overthinkos/jupyter-ml
jupyter/skills/jupyter-ml/SKILL.md
Full CUDA ML JupyterLab box with real-time collaboration and CRDT MCP server. Base: nvidia. Port 8888. GPU-accelerated ML training + collaborative notebooks. MUST be invoked before building, deploying, or troubleshooting the jupyter-ml box.
tools
Updated Jun 27, 2026
$ install --global
skillsauthnpx skillsauth add overthinkos/overthink-plugins jupyter-mlInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- jupyter-ml
- description:
- |
- Base:
- nvidia. Port 8888. GPU-accelerated ML training + collaborative notebooks.
jupyter-ml -- GPU ML Jupyter with CRDT MCP
Box Definition
jupyter-ml:
base: nvidia
candy:
- agent-forwarding
- jupyter-ml
- notebook-templates
- dbus
- charly
ports:
- "8888:8888"
platforms:
- linux/amd64
Candy Composition
The jupyter-ml candy is a Tier 2 environment-owning meta-layer that composes:
llama-cpp— llama.cpp prebuilt binaries + GGUF toolsunsloth— vLLM 0.19 cu130 inference engine + fine-tuning (unsloth + unsloth-zoo) + vLLM torch.compile patchjupyter-mcp— CRDT MCP extension (fastmcp + jupyter_mcp package)
Additional candies from the box:
agent-forwarding— SSH/GPG agent forwardingnotebook-templates— Starter notebooks (data candy, seeds /workspace)dbus— D-Bus session buscharly— OpenCharly CLI
Base Chain
quay.io/fedora/fedora:43
└── fedora
└── fedora-nonfree (RPM Fusion repos)
└── nvidia (NVIDIA drivers + container toolkit)
└── jupyter-ml
Ports
| Port | Service |
|------|---------|
| 8888 | JupyterLab + MCP endpoint at /mcp |
Volumes
| Name | Path | Purpose | |------|------|---------| | workspace | /workspace | Persistent notebook storage | | models | ~/.cache/huggingface | HuggingFace model cache (from unsloth sub-candy) |
Service Environment Integration
This box receives env_provide variables from infrastructure candies when they are deployed:
| Variable | Injected by | Value |
|----------|------------|-------|
| OLLAMA_HOST | /charly-ollama:ollama | http://charly-ollama:11434 |
| PGHOST | /charly-infrastructure:postgresql | charly-postgresql |
| PGPORT | /charly-infrastructure:postgresql | 5432 |
| REDIS_URL | /charly-infrastructure:redis or /charly-infrastructure:valkey | redis://charly-<image>:6379 |
These variables are injected automatically into the container environment at charly config time when the corresponding service is deployed. No manual -e flags needed.
Key Capabilities
- JupyterLab with real-time collaboration (jupyter-collaboration, Y-CRDT)
- CRDT MCP Server at
/mcp— 11 tools for programmatic notebook access (server manages CRDT rooms invisibly; see/charly-jupyter:jupyter-mcpfor the auto-attach + canonicalization design) - PyTorch >= 2.10.0 with CUDA 13.0
- vLLM 0.19 inference engine
- Unsloth fine-tuning (LoRA, QLoRA)
- LangChain RAG stack (chromadb, faiss-cpu)
- Evaluation tools (evidently, sacrebleu, rouge-score)
- Notebook templates seeded into /workspace
Quick Start
charly box build jupyter-ml
charly config jupyter-ml
charly start jupyter-ml
charly status jupyter-ml
charly logs jupyter-ml -f
# JupyterLab: http://localhost:8888
# MCP endpoint: http://localhost:8888/mcp
Verify
charly shell jupyter-ml -c "pixi run verify-pytorch"
charly shell jupyter-ml -c "pixi run verify-vllm"
charly shell jupyter-ml -c "pixi run verify-unsloth"
charly shell jupyter-ml -c "pixi run verify-mcp"
charly shell jupyter-ml -c "pixi run verify-collaboration"
Comparison with Other Jupyter Images
| | jupyter | jupyter-ml | |---|---|---| | Base | fedora | nvidia | | CUDA | No | Yes | | Arch | amd64 + arm64 | amd64 | | MCP | CRDT (11 tools) | CRDT (11 tools) | | Collaboration | Yes | Yes | | ML Stack | No | Full | | Volume | workspace | workspace + models | | Notebook dir | /workspace | /workspace |
Related Boxes
/charly-jupyter:jupyter-ml-notebook— Same stack with fine-tuning notebooks/charly-jupyter:jupyter— Lightweight variant (no CUDA, multi-arch)/charly-languages:python-ml— ML base without Jupyter- CachyOS variant —
cachyos.jupyter-mlis the CachyOS GPU sibling (built on thecachyos.nvidiaGPU base) in theoverthinkos/cachyossubmodule. See/charly-distros:cachyos.
MCP testing: inherits 3 deploy-scope mcp: checks from the jupyter-ml candy (ping, list-tools, call list_notebooks). Run charly check live jupyter-ml --filter mcp to exercise them. See /charly-build:charly-mcp-cmd.
When to Use This Skill
MUST be invoked before building, deploying, configuring, or troubleshooting the jupyter-ml box.
Related
/charly-image:image— image family umbrella (candy:image entries — those carryingbase:/from:— incharly.yml, build/validate/inspect/list)/charly-build:build— the embedded build vocabulary (distros, builders, init-systems)
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