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This repo contains a Nix package that can be used to build custom machine learning kernels for PyTorch. The kernels are built using the PyTorch C++ Frontend and can be loaded from the Hub with the kernels Python package.

This builder is a core component of the larger kernel build/distribution system.

We recommend using Nix to build kernels. To speed up builds, first enable the Hugging Face binary cache:

# Install cachix and configure the cache
cachix use huggingface
# Or run once without installing cachix
nix run nixpkgs#cachix -- use huggingface

Then quick start a build with:

cd examples/relu
nix run .#build-and-copy \
  --max-jobs 2 \
  --cores 8 \
  -L

Where --max-jobs specifies the number of build variant that should be built concurrently and --cores the number of CPU cores that should be used per build variant.

The compiled kernel will then be available in the local build/ directory. We also provide Docker containers for CI builds. For a quick build:

# Using the prebuilt container
cd examples/relu
docker run --rm \
  --mount type=bind,source=$(pwd),target=/kernelcode \
  -w /kernelcode ghcr.io/huggingface/kernel-builder:main build

See dockerfiles/README.md for more options, including a user-level container for CI/CD environments.

• Writing Hub kernels
• Building kernels with Nix
• Framework-specific notes:
  • Metal
• Building kernels with Docker (for systems without Nix)
• Local kernel development (IDE integration)
• Kernel security
• Why Nix?

The generated CMake build files are based on the vLLM build infrastructure.