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 12.1.0 and 11.8 in CI, and with PyTorch packages (2.1+) that are built against those CUDA versions.
Only Intel/AMD CPUs with AVX2 extensions are currently supported.
See our Documentation for more information.
Installation
The full installation instructions for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be found here. In addition, instructions for running example tests and benchmarks can be found here.
Build Instructions
This section is intended for FBGEMM_GPU developers only. The full build instructions for the CUDA, ROCm, and CPU-only variants of FBGEMM_GPU can be found here.
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
#fbgemm
channel 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.
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
Built Distributions
Hashes for fbgemm_gpu-0.6.0-cp312-cp312-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 231268bc9d50ac73c64dcd86b3d74532730a52c1d43fc298b875cce4d718f4ec |
|
MD5 | 59f13acb3d5996ec753ee170df2f56ff |
|
BLAKE2b-256 | 2a1db9fe7239b0e84493866d8cebda1b1d417fb077ea1d4aa68f78b560aed8ab |
Hashes for fbgemm_gpu-0.6.0-cp311-cp311-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 44b51a1a650201f41bb2066063caa6adf4011b3fe2fc356b6b76018562237284 |
|
MD5 | 874703e05407e19281b21cd36ceb5e27 |
|
BLAKE2b-256 | 8b13a627993dc2f3c8e44694ec14c5006b5365f6dffd62e5e624d82dd92450af |
Hashes for fbgemm_gpu-0.6.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8136ea12ecc072fceea88296540e2b8cc2dc68cf3872f6f2543dc325ab54a88c |
|
MD5 | c75556b1c753c2d3a52f3dbd08f600ed |
|
BLAKE2b-256 | b061c3f1155d6a50821bac1cbf51a8e6f58892b99b278913638904f5b4a67b77 |
Hashes for fbgemm_gpu-0.6.0-cp39-cp39-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bee42ffe3d512b13713fb2367b999ff4235e94beea1d64cfa01dbe0f457dac29 |
|
MD5 | 49e596b8358a9961aae3d47e5e6dd9dc |
|
BLAKE2b-256 | f7112b80ed747781703216247ae6fcffe4986856340fcb4168d24814bdba2295 |
Hashes for fbgemm_gpu-0.6.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 602271f6822a275068e52fe49a16a2cef831730855fb2406261541185a73ddd8 |
|
MD5 | 08141e73bbd87c186b86454199bd6e17 |
|
BLAKE2b-256 | f6fbe0fedca06f85a8285351e4048e33ce567b1ba1f0e3aafbd900cc17aa0048 |