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-1.6.0-cp314-cp314-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai-1.6.0-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-1.6.0-cp312-cp312-manylinux_2_28_x86_64.whl (35.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_genai-1.6.0-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-1.6.0-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-1.6.0-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai-1.6.0-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c7ce11bd492480501ff9c17e4930888632b0a9c571dca020c2637c13b9f140ca
MD5 1a8e8add1e793de2be6c843685b3627b
BLAKE2b-256 144750d53c4d4b35dc678dc18dbd9b07e7969f1f1af9d61d19fba2ab8eea30b5

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai-1.6.0-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai-1.6.0-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 7f5756bde5be74db15b93b1d8375e9e8757aa18d3d93862d57966e530ab87bd1
MD5 5d5373baee99833dd4bbd78caea77130
BLAKE2b-256 99473927e3208d11e448b405b71bd92f4d2741d167df4fae9d9a1adfc7b900b9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai-1.6.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai-1.6.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 bf8b4cbc21b0a9a3906dd4a418d453b423fd22ddc8bd31b005857c5c5eae8ad2
MD5 31339c287f5ddee1e0c3c2360f456ee5
BLAKE2b-256 ee17ee52b7d24ea735408d46083818476f54392bf6e3278f92862edfda8549b4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai-1.6.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai-1.6.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0c0ed6add68b83edc217ead4e6feb9c6f3e8497354e90224485533e8fffa96c3
MD5 10dad65e6616e895686ca3ac0bd1b78b
BLAKE2b-256 171b5708bb072083c9b79a344798fb41fe06e5a946a28081f7a61639fd519bdc

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_genai-1.6.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_genai-1.6.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 96bbd0f17a411e3a9b9d1f2875db0246929997ef8ff2faa4a4b89099a380e258
MD5 dd5d5f3ac8e9bcab59d7ff89c786820f
BLAKE2b-256 ec3750dfcf65158beadffea4de22e762c1defa7122b1676dbda697f90bd72e0c

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