# 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",
"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": "1.4.1",
"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": "adb38f18f024cf053b227d8b8f5973070e29e44dacc1ae0e10e66293c79cae1a",
"md5": "a920b81b17a9372d1ffdf0873fbf358f",
"sha256": "2eaef752d29f513a1089deaa883498d1bfc8e59ad5a755941393fde46385fcfa"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai-1.4.1-cp310-cp310-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "a920b81b17a9372d1ffdf0873fbf358f",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 63256432,
"upload_time": "2025-10-24T06:26:50",
"upload_time_iso_8601": "2025-10-24T06:26:50.299229Z",
"url": "https://files.pythonhosted.org/packages/ad/b3/8f18f024cf053b227d8b8f5973070e29e44dacc1ae0e10e66293c79cae1a/fbgemm_gpu_genai-1.4.1-cp310-cp310-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "0c92cdded43731cf23106b01d18b57cc61f9e8ec6870c81ef4d9bb50fb8edc28",
"md5": "2d6f155fa5554d9806bd22253d1fd115",
"sha256": "8e77b62473aa6dc4013cfd791100eb1ed24aab3d90546d353a4fde44ae09b26c"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai-1.4.1-cp311-cp311-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "2d6f155fa5554d9806bd22253d1fd115",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 64199378,
"upload_time": "2025-10-24T06:27:01",
"upload_time_iso_8601": "2025-10-24T06:27:01.115789Z",
"url": "https://files.pythonhosted.org/packages/0c/92/cdded43731cf23106b01d18b57cc61f9e8ec6870c81ef4d9bb50fb8edc28/fbgemm_gpu_genai-1.4.1-cp311-cp311-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "20070c4daf1213df6593ca19659f80cc21cdc7b11fe089895090fdc7b788f570",
"md5": "7ec68661621b55c75896ed9ef49f7109",
"sha256": "7c7f0fa4f3423506021b4cfc7f9c1bec1ba022b92617ca8882c09304329bc029"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai-1.4.1-cp312-cp312-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "7ec68661621b55c75896ed9ef49f7109",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 63256437,
"upload_time": "2025-10-24T06:27:22",
"upload_time_iso_8601": "2025-10-24T06:27:22.018523Z",
"url": "https://files.pythonhosted.org/packages/20/07/0c4daf1213df6593ca19659f80cc21cdc7b11fe089895090fdc7b788f570/fbgemm_gpu_genai-1.4.1-cp312-cp312-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "d2a48c47c9fdd2ac9b4ecd7459dbe11831b7b12cde550ddea6a124689dd7ff42",
"md5": "867c89194a7df204c6e4c80ac373e2ae",
"sha256": "c30980f5a7cc365e2c83dc48418e50ecc6e708996e12117c22a9a435242854a8"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai-1.4.1-cp313-cp313-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "867c89194a7df204c6e4c80ac373e2ae",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 64199369,
"upload_time": "2025-10-24T06:27:04",
"upload_time_iso_8601": "2025-10-24T06:27:04.509166Z",
"url": "https://files.pythonhosted.org/packages/d2/a4/8c47c9fdd2ac9b4ecd7459dbe11831b7b12cde550ddea6a124689dd7ff42/fbgemm_gpu_genai-1.4.1-cp313-cp313-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-10-24 06:26:50",
"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"
}