igamenovoer/pixi-install-nvidia
skills/tools/pixi/pixi-install-nvidia/SKILL.md
Use when the user says "use pixi to install <some nvidia tool>" (or similar) and wants NVIDIA/CUDA/GPU packages installed via Pixi (no sudo/apt), e.g., CUDA toolkit pieces, cuDNN/NCCL, PyTorch CUDA builds, RAPIDS.
$ install --global
skillsauthnpx skillsauth add igamenovoer/magic-context pixi-install-nvidiaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 5, 2026, 8:07 PM4.4s3 files scanned
SKILL.md
- name:
- pixi-install-nvidia
- description:
- Use when the user says "use pixi to install <some nvidia tool>" (or similar) and wants NVIDIA/CUDA/GPU packages installed via Pixi (no sudo/apt), e.g., CUDA toolkit pieces, cuDNN/NCCL, PyTorch CUDA builds, RAPIDS.
Pixi Install NVIDIA
Trigger
Use this skill when the user asks to install NVIDIA tooling via Pixi, especially in the form:
- "use pixi to install <some nvidia tool>"
Examples:
- "use pixi to install cuda"
- "use pixi to install nvcc"
- "use pixi to install cudnn/nccl"
- "use pixi to install pytorch cuda 12.1"
Overview
This skill provides workflows for setting up No-Sudo, User-Space GPU environments. By installing CUDA toolkits and libraries via Pixi, you avoid modifying the host system (apt install) and ensure perfect reproducibility across different machines.
Key Benefits
- No Root Needed: Install compilers (NVCC) and libraries (cuDNN, NCCL) without sudo.
- Isolation: Project CUDA version is independent of the host's
/usr/local/cuda. - Reproducibility:
pixi.lockguarantees the exact same driver-compatible libraries everywhere.
Workflow
1. Channel Configuration
Ensure the correct channels are present in pyproject.toml. The order determines priority.
- Command:
pixi project channel add nvidia(andpytorchif needed). - Priority:
nvidiaMUST be prioritized overconda-forgefor all NVIDIA tools and libraries (CUDA, cuDNN, NCCL, etc.), unless the user explicitly requests otherwise.
2. Adding GPU Packages
Use specific versions to ensure compatibility between CUDA and the framework. Always prefer the nvidia channel for these packages.
PyTorch (Recommended)
pixi add pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
CUDA Toolkit Components
Instead of the massive cuda-toolkit, consider adding only what's needed:
pixi add cuda-compiler cuda-libraries-dev
3. Verification
After installation, verify GPU visibility:
pixi run python -c "import torch; print(torch.cuda.is_available())"
Troubleshooting & References
- CUDA Version Mismatch: Check
nvidia-smion the host to ensure the installedpytorch-cudaversion is supported by the host driver. - Library Loading Issues: If
libcuda.soorlibcudart.soare not found, ensure the environment is activated (pixi shell). - Detailed Package List: See nvidia-packages.md for a comprehensive list of available NVIDIA and GPU libraries.
- Compiling Code: See compiling-cuda.md for instructions on using
nvccandcmakewith the Pixi-managed toolkit.
Resources
references/
- nvidia-packages.md: Comprehensive guide on channels, packages, and versioning for NVIDIA ecosystems.
- compiling-cuda.md: Guide for compiling CUDA C++ code using the Pixi-managed toolchain.
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