huggingface / kernels
✓ Official★ 703A skill package that teaches your agent 2 capabilities — every one documented and browsable below, no GitHub required · by huggingface.
Each skill below is one capability this package teaches your agent. Install the whole package, or open a skill to install just that one.
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers…
18 files — installable on its own
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3,…
25 files — installable on its own
kernels
<div align="center"> <a href="https://huggingface.co/kernels"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-dark.png"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-light.png"> <img alt="Kernels" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kernels/kernels-thumbnail-light.png" style="max-width: 100%;"> </picture> </a> <p align="center"> <a href="https://pypi.org/project/kernels"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/kernels"></a> <a href="https://github.com/huggingface/kernels/tags"><img alt="GitHub tag" src="https://img.shields.io/github/v/tag/huggingface/kernels"></a> <a href="https://github.com/huggingface/kernels/actions/workflows/test_kernels.yaml"><img alt="Test kernels" src="https://img.shields.io/github/actions/workflow/status/huggingface/kernels/test_kernels.yaml?label=test"></a> </p> </div> <hr/>The Kernel Hub allows Python libraries and applications to load compute kernels directly from the Hub. To support this kind of dynamic loading, Hub kernels differ from traditional Python kernel packages in that they are made to be:
- Portable: a kernel can be loaded from paths outside
PYTHONPATH. - Unique: multiple versions of the same kernel can be loaded in the same Python process.
- Compatible: kernels must support all recent versions of Python and the different PyTorch build configurations (various CUDA versions and C++ ABIs). Furthermore, older C library versions must be supported.
Components
- You can load kernels from the Hub using the
kernelsPython package. - If you are a kernel author, you can build your kernels with kernel-builder.
- Hugging Face maintains a set of kernels in kernels-community.
🚀 Quick Start
Install the kernels Python package with pip (requires torch>=2.5 and CUDA):
pip install kernelsHere is how you would use the activation kernels from the Hugging Face Hub:
import torch
from kernels import get_kernel
# Download optimized kernels from the Hugging Face hub
activation = get_kernel("kernels-community/activation", version=1)
# Random tensor
x = torch.randn((10, 10), dtype=torch.float16, device="cuda")
# Run the kernel
y = torch.empty_like(x)
activation.gelu_fast(y, x)
print(y)Browse available kernels at huggingface.co/kernels.
📚 Documentation
Install the whole package (2 skills):
npx skills add https://github.com/huggingface/kernelsOr install a single skill:
npx skills add https://github.com/huggingface/kernels --skill <name>Pick the skill name from the Skills tab — each entry there installs independently.