nvgpu 0.5.1

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.

In addition we have a fancy table of GPU with more information taken by python binding to NVML.

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)

Print pretty colored table of devices, availability, users, processes:

$ nvgpu list
    status    type                 util.      temp.    MHz  users    since    pids    cmd
--  --------  -------------------  -------  -------  -----  -------  ---------------  ------  --------
 0  [ ]       GeForce GTX 1070      0 %          44    139
 1  [~]       GeForce GTX 1080 Ti   0 %          44    139  alice    2 days ago       19028   jupyter
 2  [~]       GeForce GTX 1080 Ti   0 %          44    139  bob      14 hours ago     8479    jupyter
 3  [~]       GeForce GTX 1070     46 %          54   1506  bob      7 days ago       20883   train.py
 4  [~]       GeForce GTX 1070     35 %          64   1480  bob      7 days ago       26228   evaluate.py
 5  [!]       GeForce GTX 1080 Ti   0 %          44    139  ?                         9305
 6  [ ]       GeForce GTX 1080 Ti   0 %          44    139

Or shortcut:

$ nvl

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'},
  ...
]

Author

• Bohumír Zámečník, Rossum, Ltd.

• License: MIT

TODO

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