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OpenCharly

jupyter-ml-layer

Recipe card from the charly-jupyter plugin (Images — the deployable catalog).

jupyter-ml – Full ML + JupyterLab with CRDT MCP

Section titled “jupyter-ml – Full ML + JupyterLab with CRDT MCP”

Candy Properties

Section titled “Candy Properties”
Property Value
Dependencies cuda, supervisord
Sub-candies llama-cpp, unsloth, jupyter-mcp
Ports 8888
Service jupyter-ml (supervisord)
Volume workspace at /workspace
Install files charly.yml, pixi.toml, plan:

Architecture: Environment-Owning Meta-Layer

Section titled “Architecture: Environment-Owning Meta-Layer”

This is a Tier 2 “environment owner” candy that:

  1. Owns the pixi.toml with ALL Python dependencies (Jupyter + ML + vLLM runtime deps)
  2. Composes three Tier 1 sub-candies via candy: [llama-cpp, unsloth, jupyter-mcp]
  3. MCP extension installed by the jupyter-mcp sub-candy (not directly in this candy’s plan:)

Build order: pixi environment → llama-cpp (binaries) → unsloth (vllm wheel + unsloth pip + patch) → jupyter-mcp (MCP extension)

Key Packages

Section titled “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 candy is multi-distro

Environment

Section titled “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-candy)
UNSLOTH_SKIP_LLAMA_CPP_INSTALL 1 (from unsloth sub-candy)
HF_HOME ~/.cache/huggingface (from unsloth sub-candy)

MCP Server Extension

Section titled “MCP Server Extension”

Same CRDT MCP server as /charly-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 /charly-jupyter:jupyter-mcp “Usage philosophy and caveats” for the design principles.

Endpoint: http://localhost:8888/mcp (Streamable HTTP, MCP spec 2025-11-25)

Comparison

Section titled “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 Boxes

Section titled “Used In Boxes”
Section titled “Related Candies”

When to Use This Skill

Section titled “When to Use This Skill”

Use when the user asks about:

  • GPU-accelerated Jupyter with collaboration
  • ML training notebooks with CRDT MCP
  • The jupyter-ml candy
  • Combining jupyter features with CUDA ML
Section titled “Related”
  • /charly-image:layer — candy authoring reference (charly.yml schema, plan steps, service declarations)
  • /charly-check:check — declarative testing (check: block, charly check box, charly check live)