I'm trying to monitor a process that uses cuda and MPI, is there any way I could do this, something like the command "top" but that monitors the GPU too?

I find gpustat very useful. In can be installed with pip install gpustat, and prints breakdown of usage by processes or users.

To get real-time insight on used resources, do:

nvidia-smi -l 1

This will loop and call the view at every second.

If you do not want to keep past traces of the looped call in the console history, you can also do:

watch -n0.1 nvidia-smi

Where 0.1 is the time interval, in seconds.

I'm not aware of anything that combines this information, but you can use the nvidia-smi tool to get the raw data, like so (thanks to @jmsu for the tip on -l):

$ nvidia-smi -q -g 0 -d UTILIZATION -l

==============NVSMI LOG==============

Timestamp                       : Tue Nov 22 11:50:05 2011

Driver Version                  : 275.19

Attached GPUs                   : 2

GPU 0:1:0
    Utilization
        Gpu                     : 0 %
        Memory                  : 0 %

Download and install latest stable CUDA driver (4.2) from here. On linux, nVidia-smi 295.41 gives you just what you want. use nvidia-smi:

[root@localhost release]# nvidia-smi 
Wed Sep 26 23:16:16 2012       
+------------------------------------------------------+                       
| NVIDIA-SMI 3.295.41   Driver Version: 295.41         |                       
|-------------------------------+----------------------+----------------------+
| Nb.  Name                     | Bus Id        Disp.  | Volatile ECC SB / DB |
| Fan   Temp   Power Usage /Cap | Memory Usage         | GPU Util. Compute M. |
|===============================+======================+======================|
| 0.  Tesla C2050               | 0000:05:00.0  On     |         0          0 |
|  30%   62 C  P0    N/A /  N/A |   3%   70MB / 2687MB |   44%     Default    |
|-------------------------------+----------------------+----------------------|
| Compute processes:                                               GPU Memory |
|  GPU  PID     Process name                                       Usage      |
|=============================================================================|
|  0.  7336     ./align                                                 61MB  |
+-----------------------------------------------------------------------------+

EDIT: In latest NVIDIA drivers, this support is limited to Tesla Cards.

Deepak

Use argument "--query-compute-apps="

nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv

for further help, please follow

nvidia-smi --help-query-compute-app

Another useful monitoring approach is to use ps filtered on processes that consume your GPUs. I use this one a lot:

ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `lsof -n -w -t /dev/nvidia*`

That'll show all nvidia GPU-utilizing processes and some stats about them. lsof ... retrieves a list of all processes using an nvidia GPU owned by the current user, and ps -p ... shows ps results for those processes. ps f shows nice formatting for child/parent process relationships / hierarchies, and -o specifies a custom formatting. That one is similar to just doing ps u but adds the process group ID and removes some other fields.

One advantage of this over nvidia-smi is that it'll show process forks as well as main processes that use the GPU.

One disadvantage, though, is it's limited to processes owned by the user that executes the command. To open it up to all processes owned by any user, I add a sudo before the lsof.

Lastly, I combine it with watch to get a continuous update. So, in the end, it looks like:

watch -n 0.1 'ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `sudo lsof -n -w -t /dev/nvidia*`'

Which has output like:

Every 0.1s: ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `sudo lsof -n -w -t /dev/nvi...  Mon Jun  6 14:03:20 2016
USER      PGRP   PID %CPU %MEM  STARTED     TIME COMMAND
grisait+ 27294 50934  0.0  0.1   Jun 02 00:01:40 /opt/google/chrome/chrome --type=gpu-process --channel=50877.0.2015482623
grisait+ 27294 50941  0.0  0.0   Jun 02 00:00:00  \_ /opt/google/chrome/chrome --type=gpu-broker
grisait+ 53596 53596 36.6  1.1 13:47:06 00:05:57 python -u process_examples.py
grisait+ 53596 33428  6.9  0.5 14:02:09 00:00:04  \_ python -u process_examples.py
grisait+ 53596 33773  7.5  0.5 14:02:19 00:00:04  \_ python -u process_examples.py
grisait+ 53596 34174  5.0  0.5 14:02:30 00:00:02  \_ python -u process_examples.py
grisait+ 28205 28205  905  1.5 13:30:39 04:56:09 python -u train.py
grisait+ 28205 28387  5.8  0.4 13:30:49 00:01:53  \_ python -u train.py
grisait+ 28205 28388  5.3  0.4 13:30:49 00:01:45  \_ python -u train.py
grisait+ 28205 28389  4.5  0.4 13:30:49 00:01:29  \_ python -u train.py
grisait+ 28205 28390  4.5  0.4 13:30:49 00:01:28  \_ python -u train.py
grisait+ 28205 28391  4.8  0.4 13:30:49 00:01:34  \_ python -u train.py

Just use watch nvidia-smi, it will output the message by 2s interval in default.

For example, as the below image:

You can also use watch -n 5 nvidia-smi (-n 5 by 5s interval).

This may not be elegant, but you can try

while true; do sleep 2; nvidia-smi; done

I also tried the method by @Edric, which works, but I prefer the original layout of nvidia-smi.

You can try nvtop, which is similar to the widely-used htop tool but for NVIDIA GPUs. Here is a screenshot of nvtop of it in action.

There is Prometheus GPU Metrics Exporter (PGME) that leverages the nvidai-smi binary. You may try this out. Once you have the exporter running, you can access it via http://localhost:9101/metrics. For two GPUs, the sample result looks like this:

temperature_gpu{gpu="TITAN X (Pascal)[0]"} 41
utilization_gpu{gpu="TITAN X (Pascal)[0]"} 0
utilization_memory{gpu="TITAN X (Pascal)[0]"} 0
memory_total{gpu="TITAN X (Pascal)[0]"} 12189
memory_free{gpu="TITAN X (Pascal)[0]"} 12189
memory_used{gpu="TITAN X (Pascal)[0]"} 0
temperature_gpu{gpu="TITAN X (Pascal)[1]"} 78
utilization_gpu{gpu="TITAN X (Pascal)[1]"} 95
utilization_memory{gpu="TITAN X (Pascal)[1]"} 59
memory_total{gpu="TITAN X (Pascal)[1]"} 12189
memory_free{gpu="TITAN X (Pascal)[1]"} 1738
memory_used{gpu="TITAN X (Pascal)[1]"} 10451

you can use nvidia-smi pmon -i 0 to monitor every process in GPU 0. including compute mode, sm usage, memory usage, encoder usage, decoder usage.

You can use the monitoring program glances with its GPU monitoring plug-in:

open source
to install: sudo apt-get install -y python-pip; sudo pip install glances[gpu]
to launch: sudo glances

It also monitors the CPU, disk IO, disk space, network, and a few other things:

I created a batch file with the following code in a windows machine to monitor every second. It works for me.

:loop
cls
"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi"
timeout /T 1
goto loop

nvidia-smi exe is usually located in "C:\Program Files\NVIDIA Corporation" if you want to run the command only once.

来源：https://stackoverflow.com/questions/8223811/top-command-for-gpus-using-cuda

标签

cuda

resource-monitor

top command for GPU's using CUDA

To get real-time insight on used resources, do: