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 12.1 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

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 17cd412ae6cd719fb3a840b0561e9091abfdb7f5b4db2e1b250dc96113ebe61a
MD5 a349df92a60d98c184221d4420c5c04f
BLAKE2b-256 dfbafcca55bd265f35aadb7166c9124a81b5fe8c57160ec2409521f4f618c238

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fb69edd7efa61e59b5749789d34766b89fdab52f7c8aa48355b7c16a07f87e06
MD5 c4d71823a6a416d62179dfd90b16f656
BLAKE2b-256 8bfe59a49f41c8c9c1a04e4ed8366a5ce07c3d847cf77fe61f11359256278788

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7534be8dcb7fa446f1f4d1ad8492483fe4a1956c95183cbefba62192979d5541
MD5 3720348d4c0b2baec2ca6e26045c7e20
BLAKE2b-256 2783b963b4a13228d4b44e3831c8562bfad4df6dbd313ff3421c330dfeaa755f

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4ba570051f2da61f2193c90bebb024ba1d54d832fef0a64563d9012233fcfc39
MD5 8b9a607aee8e0b488bdc1b0ea65827cb
BLAKE2b-256 aa8f21a7c8044cd7d6c70b6cfc55aa8e95572246c4d59af943d20188a3d26530

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6128ee173b38dedb18194fdbbfa5c8e5d7a9e7bc1d59a79f52654ea039eb7de1
MD5 7d93b9636966ff259db6dbdcd80e3481
BLAKE2b-256 7cb230ff891c797061191fcda67afa24fabf4d4c3f116040298b30ae3c7f2601

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 4ae66c643b6eef5291370f3bfdf338d0b1c0b9f403673d9a398069f22667f78e
MD5 fd2faeab5f05e120828bdeeea3f82680
BLAKE2b-256 97663ff6fd454fb30327a416c1d9f8e70a9d51b02f4da95f97153d8bc4dac78d

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6a84368f86d1231cb606dae2efa9c47d38146b53836a658b712aa20ffdba4167
MD5 e3661c59607cf4ce5fca218286a57d9d
BLAKE2b-256 24da7fbdcf379033e840f72eae53f40cecbdc6e6813ef72d21b7ea608e9ce4a9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e8e1269e9db3b6140a5c53a0dd98bfd8bfe068f445326d03f02ba2590c10ebbd
MD5 09b26c34f081eda9c0462e24b78ac5fb
BLAKE2b-256 fca9aab309cb3f793ca6856af65cf49dec9834b8310267dc495a743f1a0bb3fb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page