# 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",
"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": "2024.12.18",
"project_urls": {
"Homepage": "https://github.com/pytorch/fbgemm"
},
"split_keywords": [
"pytorch",
" recommendation models",
" high performance computing",
" gpu",
" cuda"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "155e49e8095df34c4b5c1841fe1cceb79ddd4392fd8152f43adfa5b220a3adab",
"md5": "f69a63156c1dbd25d1881f9265094f4a",
"sha256": "39af5dd72a37bee722ec098178a59387e27bd93ab2f422e6ac9b8f68d519f727"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2024.12.18-cp310-cp310-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "f69a63156c1dbd25d1881f9265094f4a",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": null,
"size": 406973731,
"upload_time": "2024-12-18T14:16:09",
"upload_time_iso_8601": "2024-12-18T14:16:09.424576Z",
"url": "https://files.pythonhosted.org/packages/15/5e/49e8095df34c4b5c1841fe1cceb79ddd4392fd8152f43adfa5b220a3adab/fbgemm_gpu_nightly-2024.12.18-cp310-cp310-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e6781156858c76a18e3432af9950473ae6cef178c61aacdb6fc45d8b7f257141",
"md5": "a908c284277f5129b7e13ea5bedf5ccb",
"sha256": "08233c55d3ec166ff67f4b7813e71c80933a612750833d59345d24a5df9cb52f"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2024.12.18-cp311-cp311-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "a908c284277f5129b7e13ea5bedf5ccb",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": null,
"size": 406974279,
"upload_time": "2024-12-18T14:15:43",
"upload_time_iso_8601": "2024-12-18T14:15:43.334358Z",
"url": "https://files.pythonhosted.org/packages/e6/78/1156858c76a18e3432af9950473ae6cef178c61aacdb6fc45d8b7f257141/fbgemm_gpu_nightly-2024.12.18-cp311-cp311-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f067f9cdb0a53c8433c2300951bfca7b4e1f97d4b07a1e150835bb2118ccb751",
"md5": "f50b8117ada68840f1dd118342701cc4",
"sha256": "1a6799d58a1bc02e618d9e8533cf7fb33ea22763470ce7b6a6c759ca0728f1a6"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2024.12.18-cp312-cp312-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "f50b8117ada68840f1dd118342701cc4",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": null,
"size": 406974106,
"upload_time": "2024-12-18T14:13:54",
"upload_time_iso_8601": "2024-12-18T14:13:54.448083Z",
"url": "https://files.pythonhosted.org/packages/f0/67/f9cdb0a53c8433c2300951bfca7b4e1f97d4b07a1e150835bb2118ccb751/fbgemm_gpu_nightly-2024.12.18-cp312-cp312-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "61cfa6f244985e0dfd1959137a8da1d06c218719b6a2ee359308a9ce535c55f7",
"md5": "123b89adad5d441c1e04ec4f52a07c00",
"sha256": "a8dc584d57dd7f2eb0777d4adfa691b7946e40364a3433306bc42d9b40b91851"
},
"downloads": -1,
"filename": "fbgemm_gpu_nightly-2024.12.18-cp39-cp39-manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "123b89adad5d441c1e04ec4f52a07c00",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": null,
"size": 406974839,
"upload_time": "2024-12-18T14:15:13",
"upload_time_iso_8601": "2024-12-18T14:15:13.807048Z",
"url": "https://files.pythonhosted.org/packages/61/cf/a6f244985e0dfd1959137a8da1d06c218719b6a2ee359308a9ce535c55f7/fbgemm_gpu_nightly-2024.12.18-cp39-cp39-manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-18 14:16:09",
"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"
}
JSON
