kernels
Download compute kernels
Decision gist · record as of 2026-08-14
Yes, if you are building applications that benefit from optimized kernels and want to manage them as versioned Hub artifacts. The package is actively maintained, has no known vulnerabilities, and low install friction. However, it requires Python 3.10+ and a working compute environment; without those, it will not function. The Hub kernel ecosystem is still young, so verify that the kernels you need are available and compatible before committing to this approach.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10+.
- Kernels are designed for GPU compute and require a compatible CUDA environment to function.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required.
last release 2026-06-26 (49 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,147,289 downloads/mo, #2,728 on PyPI
Alternatives
Verify before relying
pip install kernels
from kernels import get_kernel
activation = get_kernel("kernels-community/activation", version=1)- Whether all kernel implementations in the Hub are compatible with your installed compute environment.
- Performance characteristics and overhead of dynamic kernel loading compared to static imports.
- Specific PyTorch version requirements beyond what the description excerpt states.
What it is and what it does
kernels is a Python package that downloads and loads compute kernels from Hugging Face Hub directly into your application at runtime. Rather than bundling kernels statically, it treats them as portable, versioned artifacts that can be loaded from outside the standard Python path, allowing multiple versions of the same kernel to coexist in a single process. The package is built to handle the complexity of varied build configurations across environments.
The typical workflow is to call `get_kernel()` with a Hub repository identifier and optional version number, then invoke kernel functions. This is most useful for performance-critical operations where hand-optimized kernels can provide significant speedup. The package depends on huggingface-hub for Hub communication, packaging and pyyaml for metadata handling, and sigstore for verification.
Use it for
- Load optimized kernels from the Hub to accelerate compute-intensive operations in applications.
- Use multiple versions of the same kernel in a single process for testing or gradual migration.
- Deploy applications that rely on optimized kernels without pre-installing them in the container.
- Access community-contributed kernels for specialized operations without vendoring custom code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building applications that benefit from optimized kernels and want to manage them as versioned Hub artifacts.
The package is actively maintained, has no known vulnerabilities, and low install friction. However, it requires Python 3.10+ and a working compute environment; without those, it will not function. The Hub kernel ecosystem is still young, so verify that the kernels you need are available and compatible before committing to this approach.
Install
kernels on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance as of 49 days ago. Requires external CUDA and compute dependencies not managed by pip.
Requires Python 3.10+. Kernels are designed for GPU compute and require a compatible CUDA environment to function.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required.
Quickstart
pip install kernels
from kernels import get_kernel
activation = get_kernel("kernels-community/activation", version=1)Verify before relying
- Whether all kernel implementations in the Hub are compatible with your installed compute environment.
- Performance characteristics and overhead of dynamic kernel loading compared to static imports.
- Specific PyTorch version requirements beyond what the description excerpt states.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packageshuggingface-hubkernels-datapackagingpyyamlsigstoretomlityping-extensionstomlkit |
| Maintenance | Actively maintained 49 days since the last release |
| First released | |
| Downloads | 3,147,289 / month, #2,728 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: kernels-0.16.0-py3-none-any.whl
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See also kernels-data · datasets · hf-transfer · hf · sgl-kernel · cpm-kernels · sglang-kernel · nvidia-cutlass-dsl-libs-cu12 · kconfiglib