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-2026.6.16-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.16-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.16-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.16-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.16-cp310-cp310-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.16-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.16-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 72fdc9f6676156114e8543672ebf0e21f5929730c66414dba17b0e30d1ab7840
MD5 52edc71c91cd59b1a2abe08203db93eb
BLAKE2b-256 5b0a1d8365dd7b079439619ca23506ab6c912e4ff84911f2a0412acfa7ffcbdd

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.16-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.16-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3ad23d96fd4aa245b76ef4fc41731cdbeda2696e8da14e41d856b8057ba8457c
MD5 894f068ec4d8864893d202c482bf9117
BLAKE2b-256 7b5b90b79682debfad962aed2a8bf97a55f86c3435e4e9547dad6de031812764

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.16-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.16-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0d26b0b8b8eb8149e9075d3bcf37612f4c7bc12099041b9029b9500d85f89e0e
MD5 e9b7e4bbbb978b84e0b8184fc78dabb3
BLAKE2b-256 abe49e7acdf3330f0c9d8fc11d478722ff669a33ed9041e28ee44c033b7bfae7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.16-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.16-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 d13d6f288b3ff7f80b004c1274e2da9d2fa37d9c54ca31f6ef95c840d60e132f
MD5 a8e046d1ffc5926baa205af0cd2400e1
BLAKE2b-256 bb3564adb0e408e086e501fee2ddadac382b06d9292549155725e844202abc41

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.16-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.16-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3d26490f3ad3b196eafdea137ea46d0ab8ad65358d340ee1f5594004397c4ffd
MD5 40722bd816f57a4b853c465591a36555
BLAKE2b-256 b0d5255dac0327ac2f25ea8003046de271f5d8f51dc1935f70b4c6115671d4b1

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