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        "author": "Bohumir Zamecnik, Rossum",
        "author_email": "bohumir.zamecnik@rossum.ai",
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        "description": "``nvgpu`` - NVIDIA GPU tools\n============================\n\nIt provides information about GPUs and their availability for\ncomputation.\n\nOften we want to train a ML model on one of GPUs installed on a\nmulti-GPU machine. Since TensorFlow allocates all memory, only one such\nprocess can use the GPU at a time. Unfortunately ``nvidia-smi`` provides\nonly a text interface with information about GPUs. This packages wraps\nit with an easier to use CLI and Python interface.\n\nIt's a quick and dirty solution calling ``nvidia-smi`` and parsing its\noutput. We can take one or more GPUs availabile for computation based on\nrelative memory usage, ie. it is OK with Xorg taking a few MB.\n\nIn addition we have a fancy table of GPU with more information taken by\npython binding to NVML.\n\nFor easier monitoring of multiple machines it's possible to deploy\nagents (that provide the GPU information in JSON over a REST API) and\nshow the aggregated status in a web application.\n\nInstalling\n----------\n\nFor a user:\n\n.. code:: bash\n\n    pip install -U nvgpu\n\nor to the system:\n\n.. code:: bash\n\n    sudo -H pip install -U nvgpu\n\nUsage examples\n--------------\n\nCommand-line interface:\n\n.. code:: bash\n\n    # grab all available GPUs\n    CUDA_VISIBLE_DEVICES=$(nvgpu available)\n\n    # grab at most available GPU\n    CUDA_VISIBLE_DEVICES=$(nvgpu available -l 1)\n\nPrint pretty colored table of devices, availability, users, processes:\n\n::\n\n    $ nvgpu list\n        status    type                 util.      temp.    MHz  users    since    pids    cmd\n    --  --------  -------------------  -------  -------  -----  -------  ---------------  ------  --------\n     0  [ ]       GeForce GTX 1070      0 %          44    139                          \n     1  [~]       GeForce GTX 1080 Ti   0 %          44    139  alice    2 days ago       19028   jupyter\n     2  [~]       GeForce GTX 1080 Ti   0 %          44    139  bob      14 hours ago     8479    jupyter\n     3  [~]       GeForce GTX 1070     46 %          54   1506  bob      7 days ago       20883   train.py\n     4  [~]       GeForce GTX 1070     35 %          64   1480  bob      7 days ago       26228   evaluate.py\n     5  [!]       GeForce GTX 1080 Ti   0 %          44    139  ?                         9305\n     6  [ ]       GeForce GTX 1080 Ti   0 %          44    139\n\nOr shortcut:\n\n::\n\n    $ nvl\n\nPython API:\n\n.. code:: python\n\n    import nvgpu\n\n    nvgpu.available_gpus()\n    # ['0', '2']\n\n    nvgpu.gpu_info()\n    [{'index': '0',\n      'mem_total': 8119,\n      'mem_used': 7881,\n      'mem_used_percent': 97.06860450794433,\n      'type': 'GeForce GTX 1070',\n      'uuid': 'GPU-3aa99ee6-4a9f-470e-3798-70aaed942689'},\n     {'index': '1',\n      'mem_total': 11178,\n      'mem_used': 10795,\n      'mem_used_percent': 96.57362676686348,\n      'type': 'GeForce GTX 1080 Ti',\n      'uuid': 'GPU-60410ded-5218-7b06-9c7a-124b77a22447'},\n     {'index': '2',\n      'mem_total': 11178,\n      'mem_used': 10789,\n      'mem_used_percent': 96.51994990159241,\n      'type': 'GeForce GTX 1080 Ti',\n      'uuid': 'GPU-d0a77bd4-cc70-ca82-54d6-4e2018cfdca6'},\n      ...\n    ]\n\nWeb application with agents\n---------------------------\n\nThere are multiple nodes. Agents take info from GPU and provide it in\nJSON via REST API. Master gathers info from other nodes and displays it\nin a HTML page. Agents can also display their status by default.\n\nAgent\n~~~~~\n\n.. code:: bash\n\n    FLASK_APP=nvgpu.webapp flask run --host 0.0.0.0 --port 1080\n\nMaster\n~~~~~~\n\nSet agents into a config file. Agent is specified either via a URL to a\nremote machine or ``'self'`` for direct access to local machine. Remove\n``'self'`` if the machine itself does not have any GPU. Default is\n``AGENTS = ['self']``, so that agents also display their own status. Set\n``AGENTS = []`` to avoid this.\n\n::\n\n    # nvgpu_master.cfg\n    AGENTS = [\n             'self', # node01 - master - direct access without using HTTP\n             'http://node02:1080',\n             'http://node03:1080',\n             'http://node04:1080',\n    ]\n\n.. code:: bash\n\n    NVGPU_CLUSTER_CFG=/path/to/nvgpu_master.cfg FLASK_APP=nvgpu.webapp flask run --host 0.0.0.0 --port 1080\n\nOpen the master in the web browser: http://node01:1080.\n\nInstalling as a service\n-----------------------\n\nOn Ubuntu with ``systemd`` we can install the agents/master as as\nservice to be ran automatically on system start.\n\n.. code:: bash\n\n    # create an unprivileged system user\n    sudo useradd -r nvgpu\n\nCopy `nvgpu-agent.service <nvgpu-agent.service>`__ to:\n\n.. code:: bash\n\n    sudo vi /etc/systemd/system/nvgpu-agent.service\n\nSet agents to the configuration file for the master:\n\n.. code:: bash\n\n    sudo vi /etc/nvgpu.conf\n\n.. code:: python\n\n    AGENTS = [\n             # direct access without using HTTP\n             'self',\n             'http://node01:1080',\n             'http://node02:1080',\n             'http://node03:1080',\n             'http://node04:1080',\n    ]\n\nSet up and start the service:\n\n.. code:: bash\n\n    # enable for automatic startup at boot\n    sudo systemctl enable nvgpu-agent.service\n    # start\n    sudo systemctl start nvgpu-agent.service \n    # check the status\n    sudo systemctl status nvgpu-agent.service\n\n.. code:: bash\n\n    # check the service\n    open http://localhost:1080\n\nAuthor\n------\n\n-  Bohum\u00edr Z\u00e1me\u010dn\u00edk, `Rossum, Ltd. <https://rossum.ai/>`__\n-  License: MIT\n\nTODO\n----\n\n-  order GPUs by priority (decreasing power, decreasing free memory)\n\n\n",
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