fbgemm-gpu-nightly


Namefbgemm-gpu-nightly JSON
Version 2025.7.8 PyPI version JSON
download
home_pagehttps://github.com/pytorch/fbgemm
SummaryNone
upload_time2025-07-08 15:16:49
maintainerNone
docs_urlNone
authorFBGEMM Team
requires_pythonNone
licenseBSD-3
keywords pytorch recommendation models high performance computing gpu cuda
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # FBGEMM_GPU

[![FBGEMM_GPU-CPU CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml/badge.svg)](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml)
[![FBGEMM_GPU-CUDA CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml/badge.svg)](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml)
[![FBGEMM_GPU-ROCm CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_rocm.yml/badge.svg)](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-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[![FBGEMM_GPU-CPU CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml/badge.svg)](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cpu.yml)\n[![FBGEMM_GPU-CUDA CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml/badge.svg)](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_cuda.yml)\n[![FBGEMM_GPU-ROCm CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_gpu_ci_rocm.yml/badge.svg)](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": "2025.7.8",
    "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": "6f481a2821c341d1922a4b81d542575990774a22a8cf4149ecb640adc39d01dd",
                "md5": "c786c54f9f7b36bf2be5755301672ec8",
                "sha256": "7d88fa8d33a35dba33b474b2607ec83a8b1cf390e479bf906e2742386e2dea1e"
            },
            "downloads": -1,
            "filename": "fbgemm_gpu_nightly-2025.7.8-cp310-cp310-manylinux_2_28_x86_64.whl",
            "has_sig": false,
            "md5_digest": "c786c54f9f7b36bf2be5755301672ec8",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": null,
            "size": 403234964,
            "upload_time": "2025-07-08T15:16:49",
            "upload_time_iso_8601": "2025-07-08T15:16:49.882081Z",
            "url": "https://files.pythonhosted.org/packages/6f/48/1a2821c341d1922a4b81d542575990774a22a8cf4149ecb640adc39d01dd/fbgemm_gpu_nightly-2025.7.8-cp310-cp310-manylinux_2_28_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "48aa8b9f33edd8b62db026b8ecfa7cc863803f21b75362c3c5770f24947a4513",
                "md5": "98c934b1d4f72a92283268841e78f971",
                "sha256": "0e9188d7dc2536bec1869ec17a5e9dbaa190d6e70824198f638b3e82c766b3f2"
            },
            "downloads": -1,
            "filename": "fbgemm_gpu_nightly-2025.7.8-cp311-cp311-manylinux_2_28_x86_64.whl",
            "has_sig": false,
            "md5_digest": "98c934b1d4f72a92283268841e78f971",
            "packagetype": "bdist_wheel",
            "python_version": "cp311",
            "requires_python": null,
            "size": 403234427,
            "upload_time": "2025-07-08T15:17:30",
            "upload_time_iso_8601": "2025-07-08T15:17:30.405262Z",
            "url": "https://files.pythonhosted.org/packages/48/aa/8b9f33edd8b62db026b8ecfa7cc863803f21b75362c3c5770f24947a4513/fbgemm_gpu_nightly-2025.7.8-cp311-cp311-manylinux_2_28_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "cc131233bd9bf7a42cf0b8b5c93ff0dab4827ef36f0400c875d2a4f2f509059c",
                "md5": "234c20a62fc5ce8fc382fd48e2f3fa4f",
                "sha256": "50eae95f0c9ccc53bd0d6b911672d485818fd320a31df37171c2f6d42a70ecf8"
            },
            "downloads": -1,
            "filename": "fbgemm_gpu_nightly-2025.7.8-cp312-cp312-manylinux_2_28_x86_64.whl",
            "has_sig": false,
            "md5_digest": "234c20a62fc5ce8fc382fd48e2f3fa4f",
            "packagetype": "bdist_wheel",
            "python_version": "cp312",
            "requires_python": null,
            "size": 408292417,
            "upload_time": "2025-07-08T15:17:27",
            "upload_time_iso_8601": "2025-07-08T15:17:27.597342Z",
            "url": "https://files.pythonhosted.org/packages/cc/13/1233bd9bf7a42cf0b8b5c93ff0dab4827ef36f0400c875d2a4f2f509059c/fbgemm_gpu_nightly-2025.7.8-cp312-cp312-manylinux_2_28_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "2cbe3b46453c7b11ae3ccf6702b980c97d154413eaf527b57780fd625d8ab029",
                "md5": "fe8bc0cba127e01d623f7eadf3ebd1a1",
                "sha256": "42efdea115bd2684b41c928716478a1fc25a48b393fa53353ac46ab924cc7628"
            },
            "downloads": -1,
            "filename": "fbgemm_gpu_nightly-2025.7.8-cp313-cp313-manylinux_2_28_x86_64.whl",
            "has_sig": false,
            "md5_digest": "fe8bc0cba127e01d623f7eadf3ebd1a1",
            "packagetype": "bdist_wheel",
            "python_version": "cp313",
            "requires_python": null,
            "size": 403235633,
            "upload_time": "2025-07-08T15:17:53",
            "upload_time_iso_8601": "2025-07-08T15:17:53.318166Z",
            "url": "https://files.pythonhosted.org/packages/2c/be/3b46453c7b11ae3ccf6702b980c97d154413eaf527b57780fd625d8ab029/fbgemm_gpu_nightly-2025.7.8-cp313-cp313-manylinux_2_28_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": null,
            "digests": {
                "blake2b_256": "58dd9c8407da337148ee250a86da96f5fb3c5c3cfd8a2401e2dd66898d90a3a8",
                "md5": "11b1a74a603d976ea449140fc3cd4774",
                "sha256": "51ddf60f0a5c47c6594ad509649338e5d51b01ab72249680cfe46dac526da166"
            },
            "downloads": -1,
            "filename": "fbgemm_gpu_nightly-2025.7.8-cp39-cp39-manylinux_2_28_x86_64.whl",
            "has_sig": false,
            "md5_digest": "11b1a74a603d976ea449140fc3cd4774",
            "packagetype": "bdist_wheel",
            "python_version": "cp39",
            "requires_python": null,
            "size": 408296029,
            "upload_time": "2025-07-08T15:21:08",
            "upload_time_iso_8601": "2025-07-08T15:21:08.533715Z",
            "url": "https://files.pythonhosted.org/packages/58/dd/9c8407da337148ee250a86da96f5fb3c5c3cfd8a2401e2dd66898d90a3a8/fbgemm_gpu_nightly-2025.7.8-cp39-cp39-manylinux_2_28_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-07-08 15:16:49",
    "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"
}
        
Elapsed time: 0.43934s