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-2026.4.2-cp314-cp314-manylinux_2_28_x86_64.whl (34.2 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.2-cp313-cp313-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.2-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-2026.4.2-cp311-cp311-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai_nightly-2026.4.2-cp310-cp310-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.2-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.2-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c138f776f67f0db43faa349f8e0727ca3e53ba5851f59ebccfb6c5a80db1ca4d
MD5 155eba174bea20f3f18be668e2bdfc4a
BLAKE2b-256 2fb5916c087f7448532da8a676bc79bed1fe590c30d7cadef98620abf7720fd1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.2-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.2-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5642f54b3ed1da5a90755a104cd0249b138ea742ae2a1d8970983087a0b9628d
MD5 7734e9a57f0e8c61360e2a8e9c40e759
BLAKE2b-256 1bad8f9779166bae660553d949bdc388b068826ddf0ab43f28411ea5e9aa74e1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.2-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.2-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c305426f3364f884fc23a5651e9b5bf408f476ade175e98a5e0325f8ad574be8
MD5 33b5ccec4f7e787059e2c1cfceaad6fd
BLAKE2b-256 31c2626a12ed6f68dfadcd697d364f238df19e72f7afdb2aecb575ce16f1834d

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.2-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.2-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8b984cfcc082a1d802e416fe7fdf37574e388fbc7f0af22348ff5afc3dfc0920
MD5 d42321ee6c09e2556251e4dacb879cbe
BLAKE2b-256 c7ceefbcc820e5c4acc881e79c8f61a9a6cbae05e281bb23e676eb28d4bed90a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai_nightly-2026.4.2-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai_nightly-2026.4.2-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 44cf70e1fdaa6f49af0db6706a9ca12c3c01fe99b1bab32659712f846f5f649f
MD5 2525f1a6450bc755bc5372207aff5844
BLAKE2b-256 8b259d8ce8a56279d9aaff5d7decf3feae34e6c437a287f1f7c319fd83a98415

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