No project description provided
Project description
FBGEMM_GPU
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:
- File a ticket in GitHub Issues
- Post a discussion in GitHub Discussions
- Reach out to us on the
#fbgemmchannel in PyTorch Slack
For contributions, please see the CONTRIBUTING file for
ways to help out.
License
FBGEMM_GPU is BSD licensed, as found in the LICENSE file.
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
Built Distributions
Hashes for fbgemm_gpu_nightly-2024.5.2-cp312-cp312-manylinux2014_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3a35167a4b9cfc77148c8f5a2ded3898a576a5c0bdc6e3b8e25d6a94d8748642 |
|
| MD5 | 097011a13da0bd10e9cd5b38a6a672b4 |
|
| BLAKE2b-256 | 122a310b6f225d211a5b156b47a2266599f2f97e95a2cf91145f93c5e3256bb5 |
Hashes for fbgemm_gpu_nightly-2024.5.2-cp311-cp311-manylinux2014_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7ec103164f072a3aec250912b37fe0522016723e6e1ce576c9c18be65c84dcc1 |
|
| MD5 | ac90a2b12e1ba14e8dc3255a098b33b2 |
|
| BLAKE2b-256 | 6d3abff76fdeafe497282a221f2adc3fdd355759c5bcbd076d50809ac5593f11 |
Hashes for fbgemm_gpu_nightly-2024.5.2-cp310-cp310-manylinux2014_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 74e4d0a5fefcbafdaa04f632a8d9db11da05027c80357db078deaafecbf65d77 |
|
| MD5 | bf5f4be41aed1bdf88b1c9e073c88b8e |
|
| BLAKE2b-256 | daf7a2f34cfd49e30d4c218f35f165ca3dbebdde833fa4fa69e1506e5df20705 |
Hashes for fbgemm_gpu_nightly-2024.5.2-cp39-cp39-manylinux2014_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ebd270010d77744430e16749ce267f7eb2ca0585365cc5a911c809bf44a365ff |
|
| MD5 | 4d295049131324bfeab11df03271acad |
|
| BLAKE2b-256 | 028ed211c41985fac118aed04ee252a4aca3d49ba946f9f050e4063c3f23b615 |
Hashes for fbgemm_gpu_nightly-2024.5.2-cp38-cp38-manylinux2014_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6061c81ca9715a634d6ac3567f3a9421450f7a2dc0e801519271d2029ea812f5 |
|
| MD5 | 63699d0a231f512550fea621a564462a |
|
| BLAKE2b-256 | b2bc902411c63329050b40239d85593d240a3ff72ae6920324f079ea4d67f0a6 |
