Metadata-Version: 2.0
Name: nvgpu
Version: 0.1
Summary: NVIDIA GPU tools
Home-page: https://github.com/rossumai/nvgpu
Author: Bohumir Zamecnik, Rossum
Author-email: bohumir.zamecnik@rossum.ai
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX :: Linux

``nvgpu`` - NVIDIA GPU tools
============================

It provides information about GPUs and their availability for
computation.

Often we want to train a ML model on one of GPUs installed on a
multi-GPU machine. Since TensorFlow allocates all memory, only one such
process can use the GPU at a time. Unfortunately ``nvidia-smi`` provides
only a text interface with information about GPUs. This packages wraps
it with an easier to use CLI and Python interface.

It’s a quick and dirty solution calling ``nvidia-smi`` and parsing its
output. We can take one or more GPUs availabile for computation based on
relative memory usage, ie. it is OK with Xorg taking a few MB.

Installing
----------

::

    pip install nvgpu

Usage examples
--------------

Command-line interface:

::

    # grab all available GPUs
    CUDA_VISIBLE_DEVICES=$(nvgpu available)

    # grab at most available GPU
    CUDA_VISIBLE_DEVICES=$(nvgpu available -l 1)

Python API:

::

    import nvgpu

    nvgpu.available_gpus()
    # ['0', '2']

    nvgpu.gpu_info()
    [{'index': '0',
      'mem_total': 8119,
      'mem_used': 7881,
      'mem_used_percent': 97.06860450794433,
      'type': 'GeForce GTX 1070',
      'uuid': 'GPU-3aa99ee6-4a9f-470e-3798-70aaed942689'},
     {'index': '1',
      'mem_total': 11178,
      'mem_used': 10795,
      'mem_used_percent': 96.57362676686348,
      'type': 'GeForce GTX 1080 Ti',
      'uuid': 'GPU-60410ded-5218-7b06-9c7a-124b77a22447'},
     {'index': '2',
      'mem_total': 11178,
      'mem_used': 10789,
      'mem_used_percent': 96.51994990159241,
      'type': 'GeForce GTX 1080 Ti',
      'uuid': 'GPU-d0a77bd4-cc70-ca82-54d6-4e2018cfdca6'},
      ...
    ]

TODO: - order GPUs by priority (decreasing power, decreasing free
memory)


