# 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.
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-nightly",
"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\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": "2025.10.19",
"project_urls": {
"Homepage": "https://github.com/pytorch/fbgemm"
},
"split_keywords": [
"pytorch",
" recommendation models",
" high performance computing",
" gpu",
" cuda"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "3b7e03569615ea11b2cfbc2c1442b115a7ca1ec59f8968dbf9bb3197df277654",
"md5": "4a5a610aafad5ac7250584c739077fb9",
"sha256": "0ca2bd36f9f49a34f08bd1e8dd1619e4a0e4ade4db4d61eea76e004f58db8003"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2025.10.19-cp310-cp310-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "4a5a610aafad5ac7250584c739077fb9",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 585502381,
"upload_time": "2025-10-19T15:18:03",
"upload_time_iso_8601": "2025-10-19T15:18:03.531828Z",
"url": "https://files.pythonhosted.org/packages/3b/7e/03569615ea11b2cfbc2c1442b115a7ca1ec59f8968dbf9bb3197df277654/fbgemm_gpu_nightly-2025.10.19-cp310-cp310-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "a57ea9582025fd8c552b2c2c4d931ecbb4a90721181bef57f8ce2447b467f94a",
"md5": "06dd67ba891e832774bfa87798527c42",
"sha256": "2853a05b70057358ca45eb3da76ad4fad36ad3acba6136fbc88220fc823f54a6"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2025.10.19-cp311-cp311-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "06dd67ba891e832774bfa87798527c42",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 585502051,
"upload_time": "2025-10-19T15:17:25",
"upload_time_iso_8601": "2025-10-19T15:17:25.339836Z",
"url": "https://files.pythonhosted.org/packages/a5/7e/a9582025fd8c552b2c2c4d931ecbb4a90721181bef57f8ce2447b467f94a/fbgemm_gpu_nightly-2025.10.19-cp311-cp311-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "9687fedffc5c55092ba12810a7f377176b7f2bdf43a18d164ff0e9615dfafab8",
"md5": "6d63d0a4d74ab38c9183be0e5f8d6533",
"sha256": "1b64ed183fc747b997e588856d3fd7e71d8e5a1c460e41fd2d16164505ce4164"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2025.10.19-cp312-cp312-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "6d63d0a4d74ab38c9183be0e5f8d6533",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 585502607,
"upload_time": "2025-10-19T15:14:23",
"upload_time_iso_8601": "2025-10-19T15:14:23.219835Z",
"url": "https://files.pythonhosted.org/packages/96/87/fedffc5c55092ba12810a7f377176b7f2bdf43a18d164ff0e9615dfafab8/fbgemm_gpu_nightly-2025.10.19-cp312-cp312-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "7407d125b185de47866796b1e250cc11c851b2b117fde3033e906a7f5a961c49",
"md5": "5ace216c6885a4604a826964b753b3a6",
"sha256": "5ef21d195709754695fad22a732e789935d19755b0fc6bee3a9fba62b06a201a"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2025.10.19-cp313-cp313-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "5ace216c6885a4604a826964b753b3a6",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 585503119,
"upload_time": "2025-10-19T15:14:29",
"upload_time_iso_8601": "2025-10-19T15:14:29.790112Z",
"url": "https://files.pythonhosted.org/packages/74/07/d125b185de47866796b1e250cc11c851b2b117fde3033e906a7f5a961c49/fbgemm_gpu_nightly-2025.10.19-cp313-cp313-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-10-19 15:18:03",
"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-nightly"
}
JSON
