# 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-genai-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": "2025.9.14",
"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": "20b6704fec7d75d5f91550e6c1911845e93855e56dd1cd3b43426d1941a529f1",
"md5": "69aeaddda6cb099beec7ac7f491544df",
"sha256": "fe7960a9cc52111223edfe65031369bb338e98443f5f43a0b6694da67f46de06"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.9.14-cp310-cp310-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "69aeaddda6cb099beec7ac7f491544df",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 16411169,
"upload_time": "2025-09-14T14:38:52",
"upload_time_iso_8601": "2025-09-14T14:38:52.514568Z",
"url": "https://files.pythonhosted.org/packages/20/b6/704fec7d75d5f91550e6c1911845e93855e56dd1cd3b43426d1941a529f1/fbgemm_gpu_genai_nightly-2025.9.14-cp310-cp310-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "53257836c500a182758b2c6fc2616a73e852b88f6d0513a62e067f5f0842fec9",
"md5": "e0df01ac575f71339d17fc2c00221b38",
"sha256": "5499a173a9a846354908f205a3bc3305b58a8ef4352aa019a5fdb2e56d4bea72"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.9.14-cp311-cp311-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "e0df01ac575f71339d17fc2c00221b38",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 16409579,
"upload_time": "2025-09-14T14:38:55",
"upload_time_iso_8601": "2025-09-14T14:38:55.199429Z",
"url": "https://files.pythonhosted.org/packages/53/25/7836c500a182758b2c6fc2616a73e852b88f6d0513a62e067f5f0842fec9/fbgemm_gpu_genai_nightly-2025.9.14-cp311-cp311-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "6f65510b5da56baa5d7a2575fa0b5d93bfb2f0cb35fa393f686046e2e9fee9e8",
"md5": "63b378e735b740d16a4c470c6fb2bdcd",
"sha256": "20b2b59c7d259554c3e1dd3be90b89cff2e62e35fd8aea2697e16f3d9e094069"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.9.14-cp312-cp312-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "63b378e735b740d16a4c470c6fb2bdcd",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 16409908,
"upload_time": "2025-09-14T14:39:12",
"upload_time_iso_8601": "2025-09-14T14:39:12.688701Z",
"url": "https://files.pythonhosted.org/packages/6f/65/510b5da56baa5d7a2575fa0b5d93bfb2f0cb35fa393f686046e2e9fee9e8/fbgemm_gpu_genai_nightly-2025.9.14-cp312-cp312-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "a76fe59daf423c4c7b2a6a6f4c6b74928c498a18023add8e8635ba66424538cf",
"md5": "86022d51728d53d9cf4b542a78693d6f",
"sha256": "da940e1f61f36be69abd7dc38da0a130bad3887bda0d919f4825b1ea5d77547a"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.9.14-cp313-cp313-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "86022d51728d53d9cf4b542a78693d6f",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 16409753,
"upload_time": "2025-09-14T14:38:42",
"upload_time_iso_8601": "2025-09-14T14:38:42.697820Z",
"url": "https://files.pythonhosted.org/packages/a7/6f/e59daf423c4c7b2a6a6f4c6b74928c498a18023add8e8635ba66424538cf/fbgemm_gpu_genai_nightly-2025.9.14-cp313-cp313-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "06d7c1e70eaff332e75b3bed10cd8066c314f3ae967e7d1ed8761f5d62794ca6",
"md5": "f49683c4995071dd14db1b5b561cc5df",
"sha256": "eee6f457c15e2fe34cc856e3212bab9d708657f6a9e32ccbc07b51ede4ff993f"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.9.14-cp39-cp39-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "f49683c4995071dd14db1b5b561cc5df",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 16425464,
"upload_time": "2025-09-14T14:38:46",
"upload_time_iso_8601": "2025-09-14T14:38:46.405197Z",
"url": "https://files.pythonhosted.org/packages/06/d7/c1e70eaff332e75b3bed10cd8066c314f3ae967e7d1ed8761f5d62794ca6/fbgemm_gpu_genai_nightly-2025.9.14-cp39-cp39-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-09-14 14:38:52",
"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-genai-nightly"
}