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-2026.3.25-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-2026.3.25-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.25-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-2026.3.25-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.25-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-2026.3.25-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.25-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-2026.3.25-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.25-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-2026.3.25-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-2026.3.25-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3a969990a9c551840c8d0ddce8409beb464bec2bbfe90e7daade8b899fc311be
MD5 67bbb26f98bdb9a585e31eeacf3b1341
BLAKE2b-256 ec822b7769bedc56abf3a9680bad634bdc528e24e9a77cb47a2bae421ed55dc0

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3255ade0cc424d279e70987d1519f0a1c128155a150d566cffab8d716dd5eaed
MD5 0ed0c64406903c177f24f1538e3cccac
BLAKE2b-256 46ee9644a0bc747a879482155eed358c995a6fab0e1bb8630d6ada3fa59a2ece

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a8463d5d5aecc4790f20adfc090ac71181706a02173bd857db4ba53906dd14d9
MD5 e1fefe3427cd82f7dec9d16039c7908b
BLAKE2b-256 2bd2523b80cc11ad630cdfd861e1d513e91ca25113a1e5db31b3ef5b11173c16

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e35c7533ade4a6fcc9fe786cd359fc8becf009e9cba7f70009364c4b9500bf0c
MD5 ea83d221ee2ecbb37ce82f632b2b8852
BLAKE2b-256 907868c2da00a5e260fdfb38b26469ae314f87ea5696332739ddffac11ce4122

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 692d6dd855611cee51240666e1cf27edbd86f5ec32eaef3543ceb11c886af430
MD5 0e236ca3e3b11c1cd59e74e8ea1de536
BLAKE2b-256 8a07142bcc2d314b68f0f5d7f181a62f4e61bdd8c4ae71a55729f2e675b8f1a0

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3df3a14862e38d9bfc67718a678285a0e718dce55aa40e54e0576546710c7ea3
MD5 3c4688d4bace95751c26937c591fc3ec
BLAKE2b-256 47ebe5e347395c2eb8dc16ab9ea25f4e95663e80a912e0570fef750dba84cf2a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 03539192162096d79e3d1e8f12a4bb7d4324932fa9516d94eda777afeb2cd682
MD5 5487744d626f2876cff47377d4d3f700
BLAKE2b-256 5df9a2c76efd191b57e0716b61d02f2c90331c7ba631ff7b12949473827ff59c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 1215d9eaae8e314c92f5c4fe1a0d63dda589c430af3dbbced1d3691634b26447
MD5 77c0b96782369968030c2658bf021cc7
BLAKE2b-256 115214246030e527c7198cae335aaf9bbe01d2ef03b70eea56785f45d7962356

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 cba74e3f580697c48dd286f66d2f8f1829c84c68a154c54963a5fc729a070834
MD5 04e40a812c830ba2e488a0bfc3ecaa7e
BLAKE2b-256 b7f77720f57064da07ee068b091ef6356be752d1d959228f6e9c3ef57d5d7f5e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.25-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.25-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b84d367de356151b4295b7673bfc2f03457bfcb30076b57dd1e11bfa816f8686
MD5 13665bd1dd7575c6a1bbbd188331a5ef
BLAKE2b-256 27ee8580dc306729e4a35c9b2dc3fac7ab7b966498aacbd60315f327245ed858

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