overthinkos/jupyter-ml-layer
jupyter/skills/jupyter-ml-layer/SKILL.md
Full CUDA ML stack + JupyterLab with real-time collaboration and CRDT MCP server on port 8888. Use when working with GPU-accelerated Jupyter notebooks, ML training with collaboration, or the jupyter-ml layer.
tools
Updated May 25, 2026
$ install --global
skillsauthnpx skillsauth add overthinkos/overthink-plugins jupyter-ml-layerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- jupyter-ml-layer
- description:
- |
jupyter-ml -- Full ML + JupyterLab with CRDT MCP
Layer Properties
| Property | Value |
|----------|-------|
| Dependencies | cuda, supervisord |
| Sub-layers | llama-cpp, unsloth, jupyter-mcp |
| Ports | 8888 |
| Service | jupyter-ml (supervisord) |
| Volume | workspace at /workspace |
| Install files | layer.yml, pixi.toml, task: |
Architecture: Environment-Owning Meta-Layer
This is a Tier 2 "environment owner" layer that:
- Owns the pixi.toml with ALL Python dependencies (Jupyter + ML + vLLM runtime deps)
- Composes three Tier 1 sub-layers via
layers: [llama-cpp, unsloth, jupyter-mcp] - MCP extension installed by the
jupyter-mcpsub-layer (not directly in tasks:)
Build order: pixi environment → llama-cpp (binaries) → unsloth (vllm wheel + unsloth pip + patch) → jupyter-mcp (MCP extension)
Key Packages
conda-forge: JupyterLab >= 4.4.0, jupyter-resource-usage, jupyterlab-git, jupyterlab-lsp, jupyterlab-spellchecker, tensorboard, wandb, matplotlib, seaborn, pandas, numpy, scikit-learn, scipy, polars, pyarrow, dask, duckdb, altair, papermill, marimo, mkdocs, black, pytest
PyPI (ML Core): PyTorch >= 2.10.0 (CUDA 13.0), xformers, transformers >= 5.0.0rc1, accelerate, einops, kornia, spandrel, torchsde
PyPI (vLLM Runtime): blake3, flashinfer-python, numba, ray, xgrammar, and 25+ more runtime deps
PyPI (Fine-tuning): peft, trl, bitsandbytes, deepspeed, liger-kernel
PyPI (LangChain): langchain, langchain-core, langchain-openai, langchain-community, langchain-classic, langchain-anthropic, langchain-huggingface, langchain-ollama, chromadb, faiss-cpu
PyPI (Evaluation): evidently (with llm extras), evaluate, sacrebleu, rouge-score, nltk, bertviz
PyPI (APIs): openai, anthropic, gradio, ollama (client)
PyPI (Collaboration): jupyter-collaboration >= 4.1.0
RPM (Fedora): git, gcc, gcc-c++
PAC (Arch/CachyOS): git, gcc (includes g++) — the layer is multi-distro
Environment
| Variable | Value | Purpose |
|----------|-------|---------|
| NVIDIA_PYTHON_PROJECT | ~/.pixi | NVIDIA driver → pixi env mapping |
| LD_LIBRARY_PATH | /usr/lib64:$HOME/llama.cpp | CUDA libs + llama.cpp shared libs |
| LLAMA_CPP_PATH | ~/llama.cpp | (from llama-cpp sub-layer) |
| UNSLOTH_SKIP_LLAMA_CPP_INSTALL | 1 | (from unsloth sub-layer) |
| HF_HOME | ~/.cache/huggingface | (from unsloth sub-layer) |
MCP Server Extension
Same CRDT MCP server as /ov-jupyter:jupyter — 11 tools for programmatic notebook access (notebook_list/create/get/watch/list_users, cell_get/update/insert/delete/execute, room_list). Clients no longer manage CRDT rooms — every notebook_/cell_ call auto-attaches. See /ov-jupyter:jupyter-mcp "Usage philosophy and caveats" for the design principles.
Endpoint: http://localhost:8888/mcp (Streamable HTTP, MCP spec 2025-11-25)
Comparison
| | jupyter | jupyter-ml | |---|---|---| | Base dep | supervisord | cuda, supervisord | | GPU | No | CUDA 13.0 | | Platforms | amd64 + arm64 | amd64 only | | MCP | CRDT (11 tools) | CRDT (11 tools) | | ML stack | No | Full (PyTorch, vLLM 0.19, unsloth) | | Volume | workspace | workspace |
Used In Images
/ov-jupyter:jupyter-ml/ov-jupyter:jupyter-ml-notebook
Related Layers
/ov-jupyter:jupyter— Lightweight variant (no CUDA, multi-arch)/ov-jupyter:llama-cpp— Sub-layer: llama.cpp binaries/ov-jupyter:unsloth— Sub-layer: vLLM wheel + fine-tuning + vLLM patch/ov-jupyter:jupyter-mcp— Sub-layer: CRDT MCP extension/ov-jupyter:notebook-templates— Starter notebooks (data layer, used alongside this layer in images)/ov-hermes:hermes— MCP consumer (auto-discovers viaOV_MCP_SERVERS; uses jupyter tools to read/edit/execute cells)/ov-openwebui:openwebui— MCP consumer (setsCODE_EXECUTION_ENGINE=jupyterwhen this server is discovered, routing Open WebUI code blocks to the Jupyter kernel)
When to Use This Skill
Use when the user asks about:
- GPU-accelerated Jupyter with collaboration
- ML training notebooks with CRDT MCP
- The
jupyter-mllayer - Combining jupyter features with CUDA ML
Related
/ov-image:layer— layer authoring reference (layer.ymlschema, task verbs, service declarations)/ov-eval:eval— declarative testing (eval:block,ov eval image,ov eval live)
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