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-2026.7.6-cp314-cp314-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.6-cp314-cp314-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 da8ec83f5455fb6e97ab54009377d6fbd77b73557e8bb30154dc69635d68a5c3
MD5 084e4d603e9cda9ca9ec6849dcf080e6
BLAKE2b-256 ae636369efab50b74e8a42f0d00c8d380d9f1a0e3eb04d0ee214aac2a4b064c3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ccc305449f65d6cd28177f8ea4979e815c05a794546078dc1617143d959e5adc
MD5 f34d4766a22a1ce1c9d47f2d82e17d17
BLAKE2b-256 a719fe80ecf4de271351ed68395b99d7c500e121fa8cec9c48bbb37f89edba5e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 49e710daa29fb091872b4aabe2b49bf2539d089a08379156aef61cb297e0f96b
MD5 ce286269ed1252feb43939dc4338f56d
BLAKE2b-256 53766e3d6e9adc6183c2baf78089f58f5d9c7a344e72a572edab7239113a8d3e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 8e6267e341d7d7787f08c5fd1d93adfc689fc3a5db4acf4a2f090b131ec778c6
MD5 c29a300cb1c596c06c6444f1ab1952bd
BLAKE2b-256 6a323ed94ff36eb416bc73203073d11f009422e09badd46710223f84d8448ff1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1a2a3c97b04d2bcd5b06af3471b4b8b278b373a0a52247378c0e882bada61b2a
MD5 3f44119146b699a0cfeed0a346dd1f57
BLAKE2b-256 e3d8c7d4d22a6bbd178ed3e881501bc6dc9e7d269a6eb35a39cc2e9202ee966d

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 536e81ffac7fd0bb331cca164287bc1b6f2d5fc3528bf68a2372279d93a25259
MD5 1de343b6211bdbe4c647b5523ed143db
BLAKE2b-256 c6bd74604643acdc75b3e5a795a9fe38a8d14fb79552f37ea2f200db22609d1e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e5632c5f099e325998f3bdcd9ee85443365c1b54a7fa2327086338ea61f8dd65
MD5 cf040d400a8a2b207cd753eed66325ee
BLAKE2b-256 039a399d4805ca87d0f9dce8bcdf5cfd4e173347b31940efbb38f26e79670ac1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 38fde783f891947ea0d12960e4a11dedc12c2a1d2713629deb556c8b194f136d
MD5 1faf2680abd94f09cbdf49e61d3e6c0c
BLAKE2b-256 d900e960ebcca0a9073b7003ec2fb95a825f9aa5b23d07b281ea13fcfc253b67

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 de61a82a3068d9515c1957bac076fcaac51b06653653f2479cec599c8125ed85
MD5 aac9faa488b0bca465ecb72383b98327
BLAKE2b-256 6abf6c07d5ec398d65658bebd8c502af20f4a67caefa730073ff0aa3f5385264

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.6-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 1666305c361d0358f0823351b52e1f705483b217bcfa3f24b09908b199dab33e
MD5 111b3178a9ba743e50948927445ec56f
BLAKE2b-256 2a203ee902a266e28dccf29803b3d49551b7fb44704652e243d825c93d7dc0e8

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