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.

FBGEMM_GPU is currently tested with CUDA 12.4 and 11.8 in CI, and with PyTorch packages (2.1+) that are built against those CUDA versions.

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-1.1.0-cp313-cp313-manylinux_2_28_x86_64.whl (417.2 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.1.0-cp312-cp312-manylinux_2_28_x86_64.whl (417.2 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (417.2 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (417.2 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.1.0-cp39-cp39-manylinux_2_28_x86_64.whl (417.2 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

File details

Details for the file fbgemm_gpu-1.1.0-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.1.0-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9ef7c41b4a3e050f6cb7d8312e808ba48009856dabda579ef21818a7823fadf5
MD5 19de2d1295c8ddb49cc67941e6da82df
BLAKE2b-256 bde3891a038e41c3b9cc7cd8c32b5606f77fe51a5a2979c5db0bd93170dc72cb

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.1.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.1.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1cfc1abd47f08b40e486cae7a0ffa943b04120d30453c9702c70439f7541e223
MD5 76665b7d3c56eed49dbc7a822825938e
BLAKE2b-256 fcf06a93cfe25bd13b92b3ea8d821756650d8e66873d836cdb44a4dde67e3c5e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.1.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 721638a849605d20a831348e9b3fb5cb1727577b8f6fb0e8561e91107fb1b85f
MD5 2e3949a56e604ef82e434f1d3e0685ec
BLAKE2b-256 06ad03b1b60c5c81597874a226f9274ca09af976037bae8148b40181a0ebc4fa

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.1.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 97b88a8f6895b0369782f84e31352b9b0dd548cdd10cbb14da6892bc3f792a51
MD5 0ab44a2b9b8983c10a86a4152ebf538e
BLAKE2b-256 449ca0820c23afe153d13e5b0b8e3218550cca794398fc0f4e42eb34eeae54a3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.1.0-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.1.0-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3ef2477c9ff5a1c74c2ddcf1110c4a59349b3cdeae79de926808125f0f20ec21
MD5 ddced391109446cf57f4192c5a19cd9f
BLAKE2b-256 d295c5d55bef5a800079db88594859dcdfbb015f6fca773f041a5263111ada94

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