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Project description

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.


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

# ['0', '2']

[{'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'},



  • order GPUs by priority (decreasing power, decreasing free memory)

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