Skip to main content

No project description provided

Project description

FBGEMM_GPU

FBGEMM_GPU-CPU CI FBGEMM_GPU-CUDA CI FBGEMM_GPU-ROCm CI

FBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch GPU operator libraries for training and inference. The library provides efficient table batched embedding bag, data layout transformation, and quantization supports.

See the full Documentation for more information on building, installing, and developing with FBGEMM_GPU, as well as the most up-to-date support matrix for this library.

Join the FBGEMM_GPU Community

For questions, support, news updates, or feature requests, please feel free to:

For contributions, please see the CONTRIBUTING file for ways to help out.

License

FBGEMM_GPU is BSD licensed, as found in the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

fbgemm_gpu_genai_nightly-2026.4.18-cp314-cp314-manylinux_2_28_x86_64.whl (34.4 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.18-cp313-cp313-manylinux_2_28_x86_64.whl (35.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.18-cp312-cp312-manylinux_2_28_x86_64.whl (34.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.18-cp311-cp311-manylinux_2_28_x86_64.whl (35.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.18-cp310-cp310-manylinux_2_28_x86_64.whl (34.4 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.18-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.18-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 43bf79cbbd553dc638601390ebe42fe9269050e560b4d237a44995cc471d8fda
MD5 f7457e2059691cdae17d825b6308580b
BLAKE2b-256 802d2e10e4088bec43262d2bff726b80c550ab7f00e94d00612c90657490b364

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.18-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.18-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0459b34e81e3c033a553ba336f9ec68dd18b96700ea5fe85c9361239b717a83e
MD5 1a1b28eac57ca54fd18e22c9b5491749
BLAKE2b-256 156a77ac2003506e4bf22accfd05b4a78c94d6c1e1e7b6ffece8cf41c725713e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.18-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.18-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 dce328ba8805a302d67c8cfcd85dce3139f149f7c5b4125da82f357e248b0db8
MD5 731964005991bbdd04b5c4b8148a46a9
BLAKE2b-256 3c55127a3d4b564c11a003feff28d7f0e2c564e5c5d3153bdd6756a2a187bc1b

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.18-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.18-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c4b02c961246ee41292b9cde935fc7207823f2f5f1f4f8f04b2170684c9bd3fb
MD5 d7a3fb17db84b861704084e90b274493
BLAKE2b-256 983927f4a6018175ca957fb01aef51cc7af502f67e28707766dd349aa000d3d2

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.18-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.18-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a8e3037ebeb9686dc2aa0e2c415dda1a15b39107bb67bc796c57dbc8b35d822d
MD5 4cd2530ccdd82373318ee9e6c2dff132
BLAKE2b-256 38547ad39ac757f904a4957da0029bd199a5bc8b6f815feaa93649c7768a15c2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page