A tool for enriching the output of nvidia-smi
git clone https://github.com/Kashu7100/nvidia-htop.git
cd nvidia-htop
python setup.py install
nvhtop [-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)
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 nividia-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.
$ nvhtop -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 |
+-----------------------------------------------------------------------------+
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