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_genai_nightly-2026.5.15-cp314-cp314-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.5.15-cp313-cp313-manylinux_2_28_x86_64.whl (34.2 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.5.15-cp312-cp312-manylinux_2_28_x86_64.whl (34.2 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.5.15-cp311-cp311-manylinux_2_28_x86_64.whl (34.2 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.5.15-cp310-cp310-manylinux_2_28_x86_64.whl (34.2 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

File details

Details for the file fbgemm_gpu_genai_nightly-2026.5.15-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.5.15-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9b906b1b4b19b19fad5cb37eaaa7cff52d3055371c4aefc3a0087eea8570cb51
MD5 1a05cac93e78374469a1e8812e16a9ea
BLAKE2b-256 69bdd24176f7893abe9d23693aef92fd19025792f0fede12d4f318db9f37a291

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.5.15-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.5.15-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ba08a29e44de86bdda076353a4cd53c31fca481af1eb988b6854745b93a4fae8
MD5 8259dd4c4f2158533755c856f755b7b3
BLAKE2b-256 5878b956a8271ad581bc25e9e2ceff35825271bf4827ed1a0b06387c2ccaf772

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.5.15-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.5.15-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9a3ddd347ccd96ecceb8dbb93390161891590926dc80c2c5bbd827024d4724cc
MD5 c3bd374baf30b038d3872750efa18f6b
BLAKE2b-256 3dad28d07474cf935817635d8838d9721465f9697a4f6363a71449904ca75976

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.5.15-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.5.15-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 df8215d730245432df4f88846a0f0f758794d19d15d1da590827139c4467677e
MD5 5643e8c9398b51db2042a6134bbd1d74
BLAKE2b-256 2443711dbf93819f0102638e85a9ffc8c1abe46885c0d7624c535df87377dd40

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.5.15-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.5.15-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a655e834cc94414ca6b870f455995c4bb6cc6cbd14c8d55b0343157f8dafe5b8
MD5 710919108d7b395ffc0723118cf9b138
BLAKE2b-256 aab814af3da047e57acc6ab07258b47946f4985d3765e5c9194cd5b451dba09e

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