# FBGEMM_GPU
[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml)
[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml)
[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_rocm.yml)
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](https://pytorch.org/FBGEMM) 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](https://github.com/pytorch/FBGEMM/issues)
* Post a discussion in [GitHub Discussions](https://github.com/pytorch/FBGEMM/discussions)
* Reach out to us on the `#fbgemm` channel in [PyTorch Slack](https://bit.ly/ptslack)
For contributions, please see the [`CONTRIBUTING`](../CONTRIBUTING.md) file for
ways to help out.
## License
FBGEMM_GPU is BSD licensed, as found in the [`LICENSE`](../LICENSE) file.
Raw data
{
"_id": null,
"home_page": "https://github.com/pytorch/fbgemm",
"name": "fbgemm-gpu",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "PyTorch, Recommendation Models, High Performance Computing, GPU, CUDA",
"author": "FBGEMM Team",
"author_email": "packages@pytorch.org",
"download_url": null,
"platform": null,
"description": "# FBGEMM_GPU\n\n[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml)\n[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml)\n[](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_rocm.yml)\n\nFBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance\nPyTorch GPU operator libraries for training and inference. The library provides\nefficient table batched embedding bag, data layout transformation, and\nquantization supports.\n\nFBGEMM_GPU is currently tested with CUDA 12.1 and 11.8 in CI, and with PyTorch\npackages (2.1+) that are built against those CUDA versions.\n\nSee the full [Documentation](https://pytorch.org/FBGEMM) for more information\non building, installing, and developing with FBGEMM_GPU, as well as the most\nup-to-date support matrix for this library.\n\n\n## Join the FBGEMM_GPU Community\n\nFor questions, support, news updates, or feature requests, please feel free to:\n\n* File a ticket in [GitHub Issues](https://github.com/pytorch/FBGEMM/issues)\n* Post a discussion in [GitHub Discussions](https://github.com/pytorch/FBGEMM/discussions)\n* Reach out to us on the `#fbgemm` channel in [PyTorch Slack](https://bit.ly/ptslack)\n\nFor contributions, please see the [`CONTRIBUTING`](../CONTRIBUTING.md) file for\nways to help out.\n\n\n## License\n\nFBGEMM_GPU is BSD licensed, as found in the [`LICENSE`](../LICENSE) file.\n",
"bugtrack_url": null,
"license": "BSD-3",
"summary": null,
"version": "1.0.0",
"project_urls": {
"Homepage": "https://github.com/pytorch/fbgemm"
},
"split_keywords": [
"pytorch",
" recommendation models",
" high performance computing",
" gpu",
" cuda"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "1ed62e10b1a8c32360255f3126ebe1bea2efc021d18418ecf9e68e8154e2d253",
"md5": "b8b33b9eef5c7b78a09cc68cbea4931d",
"sha256": "b0e0ff449a32618679c696531b4da4ed7c2655a073629e5b4d042bfb7d8cbef8"
},
"downloads": -1,
"filename": "fbgemm_gpu-1.0.0-cp310-cp310-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "b8b33b9eef5c7b78a09cc68cbea4931d",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 349978577,
"upload_time": "2024-10-18T01:50:22",
"upload_time_iso_8601": "2024-10-18T01:50:22.985804Z",
"url": "https://files.pythonhosted.org/packages/1e/d6/2e10b1a8c32360255f3126ebe1bea2efc021d18418ecf9e68e8154e2d253/fbgemm_gpu-1.0.0-cp310-cp310-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "116714055b4e77c7aa60a96d510c9aab6d6256bc39b628f4c6adf12e378588a8",
"md5": "09f783ddba4748ee561d7f3b8a7b2e6a",
"sha256": "a2c538dd60bfede87dcc68e81cae8216bb0615d79d7885553a9846abf248c538"
},
"downloads": -1,
"filename": "fbgemm_gpu-1.0.0-cp311-cp311-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "09f783ddba4748ee561d7f3b8a7b2e6a",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 349978739,
"upload_time": "2024-10-18T01:50:27",
"upload_time_iso_8601": "2024-10-18T01:50:27.471301Z",
"url": "https://files.pythonhosted.org/packages/11/67/14055b4e77c7aa60a96d510c9aab6d6256bc39b628f4c6adf12e378588a8/fbgemm_gpu-1.0.0-cp311-cp311-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dcf17f9a9a149a611973821f02920e9072bf0e0331842fb8a81e95ff43ef2e88",
"md5": "165e5bdd9c118ba342ef4b9fc0242fc8",
"sha256": "5189fabf06c00b6c98accf61a7ccc4b6d27f8928f2957f9f4a2cb65e4c5f84e9"
},
"downloads": -1,
"filename": "fbgemm_gpu-1.0.0-cp312-cp312-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "165e5bdd9c118ba342ef4b9fc0242fc8",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 349979180,
"upload_time": "2024-10-18T01:50:05",
"upload_time_iso_8601": "2024-10-18T01:50:05.038060Z",
"url": "https://files.pythonhosted.org/packages/dc/f1/7f9a9a149a611973821f02920e9072bf0e0331842fb8a81e95ff43ef2e88/fbgemm_gpu-1.0.0-cp312-cp312-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c005cdda6998fc57cf63db2547c322810fd02b67f2feb31af320531356a77064",
"md5": "ef5f221ddc50a90d53386305050c1211",
"sha256": "0188c4880590870f780c07fc98d8fbd5a1336475fd890837cc8b595792eccf41"
},
"downloads": -1,
"filename": "fbgemm_gpu-1.0.0-cp39-cp39-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "ef5f221ddc50a90d53386305050c1211",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 349978262,
"upload_time": "2024-10-18T01:50:27",
"upload_time_iso_8601": "2024-10-18T01:50:27.165240Z",
"url": "https://files.pythonhosted.org/packages/c0/05/cdda6998fc57cf63db2547c322810fd02b67f2feb31af320531356a77064/fbgemm_gpu-1.0.0-cp39-cp39-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-18 01:50:22",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "pytorch",
"github_project": "fbgemm",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "fbgemm-gpu"
}
JSON
