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Look up GPU/CPU/RAM usage on multiple servers at the same time

Project description

Multiple smi

Look up GPU/CPU/RAM usage on multiple machines at the same time !

intended to work with python 3+

Based on pyNVML, and psutil.


  • Allows you to get nvidia-smi output and pustilinformation for multiple connected computers at once, and display it on a a selected GUI.
    • Availables frontends :
      • Ubuntu Appindicator
        • works best on Unity, partially supported on Gnome-shell
      • Argos
        • works on Gnome shell, but also on MacOS thanks to BitBar compatibility


  • Allows you to get a notification every time a new process is launched or finished. A default minimum of 1GB memory use is needed for the notification to appear.


  • This tool is aimed at small research teams, with multiple GPU-equipped computers, which you can manually ssh to. At a glance you can see every usage of your computer stock, and where you can launch your computation. It also provides some basis if you want to develop a tool to automatically launch your computation on the least busy computer of your network.


[sudo] python3 install [sudo] pip3 install multiple-smi

If using appindicator frontend or gnome notifier, it is advised to install it with system python, with which the gi package can be imported.

For server_smi it is also advised to install it with system python because then it will be available in sudo mode, needed to install the systemctl service.

Optional note for Ubuntu 18+ users

If you want to use the appindicator fronted, as unity is replaced by Gnome-shell, you need to enable appindicators on gnome-shell with gnome-tweak-tool, and you need to install python bindings for Appindicator3 :

sudo apt install gir1.2-appindicator3-0.1

Server side services installation

To allow clients to access your computer's smi stats, simply run server_smi

But you can also enable it as a service that will be launched at boot.

  • Ubuntu 16+ : A script is provided to automatically create the service file, whih will allow the server_smi to run automatically during boot (some options are available)
sudo install_server_service

to uninstall:

sudo install_server_service -u

(make the systemd folder specified is the same as during installation)

  • Ubuntu 14 : you have to daemonize the script and put it in init.d, you can do it with the provided script
sudo cp /etc/init.d/.
sudo chmod 0755 /etc/init.d/
sudo update-rc.d defaults

to uninstall:

sudo update-rc.d -f remove

to run the appindicator

client_smi --frontend {argos,appindicator} --notify-backend {gnome,ntfy}

to run the client_smi as only a CLI tool with no gui or notificaion:



To know which servers have a running server_smi in your local network, you can use the discover_hosts script, it will automatically populate a json file in ~/.client_smi/hosts_to_smi.json with found machines.


To add your own hosts manually, simply run a client_smi or discover_hosts once and add your entries in the json file that should be created here: ~/.client_smi/hosts_to_smi.json

Tunnel Connexion

Thanks to pyzmq backend for netork, a tunnel connexion is available, when you are outside your usual local network and have to go through a bastion.

Simply launch client_smi with --tunnel option set to your bastion address

client_smi --tunnel user@bastion_ip

Gpu usage stats:

Server-side, gpu usage history is stored in ~/.server_smi/{date}.csv if launched from CLI, /etc/server_smi/{date}.csv if launched from systemctl/init.d. Usage is written on it every ~60 sec, feel free to make some data science with it.

To enable it, you can use option -s in install_server_service or add it in (line 6) before installing

Project details

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Files for Multiple-smi, version 2.0.3
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Multiple_smi-2.0.3-py3-none-any.whl (25.1 kB) View hashes Wheel py3
Multiple smi-2.0.3.tar.gz (15.2 kB) View hashes Source None

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