nvidia-htop

A tool for enriching the output of nvidia-smi.

Install

pip3 install nvidia-htop

Yes, this tool has been on PyPi since 2021! Enjoy the super-easy way to install it.

Usage

nvidia-htop.py [-l [length]]
  print GPU utilization with usernames and CPU stats for each GPU-utilizing process

  -l|--command-length [length]     Print longer part of the commandline. If `length'
                                   is provided, use it as the commandline length,
                                   otherwise print first 100 characters.
  -c|--color                       Colorize the output (green - free GPU, yellow -
                                   moderately used GPU, red - fully used GPU)
  -u|--user USER[,USER]            Limit the list of processes to selected users
                                   (comma-separated).
  -i|--id ID[,ID[,ID]]             Limit the command to selected GPU IDs (comma-separated).
  -m|--meter                       Shows meters for GPU and VRAM utilization.

Note: for backward compatibility, nvidia-smi | nvidia-htop.py [-l [length]] is also supported.

Note: running inside a container (docker, singularity, ...), nvidia-smi can only see processes running in the container.

Note: To periodically check the output of nvidia-htop, use the watch utility: watch nvidia-htop.py. To get colored output, you have to pass option -c to both watch and nvidia-htop, e.g. watch -c nvidia-htop.py -c.

Example output

$ nvidia-htop.py -l
Mon May 21 15:06:35 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.25                 Driver Version: 390.25                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 108...  Off  | 00000000:04:00.0 Off |                  N/A |
| 53%   75C    P2   174W / 250W |  10807MiB / 11178MiB |     97%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX 108...  Off  | 00000000:05:00.0 Off |                  N/A |
| 66%   82C    P2   220W / 250W |  10783MiB / 11178MiB |    100%      Default |
+-------------------------------+----------------------+----------------------+
|   2  GeForce GTX 108...  Off  | 00000000:08:00.0 Off |                  N/A |
| 45%   67C    P2    85W / 250W |  10793MiB / 11178MiB |     51%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
|  GPU   PID     USER    GPU MEM  %MEM  %CPU  COMMAND                                                                                               |
|    0  1032 anonymou   10781MiB   308   3.7  python train_image_classifier.py --train_dir=/mnt/xxxxxxxx/xxxxxxxx/xxxxxxxx/xxxxxxx/xxxxxxxxxxxxxxx  |
|    1 11021 cannotte   10765MiB   114   1.5  python3 ./train.py --flagfile /xxxxxxxx/xxxxxxxx/xxxxxxxx/xxxxxxxxx/xx/xxxxxxxxxxxxxxx                |
|    2 25544 nevermin   10775MiB   108   2.0  python -m xxxxxxxxxxxxxxxxxxxxxxxxxxxxx                                                               |
+-----------------------------------------------------------------------------+

Screenshot with output coloring