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Remote Inria
Code here, run there !

A CLI tool for remotely interacting with Inria computing resources.

GitLab repository (source code)

What is remi ?

remi is a tool aiming at easing the workflow of Inria researchers when it comes to performing computations remotely. More precisely, remi is configured for each project you might want to use it for.
Once your preferences are set, you can run your code either on your desktop at Inria or on one the cluster nodes.

If you are tired of messing up with 4-line oarsub commands or if you find yourself committing and pushing your code each time you want to test it remotely, this tool is for you !

Note: Even though this tool has been made to work from a personal computer different from your Inria desktop, it also totally works when running directly from the Inria desktop.
Most of the remi features are still relevant in this case.

Presentation / tutorial video:

Main features

  • Synchronization: remi creates a remote clone of your project on the Inria storage space (under /scratch) and lets you synchronize it easily.

  • Remote execution: The core idea behind remi is to provide an easy way to execute code on remote computers (regular Inria desktops or cluster servers).

  • Clusters and singularity support: The complex way to request computing resources is integrated in remi to minimize overhead. Singularity container management is also embedded (build and use).

  • Jupyter notebook support: This tool lets you run a jupyter notebook server on your Inria workstation and connect to it locally on the browser.

Website content


Any help to the improvement of remi is more than welcome.
You may write an issue to warn me of any bug or desired feature by writing an issue.


This project was inspired from LabML Remote.
The latter does not support ssh through a bastion because it uses the paramiko library. remi, on the other hand, uses traditional calls to ssh command.
The main motivation to start a separate project was to design a tool specifically with the Inria computing needs in mind (use of Inria clusters, natively using the available resources etc).