How do I install the Pixi Install Nvidia skill?

Run npx skillmds add majiayu000/pixi-install-nvidia in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Pixi Install Nvidia free to use?

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Who published Pixi Install Nvidia?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pixi Install Nvidia by majiayu000 — Agent Skill

Name: Pixi Install Nvidia
Rating: 4.2 (5 reviews)
Author: majiayu000

Pixi Install Nvidia

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.

majiayu000 ba38532 2 files · 3.7 KB Updated Sep 9, 2026 567 repo stars

File contents

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 "

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.lock guarantees 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 (and pytorch if needed).
Priority: nvidia MUST be prioritized over conda-forge for 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-smi on the host to ensure the installed pytorch-cuda version is supported by the host driver.
Library Loading Issues: If libcuda.so or libcudart.so are 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 nvcc and cmake with 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.

---name: pixi-install-nvidiadescription: 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.lock` guarantees 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` (and `pytorch` if needed). * **Priority**: `nvidia` **MUST** be prioritized over `conda-forge` for 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) ```bash 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: ```bash pixi add cuda-compiler cuda-libraries-dev ``` ### 3. Verification After installation, verify GPU visibility: ```bash pixi run python -c "import torch; print(torch.cuda.is_available())" ``` ## Troubleshooting & References * **CUDA Version Mismatch**: Check `nvidia-smi` on the host to ensure the installed `pytorch-cuda` version is supported by the host driver. * **Library Loading Issues**: If `libcuda.so` or `libcudart.so` are not found, ensure the environment is activated (`pixi shell`). * **Detailed Package List**: See [nvidia-packages.md](references/nvidia-packages.md) for a comprehensive list of available NVIDIA and GPU libraries. * **Compiling Code**: See [compiling-cuda.md](references/compiling-cuda.md) for instructions on using `nvcc` and `cmake` with the Pixi-managed toolkit. ## Resources ### references/ * **[nvidia-packages.md](references/nvidia-packages.md)**: Comprehensive guide on channels, packages, and versioning for NVIDIA ecosystems. * **[compiling-cuda.md](references/compiling-cuda.md)**: Guide for compiling CUDA C++ code using the Pixi-managed toolchain.

Frequently asked questions