python-ml-layer

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Core ML/AI Python environment with PyTorch, vLLM runtime deps, and CUDA support. Tier 2 environment-owner meta-layer that composes llama-cpp. Use when working with machine learning, PyTorch, HuggingFace, or GPU computing.

overthinkos By overthinkos schedule Updated 6/8/2026

name: python-ml-layer description: | Core ML/AI Python environment with PyTorch, vLLM runtime deps, and CUDA support. Tier 2 environment-owner meta-layer that composes llama-cpp. Use when working with machine learning, PyTorch, HuggingFace, or GPU computing.

python-ml -- Core ML Python environment (Tier 2 meta-layer)

Candy Properties

Property Value
Dependencies cuda
Sub-candies llama-cpp
Install files charly.yml, pixi.toml, task:

Architecture: Tier 2 Environment-Owner Meta-Layer

This candy owns the pixi.toml for the core ML Python environment and composes the llama-cpp Tier 1 candy via candy: [llama-cpp]. Build order: pixi environment → llama-cpp (binaries) → python-ml user-phase tasks (vLLM wheel).

Environment Variables

Variable Value
NVIDIA_PYTHON_PROJECT ~/.pixi
LD_LIBRARY_PATH /usr/lib64:$HOME/llama.cpp

Plus from llama-cpp sub-candy:

Variable Value
LLAMA_CPP_PATH ~/llama.cpp
PATH (appended) ~/llama.cpp

Packages (pixi.toml)

PyPI: PyTorch >= 2.10.0 (CUDA 13.0), xformers, transformers, accelerate, safetensors, numpy, scipy, einops, pillow, kornia, spandrel, torchsde, vLLM runtime deps (blake3, flashinfer, numba, ray, xgrammar, etc.), gguf, pydantic, aiohttp

Post-pixi Installs (tasks:)

  • vLLM 0.19 cu130 nightly wheel (pip install --no-deps)

Used In Boxes

  • /charly-languages:python-ml
  • /charly-immich:immich-ml

Related Candies

  • /charly-jupyter:llama-cpp — Sub-candy: llama.cpp binaries (composed via candy:)
  • /charly-distros:cuda — CUDA toolkit dependency
  • /charly-jupyter:jupyter-ml — Full ML + Jupyter variant (superset of python-ml's pixi env)
  • /charly-jupyter:unsloth-studio — Fine-tuning variant (similar pixi env + unsloth)

When to Use This Skill

Use when the user asks about:

  • Machine learning Python environment
  • PyTorch, transformers, or vLLM setup
  • CUDA Python integration
  • The python-ml candy, its packages, or its meta-layer composition
  • The two-tier candy architecture for ML candies

Related

  • /charly-image:layer — candy authoring reference (charly.yml schema, task verbs, service declarations)
  • /charly-check:check — declarative testing (check: block, charly check box, charly check live)
Install via CLI
npx skills add https://github.com/overthinkos/overthink-plugins --skill python-ml-layer
Repository Details
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