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-1.5.0-cp313-cp313-manylinux_2_28_x86_64.whl (551.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.5.0-cp312-cp312-manylinux_2_28_x86_64.whl (551.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.5.0-cp311-cp311-manylinux_2_28_x86_64.whl (551.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu-1.5.0-cp310-cp310-manylinux_2_28_x86_64.whl (551.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

File details

Details for the file fbgemm_gpu-1.5.0-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.5.0-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e9385f86931abbf604afe892422304a01ca175eae3fddfbbb35314e5ebcb13ec
MD5 b004de4c0a0cae4653745acbf3b9a8b2
BLAKE2b-256 77457efaa5b8fc0abec76025350765302880b612b3bf4794c3363018289cebde

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.5.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.5.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0e5b5ad9e0e740c240b9e9d052e520e969e3559c0ce80547d2d3e395afc38ac4
MD5 1081a42537c9053eecea21952f65fb54
BLAKE2b-256 308ff7f1d3ef8c4bd58e8e152f00cf426c0cb75d2d6e3a0d2db23abf72a802f3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.5.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.5.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 856c84def3fc3ca43e6d5ec5458d426db9ef2ea60cc8afb3ce21a67e07208a72
MD5 ae584680a0cd07f0b95d3d3ae983de96
BLAKE2b-256 5cdb7a3849e90199746c00ec4a6dc9e8e476d7c2428bba224f54a6aeea32aa63

See more details on using hashes here.

File details

Details for the file fbgemm_gpu-1.5.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu-1.5.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 52bacb5755742bfbf300727aecf6f0733b116d3e0d57263ec136fce7ec726847
MD5 6249a884abc98c27ffb4d021aa404a3b
BLAKE2b-256 329cee1252fa6db17b5f7237101b8607a7467e492c380a4e2eafd1ae5456fba5

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