python-ml-layer
Recipe card from the charly-languages plugin (Images — the deployable catalog).
python-ml – Core ML Python environment (Tier 2 meta-layer)
Section titled “python-ml – Core ML Python environment (Tier 2 meta-layer)”Candy Properties
Section titled “Candy Properties”| Property | Value |
|---|---|
| Dependencies | cuda |
| Sub-candies | llama-cpp |
| Install files | charly.yml, pixi.toml, plan: |
Architecture: Tier 2 Environment-Owner Meta-Layer
Section titled “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
Section titled “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)
Section titled “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 (plan: run steps)
Section titled “Post-pixi Installs (plan: run steps)”- vLLM 0.19 cu130 nightly wheel (
pip install --no-deps)
Used In Boxes
Section titled “Used In Boxes”Related Candies
Section titled “Related Candies”/charly-jupyter:llama-cpp— Sub-candy: llama.cpp binaries (composed viacandy:)/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
Section titled “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-mlcandy, its packages, or its meta-layer composition - The two-tier candy architecture for ML candies
Related
Section titled “Related”/charly-image:layer— candy authoring reference (charly.ymlschema, plan steps, service declarations)/charly-check:check— declarative testing (check:block,charly check box,charly check live)