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
#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_nightly_cpu-2024.11.11-cp312-cp312-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0c247b36763a6281fa9d6d60b2c54e2aca3c366c39962393b01ba1e50f3930f8 |
|
MD5 | 8c25a55aef95174f58aebadf49d90a4a |
|
BLAKE2b-256 | e6167911395e52692f71424491817643379d47c42a1f9b5579831dabbea12a84 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp312-cp312-manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d36a56cef315b4bcfe4a5817bfdf6926c022c4db241ff64cfb89f7b6e34fd3b6 |
|
MD5 | f7b7ac0a94e3535f3cbf38e38853ea5c |
|
BLAKE2b-256 | c62819d8ca17306b3824a712561d8ca72fea1bdb1d43356ee7c9c6bfbdb99bc6 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp311-cp311-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 297e6d79b05d9e456f245e2819cb07481f7b39c4036d02120dc5d9f9e73a31f2 |
|
MD5 | 98b5425588d6db74068688f7c9705c8c |
|
BLAKE2b-256 | f3a30f9f0cf1b062f6ed42dd18caf434310866a9939be0c8b3affb035db22fde |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp311-cp311-manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b6eaa6ce742502d296e340706d951d34e2e5be8c6c552743f726ce876fb91ca9 |
|
MD5 | b76f6624bc5d22d62d0e3b8984a5edf3 |
|
BLAKE2b-256 | b6c8cbedb73ebbc67131a45b85cebfa29bd8137f56869bb7c123a0ab5f408572 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp310-cp310-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c25ee6a25efc299b07d06840a7e57b40eb4254de49b4e96ee9ea17dd1d4b8cb5 |
|
MD5 | 5e0f00f2b9b337838c617775bf326a0e |
|
BLAKE2b-256 | 3b4f741f7f17a24c103af02bc60c57494ec9fa00714f48a4d3658a173c655736 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp310-cp310-manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c1e6fbef047160374fdf2b5580d57e76a9f81cb8913b35942d9b358e77f8b7da |
|
MD5 | e80d2b795b33d1513af9dc6a7b382727 |
|
BLAKE2b-256 | 3d6910253ddb58c1e2dc512f357c6ee51a4b605698e4f0a3545eb7f11e82ed70 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp39-cp39-manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5db35e476abfd909869cb7020faecfc2c7ac3ee89ca3d51b3683471ec77faa3c |
|
MD5 | e822964055bf4926e0f73d22bf20e82f |
|
BLAKE2b-256 | 31131f2da0d60b0c33888a711ebb4f927577621eec59b737b5463051b5fcc862 |
Hashes for fbgemm_gpu_nightly_cpu-2024.11.11-cp39-cp39-manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f0f86e55baadf006c9cb1e5fbe4e3a43947aabca8e249224234424d712f85f5d |
|
MD5 | 6d8b7b7e687c985a11b88a17e0e2ee5e |
|
BLAKE2b-256 | c34e1f784c48a0d7abaabd1938205cb304b171b534129363dcf5d6ae7ca81e38 |