# 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": "2025.7.29",
"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": "a2834477a1ed51409a4ae43f1d9bbd2f7f182a2ab3b3bc7b86010ce1f6fad353",
"md5": "ca34bc84c33f9aaec11d903307e326e0",
"sha256": "b0636690302738b56c5f5e129339fd09a544df5b678b8ea0c79b4d9d3554330b"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.7.29-cp310-cp310-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "ca34bc84c33f9aaec11d903307e326e0",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 14209898,
"upload_time": "2025-07-29T14:53:58",
"upload_time_iso_8601": "2025-07-29T14:53:58.319877Z",
"url": "https://files.pythonhosted.org/packages/a2/83/4477a1ed51409a4ae43f1d9bbd2f7f182a2ab3b3bc7b86010ce1f6fad353/fbgemm_gpu_genai_nightly-2025.7.29-cp310-cp310-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "ac35fdd34194edc67225ff400e4f13393ee35caea380c35ce18fe9d4d384378b",
"md5": "209d98157404f5e571385090e7e55d11",
"sha256": "6662268d18dd02135bbffee62db30b3e9004cdd488533bead4153b1a24ab8469"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.7.29-cp311-cp311-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "209d98157404f5e571385090e7e55d11",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 14132747,
"upload_time": "2025-07-29T14:53:54",
"upload_time_iso_8601": "2025-07-29T14:53:54.584575Z",
"url": "https://files.pythonhosted.org/packages/ac/35/fdd34194edc67225ff400e4f13393ee35caea380c35ce18fe9d4d384378b/fbgemm_gpu_genai_nightly-2025.7.29-cp311-cp311-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "20157636b3ffc3107ef7aa9c2fbc76c2c52b9efd17b4facd496900eca4df981a",
"md5": "282931bad1f64b3d01f7d684d617a0ce",
"sha256": "59bf65224f2db5a225afbf1c53fadfe3ab7234c060a9fcfaff15c8bffda3b7a4"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.7.29-cp312-cp312-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "282931bad1f64b3d01f7d684d617a0ce",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 14209534,
"upload_time": "2025-07-29T14:53:54",
"upload_time_iso_8601": "2025-07-29T14:53:54.297684Z",
"url": "https://files.pythonhosted.org/packages/20/15/7636b3ffc3107ef7aa9c2fbc76c2c52b9efd17b4facd496900eca4df981a/fbgemm_gpu_genai_nightly-2025.7.29-cp312-cp312-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "93ebebccc2447543cd6bfb0a5d9989f39ab46df4f0edbe6d7074bdfbd34da342",
"md5": "298c73fe5d43d0f07c4b211558605367",
"sha256": "e926cc388d8681c6d1286e0309649d42f1724d0a7c991311753154cb1a7da153"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.7.29-cp313-cp313-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "298c73fe5d43d0f07c4b211558605367",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 14132580,
"upload_time": "2025-07-29T14:53:22",
"upload_time_iso_8601": "2025-07-29T14:53:22.387160Z",
"url": "https://files.pythonhosted.org/packages/93/eb/ebccc2447543cd6bfb0a5d9989f39ab46df4f0edbe6d7074bdfbd34da342/fbgemm_gpu_genai_nightly-2025.7.29-cp313-cp313-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "8a609bb32a8b43b1b89a11b86dc0356ceccf25f10123d5ec48d1a642fea4d01f",
"md5": "6dff85623697436a465ab8e57e442ee1",
"sha256": "55e6d7c88c61f0c44d21e503bc29aded8c0f5a628fa9f869306e9e6581e70fe1"
},
"downloads": -1,
"filename": "fbgemm_gpu_genai_nightly-2025.7.29-cp39-cp39-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "6dff85623697436a465ab8e57e442ee1",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 14132558,
"upload_time": "2025-07-29T14:53:16",
"upload_time_iso_8601": "2025-07-29T14:53:16.217254Z",
"url": "https://files.pythonhosted.org/packages/8a/60/9bb32a8b43b1b89a11b86dc0356ceccf25f10123d5ec48d1a642fea4d01f/fbgemm_gpu_genai_nightly-2025.7.29-cp39-cp39-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-29 14:53:58",
"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"
}