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-2026.7.25-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-2026.7.25-cp314-cp314-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.25-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-2026.7.25-cp313-cp313-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.25-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-2026.7.25-cp312-cp312-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.25-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-2026.7.25-cp311-cp311-manylinux_2_28_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.25-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-2026.7.25-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-2026.7.25-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4ae2ee6aee03397e03e16067e7f3f591689ed7fb184421cb8803e9b71d8406a2
MD5 1841783f2bfa49d8e5cbe3484efddd31
BLAKE2b-256 323ee5a249f23b2e1b3e83b11d781ebfa3f967e13d23eef0ab7ed352849ef7ba

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9c94cac5dfc4c6a04cc742bfdb2d0350510fecaa82c9507fb3a7665d1db6325e
MD5 5151ba706d46a50ec3076b2c47d3a75c
BLAKE2b-256 b5b521375d20924f068cc2650f216232174c7e7f5d941526d5cd49c90cb7aa11

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 cff50fa5afbbf098c711f8d6ac42c3fd17deeb17574bcfbffad42d62ca89d455
MD5 b3a4262395ad12da6b200ab13a9b484a
BLAKE2b-256 96528fdd3b04787cf3478451f32fb7425fc6d3d6b68faf51c05b56888cc22c7a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dde9ab6f6f045ed17f4f6f258642b7350adbe5e6939b2a247a7b0e026ff1ab19
MD5 1a5b0991c035f35ca3bffe35b905d6a6
BLAKE2b-256 90583288e9058fb3e67dc44782f65459768799361b95ea9d5e0d4384d5cf78a4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9affde1e8b20d72162ab73c62d394813a6e15dcbd137baab2528e2495fa0cc29
MD5 5614500b6fcd79a59f181944e726fee2
BLAKE2b-256 a9973cc601926cf681f8c3874c787b2a91bad2114b91fb20a3a782d5dd82d077

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 99701e5cae0c276154b9cf89e50ce041f3ba18c49ea0f3156dda608b26857550
MD5 976d2b9ce03b7a1af020f6ffbdb3de8e
BLAKE2b-256 c664e86c5ec14a1f5c1ed38d2128b637509cb616f3aa91c4f860593a104cfec9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 04bb2e65faf4aa403fdcf13628f5ed8074cca692469556e41c32a65b710e9ef1
MD5 ca6cc75ea67d7cf67be8a86df6ea83fe
BLAKE2b-256 94ace1ca02434b9e2dcead362d9f2477a03900dbb97803afaecdbea5f3ea3ea1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 920d67fceb0e5248666a0c767ae7d192bda86506b2978c898ec645fbbfb579e2
MD5 8a62ce4857c6fc374c527b978c19655c
BLAKE2b-256 1538f7cad1151862896406cb18fab8c78a54ac62897e3827cc407523faaf2f9b

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 78146532be42721afbc40328b83f58cd21bde53afc44ede4d2834b85015e87f9
MD5 57f74b7041c404de17db8ae2ba446f86
BLAKE2b-256 7ef6a1b701d28088d98f113fba8d5e79b2c7351e2cb4138b6189843756f87481

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.25-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.25-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 98c74d512244229943bb8f1c7f8062c3a87f3ae3e0572a9f96666c22496ceb1c
MD5 25675ada4b0f48d515db98ca058d45d5
BLAKE2b-256 3d3555dd366fc3d811488a95df73120cb135d754ad5c75853ca379720294cfc9

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