kernels

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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 kernels Python 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)

You can search for kernels on
the Hub.

📚 Documentation

Read the documentation of kernels and kernel-builder.