charly-jupyter

Plugin charly-jupyter from marketplace charly-plugins in opencharly/marketplace. Commands read from the repo's manifest — 12 days ago.

Skills (15)

• jupyter-layer

  Lightweight JupyterLab with real-time collaboration (jupyter-collaboration) on port 8888. No GPU required. Use when working with collaborative notebooks, jupyter-collaboration, or lightweight Jupyter environments without ML/CUDA dependencies.

• jupyter-mcp

  JupyterLab CRDT MCP server extension exposing the notebook tools (notebook_*/cell_* + room_list + notebook_list_users) for programmatic notebook access. MUST be invoked when working with: the MCP server implementation, CRDT collaboration, the auto-attach single-room invariant, or the Tier 1 pip-only

• jupyter-ml-layer

  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 candy.

• jupyter-ml-notebook

  Full CUDA ML JupyterLab box with finetuning, Ollama, and LLM course notebooks, CRDT MCP server, and real-time collaboration. Base: nvidia. Port 8888. Combines jupyter-ml with 37 Unsloth fine-tuning notebooks, 6 Ollama integration notebooks, and 15 LLM course notebooks. MUST be invoked before buildin

• jupyter-ml

  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.

• jupyter

  Lightweight JupyterLab with real-time collaboration on port 8888. No GPU required. Based on fedora (not nvidia), supports both amd64 and arm64. MUST be invoked before building, deploying, configuring, or troubleshooting the jupyter box.

• llama-cpp

  llama.cpp prebuilt binaries and GGUF conversion tools. Use when working with llama.cpp, GGUF model conversion, or llama-quantize/llama-cli.

• notebook-finetuning

  Unsloth fine-tuning notebook collection provisioned into the workspace volume at deploy time. Data-only candy — no packages, no services, no dependencies. Use when working with notebook-finetuning, Unsloth training notebooks, or unsloth-studio data provisioning.

• notebook-llm-on-supercomputers

  LLMs on Supercomputers course notebook collection (TU Wien AI Factory Austria). 15 Jupyter notebooks covering prompt engineering, RAG, and fine-tuning. Data-only candy — no packages, no services, no dependencies. Use when working with the LLM course notebooks, LangChain tutorials, or RAG examples.

• notebook-ollama

  Ollama integration notebook collection provisioned into the workspace volume at deploy time. 6 Jupyter notebooks demonstrating Ollama via requests, OpenAI, ollama lib, Anthropic, HuggingFace, and GPU. Data-only candy — no packages, no services, no dependencies. Use when working with notebook-ollama,

• notebook-openrouter

  OpenRouter API integration notebook collection provisioned into the workspace volume at deploy time. 3 Jupyter notebooks demonstrating OpenRouter API basics, model discovery, and practical inference. Data-only candy with env_require — first candy to use the env_require feature. Use when working with

• notebook-templates

  Starter notebook templates provisioned into the workspace volume at deploy time. First data-only candy in the project — no packages, no services, no dependencies. Use when working with notebook-templates, data candies, or jupyter initial content.

• unsloth-studio-layer

  Unsloth Studio fine-tuning web UI on ports 8888/8000 with vLLM inference. Tier 2 environment-owner meta-layer composing llama-cpp + unsloth, owns pixi.toml. Use when working with Unsloth Studio, the fine-tuning web UI, or the unsloth-studio box.

• unsloth-studio

  Unsloth Studio fine-tuning web UI with CUDA GPU support, vLLM inference, and llama.cpp. Runs as a supervisord service on ports 8888 (Studio) and 8000 (vLLM API). MUST be invoked before building, deploying, configuring, or troubleshooting the unsloth-studio box.

• unsloth

  Unsloth LLM fine-tuning library with vLLM integration. Tier 1 post-install layer — no pixi.toml, requires pixi env from a parent candy (python-ml, jupyter-ml, unsloth-studio). Use when working with Unsloth, LLM fine-tuning, or vLLM wheel installation.

MCP servers (1)

• jupyter

  connects to: http://localhost:8888/mcp

Similar plugins