]] = {} - - for examples_dir in EXAMPLES_DIRS: - if not examples_dir.exists() or not examples_dir.is_dir(): - continue - - category_name = examples_dir.name # e.g., "01_getting_started" - category_display = category_name.replace("_", " ").title() # "01 Getting Started" - - if category_name not in examples_by_category: - examples_by_category[category_name] = { - "display_name": category_display, - "examples": {}, - } - - # Iterate through example directories - for example_path in sorted(examples_dir.iterdir()): - if not example_path.is_dir() or example_path.name.startswith("."): - continue - - example_name = example_path.name - routers = discover_example_routers(example_path, category_display, example_name) - - if routers: - # Register routers with FastAPI - for router_info in routers: - app.include_router( - router_info["router"], - prefix=router_info["prefix"], - tags=router_info["tags"], - ) - - # Build metadata - examples_by_category[category_name]["examples"][example_name] = { - "description": f"Example: {example_name.replace('_', ' ').title()}", - "category_display": category_display, - "endpoints": { - router_info["worker_type"]: { - "prefix": router_info["prefix"], - "routes": router_info.get("routes", []), - "tag": router_info["tags"][0] if router_info["tags"] else "", - } - for router_info in routers - }, - } - - return examples_by_category - - -# Discover and register all examples -examples_metadata = register_all_examples() - - -def extract_operation_ids_from_app() -> dict[str, dict[str, list[str]]]: - """ - Extract actual operation IDs from the FastAPI app's routes. - - Returns a mapping of example_name -> worker_type -> [operation_ids] - """ - operation_ids_map: dict[str, dict[str, list[str]]] = {} - - for route in app.routes: - if hasattr(route, "tags") and hasattr(route, "unique_id"): - tags = route.tags or [] - operation_id = str(route.unique_id) - - # Parse tag to get example_name and worker_type - # Tag format: "01 Hello World - GPU" - for tag in tags: - if " - " in tag: - parts = tag.split(" - ") - if len(parts) == 2: - display_name = parts[0].strip() - worker_type = parts[1].strip().lower() - - # Convert display name back to example_name - # "01 Hello World" -> "01_hello_world" - example_name = display_name.lower().replace(" ", "_") - - if example_name not in operation_ids_map: - operation_ids_map[example_name] = {} - if worker_type not in operation_ids_map[example_name]: - operation_ids_map[example_name][worker_type] = [] - - operation_ids_map[example_name][worker_type].append(operation_id) - - return operation_ids_map - - -@app.get("/", response_class=HTMLResponse) -def home(): - """Branded home page with links to all automatically discovered examples.""" - - # Extract actual operation IDs from the app - operation_ids_map = extract_operation_ids_from_app() - - # Build examples list HTML grouped by category - categories_html = "" - total_examples = 0 - total_endpoints = 0 - - for category_data in examples_metadata.values(): - category_display = category_data["display_name"] - examples = category_data["examples"] - - if not examples: - continue - - total_examples += len(examples) - - # Build example cards for this category - examples_cards = "" - for example_name, metadata in examples.items(): - display_name = example_name.replace("_", " ").title() - endpoints_html = "" - - total_endpoints += len(metadata["endpoints"]) - - for worker_type, endpoint_info in metadata["endpoints"].items(): - # Use the actual tag from the router registration - tag_name = endpoint_info.get("tag", "") - if not tag_name: - # Fallback to old format if tag is not available - tag_name = f"{display_name} - {worker_type.upper()}" - - encoded_tag = quote(tag_name) - - # Get the actual operation ID from the app - operation_ids = operation_ids_map.get(example_name, {}).get(worker_type.lower(), []) - if operation_ids: - # Use the first operation ID for this worker type - operation_id = operation_ids[0] - doc_link = f"/docs#/{encoded_tag}/{operation_id}" - else: - # Fallback to tag-only link - doc_link = f"/docs#/{encoded_tag}" - - endpoints_html += f'{worker_type.upper()}' - - examples_cards += f""" -
    -

    {display_name}

    -
    {endpoints_html}
    -
    - """ - - # Add category section - categories_html += f""" -
    -

    {category_display}

    -
    - {examples_cards} -
    -
    - """ - - return f""" - - - - - - Runpod Flash Examples - - - - - - -
    -
    - -

    Flash Examples

    -

    - Explore production-ready examples for Runpod Flash. All examples automatically discovered and unified in one FastAPI application. -

    - -
    -
    -
    {total_examples}
    -
    Examples
    -
    -
    -
    {total_endpoints}
    -
    Endpoints
    -
    -
    - -
    - Interactive API Docs - Health Check -
    -
    - -
    - {categories_html} -
    - - -
    - - - """ - - -@app.get("/health", tags=["Info"]) -def health(): - """Health check endpoint.""" - examples_loaded = {} - total_examples = 0 - - for category_data in examples_metadata.values(): - for example_name in category_data["examples"]: - examples_loaded[example_name] = True - total_examples += 1 - - return { - "status": "healthy", - "examples_loaded": examples_loaded, - "total_examples": total_examples, - } - - -if __name__ == "__main__": - import uvicorn - - host = os.getenv("FLASH_HOST", "localhost") - port = int(os.getenv("FLASH_PORT", 8888)) - logger.info(f"Starting unified Flash examples server on {host}:{port}") - logger.info(f"Discovered {len(examples_metadata)} examples") - - uvicorn.run(app, host=host, port=port) diff --git a/pyproject.toml b/pyproject.toml index 7ab7030..d0e6fa7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,120 +5,17 @@ description = "A collection of example applications showcasing Runpod Flash - a readme = "README.md" requires-python = ">=3.10" dependencies = [ - "fastapi>=0.104.0", - "numpy>=2.0.2", - "pillow>=10.0.0", - "python-multipart>=0.0.6", "runpod-flash", - "structlog>=23.0.0", - "uvicorn>=0.24.0", ] [dependency-groups] dev = [ "ruff>=0.8.0", - "mypy>=1.13.0", - "pytest>=8.3.0", - "pytest-asyncio>=0.25.0", - "pytest-cov>=6.0.0", - "tomli>=2.0.0", - "tomli-w>=1.0.0", - "packaging>=21.0.0", ] [build-system] -requires = [ - "setuptools>=61.0", -] +requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" [tool.setuptools] py-modules = [] - -[tool.ruff] -target-version = "py310" -line-length = 100 -exclude = [ - ".git", - ".venv", - "__pycache__", - "*.egg-info", - ".ruff_cache", - "dist", - "build", -] - -[tool.ruff.lint] -select = [ - "E", - "W", - "F", - "I", - "N", - "UP", - "B", - "C4", - "SIM", - "RUF", -] -ignore = [ - "E501", - "N999", -] - -[tool.ruff.lint.isort] -known-first-party = [ - "runpod-flash", -] - -[tool.ruff.format] -quote-style = "double" -indent-style = "space" -skip-magic-trailing-comma = false -line-ending = "auto" - -[tool.mypy] -python_version = "3.10" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = false -ignore_missing_imports = true -strict_optional = true -warn_redundant_casts = true -warn_unused_ignores = true -check_untyped_defs = true - -[tool.pytest.ini_options] -minversion = "8.0" -testpaths = [ - "tests", -] -pythonpath = [ - ".", -] -asyncio_mode = "auto" -addopts = [ - "--strict-markers", - "--strict-config", - "-ra", -] - -[tool.coverage.run] -source = [ - ".", -] -omit = [ - ".venv/*", - "tests/*", - "*/site-packages/*", -] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", - "if TYPE_CHECKING:", -] diff --git a/requirements.txt b/requirements.txt index 494ba78..a73ed1a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,271 +1 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements.txt -aiodns==4.0.0 - # via aiohttp -aiohappyeyeballs==2.6.1 - # via aiohttp -aiohttp==3.13.3 - # via - # aiohttp-retry - # runpod -aiohttp-retry==2.9.1 - # via runpod -aiosignal==1.4.0 - # via aiohttp -annotated-doc==0.0.4 - # via fastapi -annotated-types==0.7.0 - # via pydantic -anyio==4.12.1 - # via - # httpx - # starlette - # watchfiles -attrs==25.4.0 - # via aiohttp -backoff==2.2.1 - # via runpod -backports-zstd==1.3.0 - # via aiohttp -bcrypt==5.0.0 - # via paramiko -boto3==1.42.41 - # via runpod -botocore==1.42.41 - # via - # boto3 - # s3transfer -brotli==1.2.0 - # via aiohttp -certifi==2026.1.4 - # via - # httpcore - # httpx - # requests - # sentry-sdk -cffi==2.0.0 - # via - # cryptography - # pycares - # pynacl -charset-normalizer==3.4.4 - # via requests -click==8.3.1 - # via - # rich-toolkit - # runpod - # typer - # uvicorn -cloudpickle==3.1.2 - # via runpod-flash -colorama==0.4.6 - # via runpod -cryptography==45.0.7 - # via - # paramiko - # runpod -dnspython==2.8.0 - # via email-validator -email-validator==2.3.0 - # via - # fastapi - # pydantic -fastapi==0.128.0 - # via runpod -fastapi-cli==0.0.20 - # via fastapi -fastapi-cloud-cli==0.11.0 - # via fastapi-cli -fastar==0.8.0 - # via fastapi-cloud-cli -filelock==3.20.3 - # via runpod -frozenlist==1.8.0 - # via - # aiohttp - # aiosignal -h11==0.16.0 - # via - # httpcore - # uvicorn -httpcore==1.0.9 - # via httpx -httptools==0.7.1 - # via uvicorn -httpx==0.28.1 - # via - # fastapi - # fastapi-cloud-cli -idna==3.11 - # via - # anyio - # email-validator - # httpx - # requests - # yarl -inquirerpy==0.3.4 - # via runpod -invoke==2.2.1 - # via paramiko -itsdangerous==2.2.0 - # via fastapi -jinja2==3.1.6 - # via fastapi -jmespath==1.1.0 - # via - # boto3 - # botocore -markdown-it-py==4.0.0 - # via rich -markupsafe==3.0.3 - # via jinja2 -mdurl==0.1.2 - # via markdown-it-py -multidict==6.7.1 - # via - # aiohttp - # yarl -numpy==2.4.2 - # via runpod-flash-examples (pyproject.toml) -orjson==3.11.7 - # via fastapi -paramiko==4.0.0 - # via runpod -pathspec==1.0.4 - # via runpod-flash -pfzy==0.3.4 - # via inquirerpy -pillow==12.1.0 - # via runpod-flash-examples (pyproject.toml) -prettytable==3.17.0 - # via runpod -prompt-toolkit==3.0.52 - # via - # inquirerpy - # questionary -propcache==0.4.1 - # via - # aiohttp - # yarl -py-cpuinfo==9.0.0 - # via runpod -pycares==5.0.1 - # via aiodns -pycparser==3.0 - # via cffi -pydantic==2.12.5 - # via - # fastapi - # fastapi-cloud-cli - # pydantic-extra-types - # pydantic-settings - # runpod-flash -pydantic-core==2.41.5 - # via pydantic -pydantic-extra-types==2.11.0 - # via fastapi -pydantic-settings==2.12.0 - # via fastapi -pygments==2.19.2 - # via rich -pynacl==1.6.2 - # via paramiko -python-dateutil==2.9.0.post0 - # via botocore -python-dotenv==1.2.1 - # via - # pydantic-settings - # runpod-flash - # uvicorn -python-multipart==0.0.22 - # via - # runpod-flash-examples (pyproject.toml) - # fastapi -pyyaml==6.0.3 - # via - # fastapi - # uvicorn -questionary==2.1.1 - # via runpod-flash -requests==2.32.5 - # via runpod -rich==14.3.2 - # via - # rich-toolkit - # runpod-flash - # typer -rich-toolkit==0.18.1 - # via - # fastapi-cli - # fastapi-cloud-cli -rignore==0.7.6 - # via fastapi-cloud-cli -runpod==1.8.1 - # via runpod-flash -runpod-flash==1.0.0 - # via runpod-flash-examples (pyproject.toml) -s3transfer==0.16.0 - # via boto3 -sentry-sdk==2.51.0 - # via fastapi-cloud-cli -shellingham==1.5.4 - # via typer -six==1.17.0 - # via python-dateutil -starlette==0.50.0 - # via fastapi -structlog==25.5.0 - # via runpod-flash-examples (pyproject.toml) -tomli==2.4.0 - # via runpod -tomlkit==0.14.0 - # via runpod -tqdm==4.67.3 - # via tqdm-loggable -tqdm-loggable==0.2 - # via runpod -typer==0.21.1 - # via - # fastapi-cli - # fastapi-cloud-cli - # runpod-flash -typing-extensions==4.15.0 - # via - # fastapi - # pydantic - # pydantic-core - # pydantic-extra-types - # rich-toolkit - # typer - # typing-inspection -typing-inspection==0.4.2 - # via - # pydantic - # pydantic-settings -ujson==5.11.0 - # via fastapi -urllib3==2.6.3 - # via - # botocore - # requests - # runpod - # sentry-sdk -uvicorn==0.40.0 - # via - # fastapi - # fastapi-cli - # fastapi-cloud-cli -uvloop==0.22.1 - # via uvicorn -watchdog==6.0.0 - # via runpod -watchfiles==1.1.1 - # via uvicorn -wcwidth==0.5.3 - # via - # prettytable - # prompt-toolkit -websockets==16.0 - # via uvicorn -yarl==1.22.0 - # via aiohttp +runpod-flash diff --git a/scripts/sync_example_deps.py b/scripts/sync_example_deps.py deleted file mode 100755 index 0a5dbe4..0000000 --- a/scripts/sync_example_deps.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -"""Consolidate example dependencies into root pyproject.toml. - -Simple script that collects all dependencies from example directories -and adds them to the root pyproject.toml. No conflict resolution - -if pip/uv can't resolve versions, fix manually. - -Usage: - python scripts/sync_example_deps.py # Consolidate - python scripts/sync_example_deps.py --check # Verify (CI mode) - -Make commands: - make consolidate-deps # Run consolidation - make check-deps # Verify in sync -""" - -import sys -from pathlib import Path - -import tomli_w - -try: - import tomllib -except ModuleNotFoundError: - import tomli as tomllib # type: ignore[no-redef] - -ROOT_DIR = Path(__file__).parent.parent -ROOT_PYPROJECT = ROOT_DIR / "pyproject.toml" -EXAMPLES_DIRS = [ - "01_getting_started", - "02_ml_inference", - "03_advanced_workers", - "04_scaling_performance", - "05_data_workflows", - "06_real_world", -] - - -def collect_all_deps() -> set[str]: - """Collect all dependencies from example pyproject.toml files.""" - all_deps = set() - for examples_dir in EXAMPLES_DIRS: - examples_path = ROOT_DIR / examples_dir - if not examples_path.exists(): - continue - for example_dir in examples_path.iterdir(): - if not example_dir.is_dir(): - continue - pyproject_path = example_dir / "pyproject.toml" - if pyproject_path.exists(): - with open(pyproject_path, "rb") as f: - data = tomllib.load(f) - deps = data.get("project", {}).get("dependencies", []) - all_deps.update(deps) - return all_deps - - -def main() -> None: - """Consolidate dependencies or check if consolidation is needed.""" - check_mode = "--check" in sys.argv - - with open(ROOT_PYPROJECT, "rb") as f: - root_data = tomllib.load(f) - - example_deps = collect_all_deps() - current = set(root_data["project"].get("dependencies", [])) - - if check_mode: - missing = example_deps - current - if missing: - print("ERROR: Dependencies need consolidation:") - for dep in sorted(missing): - print(f" Missing: {dep}") - print("\nRun: make consolidate-deps") - sys.exit(1) - print("✓ All example dependencies are in root") - return - - # Consolidate mode - root_data["project"]["dependencies"] = sorted(current | example_deps) - with open(ROOT_PYPROJECT, "wb") as f: - tomli_w.dump(root_data, f) - - new_count = len(example_deps - current) - print(f"✓ Consolidated {new_count} new dependencies") - print("Run 'uv sync' to install") - - -if __name__ == "__main__": - main() diff --git a/uv.lock b/uv.lock index 5e44705..8101c28 100644 --- a/uv.lock +++ b/uv.lock @@ -241,15 +241,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148 }, ] -[[package]] -name = "backports-asyncio-runner" -version = "1.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313 }, -] - 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