kernels-data
Kernels data structures (Python bindings)
Decision gist · record as of 2026-08-14
Yes, if you are building applications that use Hugging Face kernels or models requiring dynamic kernel loading from the Hub. The package is actively maintained, has no known vulnerabilities, and is widely used (top 5000 on PyPI with 975470 monthly downloads). However, verify the unclear license terms before use in proprietary projects, and confirm your environment meets the stated requirements.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch>=2.5 and CUDA.
- Python >=3.8 required.
- Medium install friction due to platform-specific wheels and compiled bindings (Rust).
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text provided. Verify the actual license before using in proprietary or restricted-license projects.
last release 2026-06-26 (49 days) · last repo commit 2026-08-14 · 723 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 975,470 downloads/mo, #4,595 on PyPI
Alternatives
Verify before relying
pip install kernels-data
from kernels import get_kernel
kernel = get_kernel("kernels-community/activation", version=1)- Exact license identifier and terms—license_treatment is marked unclear with no SPDX or raw text provided.
- Whether kernels-data is a standalone package or a data/binding component that requires additional packages for practical use.
- Compatibility matrix for specific PyTorch and CUDA versions beyond the stated torch>=2.5 requirement.
What it is and what it does
kernels-data is a compiled Python package providing data structures and bindings for the Hugging Face kernels ecosystem. It enables dynamic loading of optimized compute kernels from the Hugging Face Hub, supporting portable, versioned kernel execution across different PyTorch and CUDA configurations. The package is built with Rust and distributed as platform-specific wheels covering CPython 3.8+ on Linux, macOS, and Windows architectures.
The package is designed as part of a larger kernel-loading system where kernels can be fetched from the Hub and executed without modifying PYTHONPATH or dealing with traditional Python packaging constraints. It abstracts away compatibility concerns across different build variants and older C library versions, making it a foundational piece for applications that need to run specialized compute kernels on GPUs.
Use it for
- Load and execute optimized CUDA kernels from the Hugging Face Hub in GPU-accelerated applications.
- Build inference pipelines that dynamically fetch and run specialized compute kernels without recompilation.
- Support multiple kernel versions in the same Python process for testing or gradual migration of kernel implementations.
- Integrate with applications that rely on custom optimized kernels for performance-critical operations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building applications that use Hugging Face kernels or models requiring dynamic kernel loading from the Hub.
The package is actively maintained, has no known vulnerabilities, and is widely used (top 5000 on PyPI with 975470 monthly downloads). However, verify the unclear license terms before use in proprietary projects, and confirm your environment meets the stated requirements.
Install
kernels-data on PyPI
Before you install
Medium install friction due to platform-specific wheels and compiled bindings (Rust). Active maintenance with recent releases and no known vulnerabilities.
Requires torch>=2.5 and CUDA. Python >=3.8 required.
License in practice
License treatment is unclear—no SPDX identifier or raw license text provided. Verify the actual license before using in proprietary or restricted-license projects.
Quickstart
pip install kernels-data
from kernels import get_kernel
kernel = get_kernel("kernels-community/activation", version=1)Verify before relying
- Exact license identifier and terms—license_treatment is marked unclear with no SPDX or raw text provided.
- Whether kernels-data is a standalone package or a data/binding component that requires additional packages for practical use.
- Compatibility matrix for specific PyTorch and CUDA versions beyond the stated torch>=2.5 requirement.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 49 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 975,470 / month, #4,595 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Rust |
Evidence: kernels_data-0.16.0-cp314-cp314t-macosx_10_12_x86_64.whl; kernels_data-0.16.0-cp314-cp314t-macosx_11_0_arm64.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; kernels_data-0.16.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl; kernels_data-0.16.0-cp314-cp314t-musllinux_1_2_aarch64.whl; kernels_data-0.16.0-cp314-cp314t-musllinux_1_2_armv7l.whl; kernels_data-0.16.0-cp314-cp314t-musllinux_1_2_i686.whl; kernels_data-0.16.0-cp314-cp314t-musllinux_1_2_x86_64.whl; kernels_data-0.16.0-cp314-cp314t-win32.whl; kernels_data-0.16.0-cp314-cp314t-win_amd64.whl; kernels_data-0.16.0-cp38-abi3-macosx_10_12_x86_64.whl; kernels_data-0.16.0-cp38-abi3-macosx_11_0_arm64.whl; kernels_data-0.16.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; kernels_data-0.16.0-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; kernels_data-0.16.0-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; kernels_data-0.16.0-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl
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See also kernels · ossdata · spaces · datasets · sgl-kernel · sglang-kernel · liger-kernel · cpm-kernels · geoarrow-c