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kernels
The Kernel Hub allows Python libraries and applications to load compute kernels directly from the Hub.
Plugin Signal70/100Solid
maintained99
capable37
documented75
adopted61
Install
$ npx -y skills add huggingface/kernels --agent claude-code
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Python
Language
Apache-2.0
License
10h ago
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1y ago
Created
Repo: huggingface/kernels
What's inside
Skill(5 skills)
/cpu-kernelsProvides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching/cuda-kernelsProvides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only./rocm-kernelsProvides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers
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README
kernels
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 kernels
Here 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
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kernels is a Claude Code plugin with 5 hand-picked skills for development work, indexed on Flowy. Install it with the command on its page. It includes cpu-kernels, cuda-kernels, rocm-kernels. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
- What is kernels?
- The Kernel Hub allows Python libraries and applications to load compute kernels directly from the Hub.
- How do I install kernels?
- Run these in Claude Code: npx -y skills add huggingface/kernels --agent claude-code. Then prompt normally.
- Does kernels auto-invoke its skills?
- Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
- Is kernels free?
- Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.
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