refac: refresh#13
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Pull request overview
This PR refreshes the examples repository branding/content and expands the example catalog by adding new Ray, Evals, and prompt assets while updating links to the datalayer.ai domain and new image hosting.
Changes:
- Updated README content across the repo (new headings, new catalog structure, updated links/assets).
- Added new example bundles: Ray CLI scripts + docs, Evals SDK examples + Makefile, prompt examples + sequence fixture, and new notebooks.
- Removed/replaced older prompt/notebook locations to match the refreshed catalog structure.
Reviewed changes
Copilot reviewed 24 out of 29 changed files in this pull request and generated 6 comments.
Show a summary per file
| File | Description |
|---|---|
| sentiment-analysis-gemma/README.md | Updates badge link and refreshes heading style. |
| README.md | Rewrites the root README with a new catalog, updated links, and updated images. |
| ray/README.md | Adds documentation for submitting Ray examples via the Datalayer Ray CLI. |
| ray/hello_ray.py | Adds a basic Ray remote execution example. |
| ray/pi_monte_carlo.py | Adds a Monte Carlo π estimation Ray example. |
| ray/actor_counter.py | Adds a stateful actor example demonstrating Ray actors. |
| pytorch/README.md | Renames/restructures PyTorch section headings. |
| pytorch/pytorch-examples.ipynb | Adds a matrix multiplication notebook intended to showcase cell-specific runtimes. |
| prompts/README.md | Adds prompt examples for Jupyter MCP usage. |
| prompts/unknown-sequence.fa | Adds an “unknown sequence” FASTA fixture referenced by prompt examples. |
| mcp-prompt/README.md | Removes the previous MCP prompt README (migrated to prompts/). |
| matrix-multiplication-pytorch/matrix-multiplication-pytorch.ipynb | Removes the previous matrix multiplication notebook (replaced by pytorch/pytorch-examples.ipynb). |
| llm-text-generation-transformers/README.md | Updates badge link and refreshes heading style. |
| llm-instruct-tuning-mistral/README.md | Updates badge link and refreshes heading style. |
| llm-inference-llama-cpp-langchain/README.md | Updates badge link and refreshes heading style. |
| llm-inference-llama-cpp-comparison/README.md | Updates badge link and refreshes heading style. |
| image-face-detection-opencv/README.md | Updates badge link/heading and swaps image URLs. |
| image-diffusion-dreambooth/README.md | Updates badge link/heading and swaps image URL. |
| image-classifier-fastai/README.md | Updates badge link/heading and swaps image URL. |
| gpu-vs-cpu/README.md | Updates badge link and refreshes heading style. |
| gpu-vs-cpu/gpu-vs-cpu.ipynb | Adds a CPU/GPU performance comparison notebook. |
| gpu-check/README.md | Updates badge link. |
| gpu-check/gpu-check.ipynb | Adds/updates GPU sanity check notebook content. |
| evals/README.md | Adds detailed documentation for Evals SDK examples and workflows. |
| evals/Makefile | Adds make targets to run batch/interactive eval examples in multiple modes. |
| evals/evals_interactive_example.py | Adds a full interactive eval example script (evalset/experiments/runs + monitoring events). |
| evals/evals_batch_example.py | Adds a full batch eval example script (evalset/experiments/runs + reporting). |
| .gitignore | Ignores generated eval reports under evals/. |
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| These are prompt examples you can use with [Jupyter MCP Server](https://github.com/datalayer/jupyter-mcp-server) | ||
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| ``` | ||
| Create matplolib examples with many variants in Jupyter. |
| ``` | ||
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| ``` | ||
| Create a jupyter notebook that uses biopython to analysis an "Unknown sequence" of DNA/RNA which happens to derive from a cornavirus genome. |
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| if requested == 'sdk-proxy': | ||
| runtimes_url = args.runtimes_url | ||
| if args.execution_target != 'cloud': | ||
| runtimes_url = runtimes_url or DEFAULT_LOCAL_RUNTIMES_URL | ||
| return ( | ||
| 'sdk', | ||
| args.iam_url or DEFAULT_LOCAL_IAM_URL, | ||
| runtimes_url, | ||
| args.ai_agents_url or DEFAULT_LOCAL_AI_AGENTS_URL, | ||
| ) |
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| "kernelspec": { | ||
| "name": "pyodide", | ||
| "display_name": "Pyodide (Python)", | ||
| "language": "python" | ||
| }, |
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| "outputs": [ | ||
| { | ||
| "ename": "ModuleNotFoundError", | ||
| "evalue": "No module named 'torch'", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[31m---------------------------------------------------------------------------\u001b[39m", | ||
| "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", | ||
| "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# Convert dataframe to torch tensor.\u001b[39;00m\n\u001b[32m 4\u001b[39m tensor = torch.tensor(df.values).float()\n", | ||
| "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'torch'" | ||
| ] | ||
| } | ||
| ], | ||
| "execution_count": 2 |
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": "# Transfer dataframe to GPU Kernel and perform computation.\nimport torch\n\n# Convert dataframe to torch tensor and transfer to GPU.\n# tensor = torch.tensor(df.values).float().cuda()\ntensor = torch.tensor(df.values).float()\n\n# Perform a intensive operator e.g. a matrix multiplication.\nresult = torch.matmul(tensor, tensor.T)\n\n# Convert to numpy.\nresult_np = result.cpu().numpy()\nresult_np", |
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