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 links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2026.8.15)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for fbgemm from gravatar.com fbgemm Avatar for jianyuhuang from gravatar.com jianyuhuang

Credits

Author: FBGEMM Team

License

BSD License (BSD-3)

Tags

PyTorch Recommendation Models High Performance Computing GPU CUDA

Classifiers

Report project as malware

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-2026.8.7-cp314-cp314-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.7-cp314-cp314-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_x86_64.whl (6.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 82054ed7f9c45093b18319c0a198edb5a96a911e71860ee846422af4b7ff7472
MD5 d935f1423a3af1326ae7fe452d81e1a7
BLAKE2b-256 527c12638f4313b60cdb6e085468729955de167b14d4459b7b85d155883d88a4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f4eae3bf9284e1dfdbd961f71e8cf97620779881f4004d26e9691326d234c7eb
MD5 793d3590b432aa2c39a60ae0f740edae
BLAKE2b-256 e73093479dfd081db8e41eafa877160967ccac5e3acfdb6ea2db608c97fe315c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2849d0b139ff6fe04969ec49e18136c4ba9d51e96f161187370c27f6d0a74e45
MD5 519e1b104a7a16585c5e0854392b3585
BLAKE2b-256 f6bd1bc899e283942e71eaadba4ddea8275519f5a516d45e905eeb3ae8f67d69

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 828e666d41b33ee0b3a00c30dbaa3b93fc3baf9af106e2aa113750a1bc1a7e4b
MD5 dd70eb702cc018d1f62ca11a443c23eb
BLAKE2b-256 c2d1e5f27662706805edf5a9bef3a1ed687994052213ab51d56cf08cb146dfa2

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9501086c4e16d2097bfbed1fa425debcf55edb863e7cae75ac103720abdd8fce
MD5 0313b644afb0df861aef01579bc39b69
BLAKE2b-256 2ee9e0d357ff1dab2ae4cb46bf2f8be9ee28baca0cd7dff4daa8c54620890203

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 7ab376e11616d880e58d6b93b43acfafd70e4921b0e2f01cd771d7a25dddbda7
MD5 a259c3484b74fa3b6760f2028898665c
BLAKE2b-256 655fbc45c2248e409a4dac2d79cb8f1ca3882695162f07bd897dd810b348b064

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3006e5c00fd7fa2b783210a4f3b928f73eb2401758c6fdb65ca4f7dd8f073c37
MD5 c2eb70d44be3fa03ef7f7b8c6ddeb14a
BLAKE2b-256 f726d5550ec8e52948c36c7701993b23a7d5b3e3defbefdb4d7388154c16b6b3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0f69218290b60ea5edb96d349dd885fde16537a58956f2d431dc614e97399ffe
MD5 2727d88cad55d2f916a3e5a2150872f0
BLAKE2b-256 746152a7eee493da386797b7da018c3c25ac8addc20257cb71d7b422bbf6b3ba

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c06efc494faea86722fb8b067fd06ffd7497d2e7b019fe0e9b1740137e20aebc
MD5 8e5ce645ca5fcd6d32de7c9a8bdc030f
BLAKE2b-256 67dcdd531ec349fe456ee1356453af66af1aa8f095ef169f9a49b9bf63508381

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.8.7-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 a3e2e9d1d93caba7db5278c04c9bb6ce9c061bc8d0dec50bfa424396a29f6ce6
MD5 7b4026e5c872e6fffceda22b1a84c788
BLAKE2b-256 af47eafbf43fd1f8865ce133e6252a8302438ed0c9637cdf13ad9e9d6dcbeaf8

See more details on using hashes here.

Release history Release notifications | RSS feed

2026.8.15

2026.8.14

2026.8.13

2026.8.12

2026.8.11

2026.8.10

2026.8.9

2026.8.8

This release

2026.8.7

2026.8.6

2026.8.5

2026.8.4

2026.8.3

2026.8.2

2026.8.1

2026.7.31

2026.7.30

2026.7.29

2026.7.28

2026.7.27

2026.7.26

2026.7.25

2026.7.24

2026.7.21

2026.7.20

2026.7.19

2026.7.18

2026.7.17

2026.7.16

2026.7.15

2026.7.14

2026.7.13

2026.7.12

2026.7.11

2026.7.10

2026.7.9

2026.7.8

2026.7.7

2026.7.6

2026.7.5

2026.7.4

2026.7.3

2026.7.2

2026.7.1

2026.6.30

2026.6.29

2026.6.28

2026.6.27

2026.6.26

2026.6.25

2026.6.24

2026.6.23

2026.6.22

2026.6.21

2026.6.20

2026.6.19

2026.6.18

2026.6.16

2026.6.15

2026.6.14

2026.6.13

2026.6.12

2026.6.11

2026.6.10

2026.6.9

2026.6.8

2026.6.7

2026.6.6

2026.6.5

2026.6.4

2026.6.3

2026.6.2

2026.6.1

2026.5.31

2026.5.30

2026.5.29

2026.5.28

2026.5.27

2026.5.26

2026.5.25

2026.5.24

2026.5.23

2026.5.22

2026.5.21

2026.5.20

2026.5.19

2026.5.18

2026.5.17

2026.5.16

2026.5.15

2026.5.14

2026.5.13

2026.5.8

2026.5.7

2026.5.6

2026.5.5

2026.5.4

2026.5.3

2026.5.2

2026.5.1

2026.4.30

2026.4.29

2026.4.27

2026.4.26

2026.4.25

2026.4.22

2026.4.21

2026.4.20

2026.4.19

2026.4.18

2026.4.17

2026.4.16

2026.4.15

2026.4.14

2026.4.13

2026.4.12

2026.4.11

2026.4.10

2026.4.9

2026.4.8

2026.4.7

2026.4.6

2026.4.5

2026.4.4

2026.4.3

2026.4.2

2026.4.1

2026.3.31

2026.3.30

2026.3.29

2026.3.28

2026.3.27

2026.3.26

2026.3.25

2026.3.24

2026.3.23

2026.3.22

2026.3.21

2026.3.20

2026.3.19

2026.3.18

2026.3.17

2026.3.16

2026.3.15

2026.3.14

2026.3.13

2026.3.12

2026.3.11

2026.3.10

2026.3.9

2026.3.8

2026.3.7

2026.3.6

2026.3.5

2026.3.4

2026.3.3

Supported by

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