aiacc-nccl-cu11


Nameaiacc-nccl-cu11 JSON
Version 2.17.1 PyPI version JSON
download
home_pagehttps://www.aliyun.com
SummaryAIACC-NCCL is an AI-Accelerator communication framework for NVIDIA-NCCL. It implements optimized all-reduce, all-gather, reduce, broadcast, reduce-scatter, all-to-all,as well as any send/receive based communication pattern.It has been optimized to achieve high bandwidth on aliyun machines using PCIe, NVLink, NVswitch,as well as networking using InfiniBand Verbs, eRDMA or TCP/IP sockets.
upload_time2023-09-05 07:48:40
maintainer
docs_urlNone
authorAlibaba Cloud
requires_python
licenseCopyright (C) Alibaba Group Holding Limited
keywords distributed deep learning communication nccl aiacc
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            AIACC-2.0 ACSpeed means AIACC communication Compiler Speeding. 
AIACC-2.0 AGSpeed means AIACC compute Graph SPEEDing. 
This is a distributed training framework plugin for PyTorch and aiacc nccl communication plugin for many deeplearning framworks including TensorFlow, PyTorch, MXNet and Caffe.
This project is to create a uniform distributed training framework plugin tool for major frameworks,
and make the distributed training as easy as possible and as fast as possible on Alibaba Cloud.



            

Raw data

            {
    "_id": null,
    "home_page": "https://www.aliyun.com",
    "name": "aiacc-nccl-cu11",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "Distributed,Deep Learning,Communication,NCCL,AIACC",
    "author": "Alibaba Cloud",
    "author_email": "ziqi.yzq@alibaba-inc.com",
    "download_url": "",
    "platform": null,
    "description": "AIACC-2.0 ACSpeed means AIACC communication Compiler Speeding. \nAIACC-2.0 AGSpeed means AIACC compute Graph SPEEDing. \nThis is a distributed training framework plugin for PyTorch and aiacc nccl communication plugin for many deeplearning framworks including TensorFlow, PyTorch, MXNet and Caffe.\nThis project is to create a uniform distributed training framework plugin tool for major frameworks,\nand make the distributed training as easy as possible and as fast as possible on Alibaba Cloud.\n\n\n",
    "bugtrack_url": null,
    "license": "Copyright (C) Alibaba Group Holding Limited",
    "summary": "AIACC-NCCL is an AI-Accelerator communication framework for NVIDIA-NCCL. It implements optimized all-reduce, all-gather, reduce, broadcast, reduce-scatter, all-to-all,as well as any send/receive based communication pattern.It has been optimized to achieve high bandwidth on aliyun machines using PCIe, NVLink, NVswitch,as well as networking using InfiniBand Verbs, eRDMA or TCP/IP sockets.",
    "version": "2.17.1",
    "project_urls": {
        "Homepage": "https://www.aliyun.com"
    },
    "split_keywords": [
        "distributed",
        "deep learning",
        "communication",
        "nccl",
        "aiacc"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "43a4c53033ffa0b5f63b9beaa0ece5e5eb3044a611e4ba3a14d158dc36155aa9",
                "md5": "4bf36a89ef440a1d6bf6ccfe7fc1f790",
                "sha256": "6a396db05b3e770a83f0012af84ff9431e5887cca731e25223d13a9fb10098b7"
            },
            "downloads": -1,
            "filename": "aiacc_nccl_cu11-2.17.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "4bf36a89ef440a1d6bf6ccfe7fc1f790",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 1796,
            "upload_time": "2023-09-05T07:48:40",
            "upload_time_iso_8601": "2023-09-05T07:48:40.701354Z",
            "url": "https://files.pythonhosted.org/packages/43/a4/c53033ffa0b5f63b9beaa0ece5e5eb3044a611e4ba3a14d158dc36155aa9/aiacc_nccl_cu11-2.17.1-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-09-05 07:48:40",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "aiacc-nccl-cu11"
}
        
Elapsed time: 0.10854s