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-2026.3.14-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-2026.3.14-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.14-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-2026.3.14-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.14-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-2026.3.14-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.14-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-2026.3.14-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.3.14-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-2026.3.14-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-2026.3.14-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f2d3c52a7fedf4273ba453c4df5ed8260cd9cb8e850422dcffad029121b61b6b
MD5 40a2657d99044b9ef15a54450e633766
BLAKE2b-256 d4d1666d78e3d61edab7dd292c889cae86d5c1937a44478a790526c786300b9f

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 18dffd35291e9640d1467cddec285c3f13f856fc634d348fc39873506a9b4c39
MD5 0ee820625192768774725ea619202424
BLAKE2b-256 2b374156f92a092de718f18f3b231d07f8722674df802cb246330ea31b679db8

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 48bf9168512752d899be9a3ea1fac24157d157edd7854d420ffe34a144848d1d
MD5 72ee9c74f320bc44361d34897c1a9390
BLAKE2b-256 6f7aa2b8f091cd610b738717c314986f393311b93d76965830aae64e80e8d691

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b7e4337169e91af3221c4fe722105f590ed59042e6a0c463024cd890d3ba564d
MD5 5d1d3079ab148aea7b0c89d2b0a79244
BLAKE2b-256 7aa1483a5a19f0221383108a01c24d3d4854c9ab35b0192f2b2f09c8003866bf

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8b695f752bf5e1bf413fcbd43d67c80fc8b59fa30c5755ad65f920415045974b
MD5 03d630c6a16ee55286bc6849b7760f1b
BLAKE2b-256 35f8692a0910929480fb7a20b9fbb017e4a3b4757d343df17274ffb0b73943c3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 fa16ab3469717e4916fcbbe26417eeb3f154240032be805ddd7ee5e11a58b0aa
MD5 27327033fa999a7b7b657ba14632700b
BLAKE2b-256 a76aa95cc284e44c9e72e19cf55143ece3c858ddb092f8e5c144ee4f0572bdc2

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 b3711268dca82f225c257a18b771f326c661969b494e1e6d50e58dfa3fe9cf67
MD5 390843c88842cc666f5c06513277b61a
BLAKE2b-256 3cd7278607511a3bb5193d6c523224522bbb1e2d8bce43351b1a1e78f2dbf715

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0b07cdb32a4899ec837a5268cdaee80926411088bc0447cd34603fc3ce5b882e
MD5 51d7b95227838d2ff64e82930ce1f352
BLAKE2b-256 362a4684f6f70686a12bcd423181f7cf3580486884b6952c953214b6fb8f42c1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f08b00c3736669ecfa6ef16595d33c5b645544dd9df56fbee14e2ed26e9e9dc0
MD5 91fc0ce2261154f329b74786926f0b2c
BLAKE2b-256 1a68782dcfeb2c80c62bae584773dfc8db7c72941f8555d96d3cc3f28073ac50

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.3.14-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.3.14-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 f610d72891b12d887baaa05a51b972ceee95624c81d5d52f1139653784c6aab1
MD5 287dea36ccff66c40e7bf20495669bc3
BLAKE2b-256 f833537278fd9f9597db7aba37694355804d84976e7d985d36ef48372ce79fb2

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