# 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 12.4 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 12.4 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": "2025.1.18",
"project_urls": {
"Homepage": "https://github.com/pytorch/fbgemm"
},
"split_keywords": [
"pytorch",
" recommendation models",
" high performance computing",
" gpu",
" cuda"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "929bfe7c0bad68e4ebbc70a7b4e62c677dc3508cb66c203e88cd9d2e2d88ee8b",
"md5": "9d669e1b1ba24d26f0f3b0b2ef424c96",
"sha256": "26de9836dc7f6b416b470ecc0feb3dbe5bc51111e59afde3c233e458b99143d5"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp310-cp310-manylinux_2_28_aarch64.whl",
"has_sig": false,
"md5_digest": "9d669e1b1ba24d26f0f3b0b2ef424c96",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 2706704,
"upload_time": "2025-01-18T13:31:19",
"upload_time_iso_8601": "2025-01-18T13:31:19.862379Z",
"url": "https://files.pythonhosted.org/packages/92/9b/fe7c0bad68e4ebbc70a7b4e62c677dc3508cb66c203e88cd9d2e2d88ee8b/fbgemm_gpu_nightly_cpu-2025.1.18-cp310-cp310-manylinux_2_28_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a5d4317872834d9e6d09c2477a59d81e4f5d460e847bd5891274851ad3f05ab7",
"md5": "8c1ef0c80070ead6f9b73fecafd73a8b",
"sha256": "45fdd0bcaaaa30a4566879ff7ac5ac7dd3dd1b26eea4805d03b4fd7b79f472f7"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp310-cp310-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "8c1ef0c80070ead6f9b73fecafd73a8b",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 3817532,
"upload_time": "2025-01-18T13:23:08",
"upload_time_iso_8601": "2025-01-18T13:23:08.390845Z",
"url": "https://files.pythonhosted.org/packages/a5/d4/317872834d9e6d09c2477a59d81e4f5d460e847bd5891274851ad3f05ab7/fbgemm_gpu_nightly_cpu-2025.1.18-cp310-cp310-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8cd86d91c0318fe22ea74520b27b65fdff7f19c310fcaea0c2dbaac4361099b2",
"md5": "cbd5c08152efd5ef31b66d82bb5f3635",
"sha256": "07add645ba066d6d8c097abc25fc1ef755c52e10edd7d9f14799e7aa9238dafc"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp311-cp311-manylinux_2_28_aarch64.whl",
"has_sig": false,
"md5_digest": "cbd5c08152efd5ef31b66d82bb5f3635",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 2706719,
"upload_time": "2025-01-18T13:30:40",
"upload_time_iso_8601": "2025-01-18T13:30:40.004443Z",
"url": "https://files.pythonhosted.org/packages/8c/d8/6d91c0318fe22ea74520b27b65fdff7f19c310fcaea0c2dbaac4361099b2/fbgemm_gpu_nightly_cpu-2025.1.18-cp311-cp311-manylinux_2_28_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "201c4a920807c8d93418f781e8dbd677a04d535e8c6be4c71624e7e2b7385d76",
"md5": "c8e542fe69ad2f62c7e61621a4af2490",
"sha256": "60e5ecf20c67c00ccdcf54eb2b3d63830637c839fb243f69134c1df812793365"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp311-cp311-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "c8e542fe69ad2f62c7e61621a4af2490",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 3817544,
"upload_time": "2025-01-18T13:22:50",
"upload_time_iso_8601": "2025-01-18T13:22:50.269288Z",
"url": "https://files.pythonhosted.org/packages/20/1c/4a920807c8d93418f781e8dbd677a04d535e8c6be4c71624e7e2b7385d76/fbgemm_gpu_nightly_cpu-2025.1.18-cp311-cp311-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2849a84b7fff48eda076a6fdcc893db3b678590d78301e426a4a434ddf16c1d0",
"md5": "5608fd0aa881744ceb958bd428d2babc",
"sha256": "155130fa7b8d9043e39f315b9870e4df13b9e1a3e1bae37b0bfb993a83f8af82"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp312-cp312-manylinux_2_28_aarch64.whl",
"has_sig": false,
"md5_digest": "5608fd0aa881744ceb958bd428d2babc",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 2706723,
"upload_time": "2025-01-18T13:30:17",
"upload_time_iso_8601": "2025-01-18T13:30:17.258001Z",
"url": "https://files.pythonhosted.org/packages/28/49/a84b7fff48eda076a6fdcc893db3b678590d78301e426a4a434ddf16c1d0/fbgemm_gpu_nightly_cpu-2025.1.18-cp312-cp312-manylinux_2_28_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4c0f0c10dbd5d5452012978ea63709e05c0e62b6565759d894c03dd6bae8ffab",
"md5": "489827aeab1550f44849b9e3ef8dd4c7",
"sha256": "12b40f9122d9c0761aa8d92870e570d3882711e041d3591f965526f7c76c7818"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp312-cp312-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "489827aeab1550f44849b9e3ef8dd4c7",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 3817543,
"upload_time": "2025-01-18T13:22:28",
"upload_time_iso_8601": "2025-01-18T13:22:28.612972Z",
"url": "https://files.pythonhosted.org/packages/4c/0f/0c10dbd5d5452012978ea63709e05c0e62b6565759d894c03dd6bae8ffab/fbgemm_gpu_nightly_cpu-2025.1.18-cp312-cp312-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "84f4e91089724259ae9b99a29749d20015f7808152a6a1b6e1dfa961439f5403",
"md5": "1344062585837e5ae74d2780136e07a8",
"sha256": "d68ae7c81939d99befa2c98a857d56724cf5374c15794d222e52bdcd111fef42"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp313-cp313-manylinux_2_28_aarch64.whl",
"has_sig": false,
"md5_digest": "1344062585837e5ae74d2780136e07a8",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 2706720,
"upload_time": "2025-01-18T13:29:01",
"upload_time_iso_8601": "2025-01-18T13:29:01.198764Z",
"url": "https://files.pythonhosted.org/packages/84/f4/e91089724259ae9b99a29749d20015f7808152a6a1b6e1dfa961439f5403/fbgemm_gpu_nightly_cpu-2025.1.18-cp313-cp313-manylinux_2_28_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f13a53adfb55a23557f8a0bd1ef544ae979e6790d8f757ee41010ad8b87a29e1",
"md5": "678abf4194332ca2d5eefd90584c0cbf",
"sha256": "be8bddb418fda482ecea7caa738437517c4b65e62d77c4aae3d5418ca12bf8a2"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp313-cp313-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "678abf4194332ca2d5eefd90584c0cbf",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": null,
"size": 3817548,
"upload_time": "2025-01-18T13:22:41",
"upload_time_iso_8601": "2025-01-18T13:22:41.711680Z",
"url": "https://files.pythonhosted.org/packages/f1/3a/53adfb55a23557f8a0bd1ef544ae979e6790d8f757ee41010ad8b87a29e1/fbgemm_gpu_nightly_cpu-2025.1.18-cp313-cp313-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2a025f6daeecfaac442a32aa8888a68330be53b6e8abfe9f923b5898e969bc9b",
"md5": "9569a020fda24c48bc9d89e6239b95f4",
"sha256": "438c9d88ed53392597484c53e29f486ee4cb009671e7e9ef97e9f5528d7cc89c"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp39-cp39-manylinux_2_28_aarch64.whl",
"has_sig": false,
"md5_digest": "9569a020fda24c48bc9d89e6239b95f4",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 2706665,
"upload_time": "2025-01-18T13:31:17",
"upload_time_iso_8601": "2025-01-18T13:31:17.904549Z",
"url": "https://files.pythonhosted.org/packages/2a/02/5f6daeecfaac442a32aa8888a68330be53b6e8abfe9f923b5898e969bc9b/fbgemm_gpu_nightly_cpu-2025.1.18-cp39-cp39-manylinux_2_28_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c92dfe6cfdf2513cc6137af920fb8d1400a31c23e29fc19c4f5d0ea25bf258c3",
"md5": "8a73ee3ba808c263e256d69485757768",
"sha256": "ac8bc3b1256ab24500d5e1ba674fe0bad6471b7c5f6188be515873a62f5e8857"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly_cpu-2025.1.18-cp39-cp39-manylinux_2_28_x86_64.whl",
"has_sig": false,
"md5_digest": "8a73ee3ba808c263e256d69485757768",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 3817469,
"upload_time": "2025-01-18T13:23:28",
"upload_time_iso_8601": "2025-01-18T13:23:28.913151Z",
"url": "https://files.pythonhosted.org/packages/c9/2d/fe6cfdf2513cc6137af920fb8d1400a31c23e29fc19c4f5d0ea25bf258c3/fbgemm_gpu_nightly_cpu-2025.1.18-cp39-cp39-manylinux_2_28_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-18 13:31:19",
"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
