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


Release history Release notifications | RSS feed

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-2026.5.26-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-2026.5.26-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.5.26-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-2026.5.26-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.5.26-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.5.26-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-2026.5.26-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.5.26-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-2026.5.26-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-2026.5.26-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e2f0a88f6dd249895cb4414c1764fdf5ca74977fc9aad7e72edb8a194e72e8f5
MD5 580caa0ffbcec9b700f675cf9b1aa38b
BLAKE2b-256 16ff1a5f75c52b6e2328f23304ad9d909efa53adf743b47ff718dca90ed353d6

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 11f9cdefe70f907c5b8b5d12785e7b05ea7389dbc6cff484930f16fe2f325bd5
MD5 ddcdb7e2d5927569aa1e06e0a838e2f7
BLAKE2b-256 cfac73202a225f02f7c4cd3b427ba41a55dac5586b7ab9f51f00cad7e3d4a14b

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1348f49ea334a6e68b1cb3b5b9ea02b7975cd90a7e7272fa3136ad91b382638c
MD5 573751fd197c6db79e70b591a62f7f85
BLAKE2b-256 a972a7256e8935fddaded3dadfd125cce096a4af85d525ec051ec3832bc9d66a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 bf1ba9d3c61372cb3417ead75cbec14c978d789b26e0d6499a7c619535b6aca1
MD5 f89b43cffee568983c02ef4c89aa36a3
BLAKE2b-256 9b454abe202d1e395d35a41e221b9a72aa5e9cda96c0018124211907b0b52d09

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 5b4be3f372e3860bfe624bccf48d6037cfe19a6407d2abc62a40565b033be85b
MD5 f71ff5b65ac5a47ab05195d3d8f0e6a6
BLAKE2b-256 bac7518cc873dfee1333eaec6ba7aa41c1e31bccc4bc9d209ae36dd1af9f0a69

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 439d39b75df7ca8a66bad26f21bb262393078afb53147ae45e10c8493dffc710
MD5 236f18d475bcfb37f94fdf1dde77ce7b
BLAKE2b-256 f072eaddef32e0bb460df93c870d76525add6382475c3e0667ae88036ba1a251

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dcbb0d29ba647b6991a3d17bdd9572e423b7d7ccae75779b5925777c706cde81
MD5 ecdb2a46ad9e750c198d77afa468dc6c
BLAKE2b-256 1225d9ad0d59affadd331f1f66b9f5156159768eb8a53e7ea5173744e3bf7cf9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5c051dafde99a568e5a3ed7db2521193179a21b320008fd549d4e1f6179605d2
MD5 03a4aa0b9236c4d2d5600ac5e433ba62
BLAKE2b-256 7714e08d3c6cce8af3ad6111e12a42d3d3d16e93b589c19b825a54dd50d9df64

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.5.26-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.5.26-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f42d308a5fadeefddaedbb56d9b14aa05f8da08e541adb4aeb61422904d11d37
MD5 ff0752ab00e9899aeb2412eff2d09924
BLAKE2b-256 d22bba7caf922b0026ca7e15914c345a2cbd42dfd7bb943d045f35c338710d1c

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