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-2024.11.7-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly-2024.11.7-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 70ad258d99476faf7f9b7d5dc2f35ee81e7d074497885583fdd7043b0843ff44
MD5 464555ef66db568bfea925dd78334df5
BLAKE2b-256 4a042496e72408b16519d85474a63749a9c971c4cd0ae9d9c1762c733707c4d9

See more details on using hashes here.

Provenance

File details

Details for the file fbgemm_gpu_nightly-2024.11.7-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly-2024.11.7-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 aed7858019a028b4ad332aa3ec7859a0a300e950328014589a999a6fbe6ceeac
MD5 32c800bfa243c238db0db09d4216950a
BLAKE2b-256 aad59575db0812ef769dfeea7d6ee6a11016abb8abb587b35db8f80c2dce6d74

See more details on using hashes here.

Provenance

File details

Details for the file fbgemm_gpu_nightly-2024.11.7-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly-2024.11.7-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 738dfc870d8f2c6a56081b9134ff42f54ca81ed672d0f8dd4532b3bc43f449ae
MD5 ea3676b1f9d69d0e38beb9cf7e167637
BLAKE2b-256 709af185f7253673a91ec92735320b94aeb78f2cd145a0440b5da715f1d5e138

See more details on using hashes here.

Provenance

File details

Details for the file fbgemm_gpu_nightly-2024.11.7-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly-2024.11.7-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b0cbecbf8f977389be328a267873e7784c5955ddb9d3eecbdb59576ebbd4dd66
MD5 322db724bd65f9fe0248779964544568
BLAKE2b-256 8d9b9b3ac2e8b3ddfc957b0568fd676b22427ec5a67c411a60f4bb3196348e8e

See more details on using hashes here.

Provenance

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