# 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-nightly-cpu",
"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": "2024.11.16",
"project_urls": {
"Homepage": "https://github.com/pytorch/fbgemm"
},
"split_keywords": [
"pytorch",
" recommendation models",
" high performance computing",
" gpu",
" cuda"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "97663ff6fd454fb30327a416c1d9f8e70a9d51b02f4da95f97153d8bc4dac78d",
"md5": "fd2faeab5f05e120828bdeeea3f82680",
"sha256": "4ae66c643b6eef5291370f3bfdf338d0b1c0b9f403673d9a398069f22667f78e"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "fd2faeab5f05e120828bdeeea3f82680",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 3132906,
"upload_time": "2024-11-16T13:32:34",
"upload_time_iso_8601": "2024-11-16T13:32:34.496030Z",
"url": "https://files.pythonhosted.org/packages/97/66/3ff6fd454fb30327a416c1d9f8e70a9d51b02f4da95f97153d8bc4dac78d/fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7cb230ff891c797061191fcda67afa24fabf4d4c3f116040298b30ae3c7f2601",
"md5": "7d93b9636966ff259db6dbdcd80e3481",
"sha256": "6128ee173b38dedb18194fdbbfa5c8e5d7a9e7bc1d59a79f52654ea039eb7de1"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "7d93b9636966ff259db6dbdcd80e3481",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 4283105,
"upload_time": "2024-11-16T13:23:46",
"upload_time_iso_8601": "2024-11-16T13:23:46.121146Z",
"url": "https://files.pythonhosted.org/packages/7c/b2/30ff891c797061191fcda67afa24fabf4d4c3f116040298b30ae3c7f2601/fbgemm_gpu_nightly_cpu-2024.11.16-cp310-cp310-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "aa8f21a7c8044cd7d6c70b6cfc55aa8e95572246c4d59af943d20188a3d26530",
"md5": "8b9a607aee8e0b488bdc1b0ea65827cb",
"sha256": "4ba570051f2da61f2193c90bebb024ba1d54d832fef0a64563d9012233fcfc39"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "8b9a607aee8e0b488bdc1b0ea65827cb",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 3132912,
"upload_time": "2024-11-16T13:32:19",
"upload_time_iso_8601": "2024-11-16T13:32:19.859168Z",
"url": "https://files.pythonhosted.org/packages/aa/8f/21a7c8044cd7d6c70b6cfc55aa8e95572246c4d59af943d20188a3d26530/fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2783b963b4a13228d4b44e3831c8562bfad4df6dbd313ff3421c330dfeaa755f",
"md5": "3720348d4c0b2baec2ca6e26045c7e20",
"sha256": "7534be8dcb7fa446f1f4d1ad8492483fe4a1956c95183cbefba62192979d5541"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "3720348d4c0b2baec2ca6e26045c7e20",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 4283113,
"upload_time": "2024-11-16T13:23:27",
"upload_time_iso_8601": "2024-11-16T13:23:27.304674Z",
"url": "https://files.pythonhosted.org/packages/27/83/b963b4a13228d4b44e3831c8562bfad4df6dbd313ff3421c330dfeaa755f/fbgemm_gpu_nightly_cpu-2024.11.16-cp311-cp311-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8bfe59a49f41c8c9c1a04e4ed8366a5ce07c3d847cf77fe61f11359256278788",
"md5": "c4d71823a6a416d62179dfd90b16f656",
"sha256": "fb69edd7efa61e59b5749789d34766b89fdab52f7c8aa48355b7c16a07f87e06"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "c4d71823a6a416d62179dfd90b16f656",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 3132912,
"upload_time": "2024-11-16T13:32:03",
"upload_time_iso_8601": "2024-11-16T13:32:03.213871Z",
"url": "https://files.pythonhosted.org/packages/8b/fe/59a49f41c8c9c1a04e4ed8366a5ce07c3d847cf77fe61f11359256278788/fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dfbafcca55bd265f35aadb7166c9124a81b5fe8c57160ec2409521f4f618c238",
"md5": "a349df92a60d98c184221d4420c5c04f",
"sha256": "17cd412ae6cd719fb3a840b0561e9091abfdb7f5b4db2e1b250dc96113ebe61a"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "a349df92a60d98c184221d4420c5c04f",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 4283107,
"upload_time": "2024-11-16T13:23:39",
"upload_time_iso_8601": "2024-11-16T13:23:39.620202Z",
"url": "https://files.pythonhosted.org/packages/df/ba/fcca55bd265f35aadb7166c9124a81b5fe8c57160ec2409521f4f618c238/fbgemm_gpu_nightly_cpu-2024.11.16-cp312-cp312-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fca9aab309cb3f793ca6856af65cf49dec9834b8310267dc495a743f1a0bb3fb",
"md5": "09b26c34f081eda9c0462e24b78ac5fb",
"sha256": "e8e1269e9db3b6140a5c53a0dd98bfd8bfe068f445326d03f02ba2590c10ebbd"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "09b26c34f081eda9c0462e24b78ac5fb",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 3132937,
"upload_time": "2024-11-16T13:31:52",
"upload_time_iso_8601": "2024-11-16T13:31:52.923378Z",
"url": "https://files.pythonhosted.org/packages/fc/a9/aab309cb3f793ca6856af65cf49dec9834b8310267dc495a743f1a0bb3fb/fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "24da7fbdcf379033e840f72eae53f40cecbdc6e6813ef72d21b7ea608e9ce4a9",
"md5": "e3661c59607cf4ce5fca218286a57d9d",
"sha256": "6a84368f86d1231cb606dae2efa9c47d38146b53836a658b712aa20ffdba4167"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "e3661c59607cf4ce5fca218286a57d9d",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 4283103,
"upload_time": "2024-11-16T13:23:48",
"upload_time_iso_8601": "2024-11-16T13:23:48.826751Z",
"url": "https://files.pythonhosted.org/packages/24/da/7fbdcf379033e840f72eae53f40cecbdc6e6813ef72d21b7ea608e9ce4a9/fbgemm_gpu_nightly_cpu-2024.11.16-cp39-cp39-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-16 13:32:34",
"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-cpu"
}
JSON
