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_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e26436b89353e29becde3514ad6789438c594e49370b173ed05d8ce1b3e62766
MD5 2b8ad0c89e639aaeef31d5d9c10a28a2
BLAKE2b-256 98fad17a2498b6ff4d05afc1fd71f7f5568f09d102026022acec4655ac8e2c3a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 c82a9d75344384885da7eb7493cea85b4aa6d1b9959b781bd4eac2b94f155d68
MD5 4e952a1e589c8217819a57dd2ac4c4e5
BLAKE2b-256 fd0668eaab825d7aa07da87c768319ed585e9490f15391d10e93fbef0fed7568

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 25ff3ab4adf6627fbd88465672cac6ca99954de5b917687fdfe400d665c923c9
MD5 2cc749b061100d618cd9f42e7d972471
BLAKE2b-256 bec3762467994c964c4ae4c37f2aed69a3333c044ae12cb6d7f997640c0b9234

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 191ab955b7940a99d0cb01b37b0632208a3b4ae824a21332a9c40260419a1f5a
MD5 3ab0e31664003b1d3c94513f5946509d
BLAKE2b-256 8a4eec6459724e7f39edd6aa56758c4afa92f09d4ea72dd1a640045a70913848

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 57bdda6fec0e95087c5f53313e56473c367cb49ab9470b71338f571068264059
MD5 0e24d044275b1571dfb745a03984f99a
BLAKE2b-256 969d0bf60314f4cb7ed81d27b06ed77fe7e1191a82fa19dd46f39a4fd2550079

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f6cc654cc4a951cc2507cce28e96de17defd7d656dc442310e178a74f792335b
MD5 49319b8fa62b42f128af77d96b797297
BLAKE2b-256 4dba51f5df286c894d5f37c1981c3a5a030a660f5e5b3a7d374b973937f7ec89

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a202733cd2c6a78a5513a7d7f32b82e4242bb22b295ae43cef1c0027bdebd97d
MD5 085ff60787e440d1e3ae9763b9f001c1
BLAKE2b-256 0badccf0b1477dd331ef252aea221fecade773015a4c57beb225815924c2de6e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ac4015dd542efd4ff38dfa87f19850aa8e3d4d160ae561e1e42819cb2366065a
MD5 c3ff719409306f23c46665dbddb2d459
BLAKE2b-256 d8aa7f2ccefb6cd48fb0a22c07813f53b6db026a58ec653bb41145544fb759b9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9029478f7956080f4f4de850522ce2e9b377f9611260f3b3fe1a612fb82d1cc4
MD5 4538ca7a89ff31b7423eb0538ea8409b
BLAKE2b-256 b0f028a5605de70a091f64e507e6a19ba15339a1fbbdbafb2907f03bab027b51

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.2.21-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 057c7bad8dd5b64fa91d00846293231d40b08caf5935b2bcedb673d676d6e106
MD5 9cb334b1d632630c80777f579d511516
BLAKE2b-256 bedc065ec40a805a725d977f153cf08ec73d3683ba531b249a6e005a2fefe356

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