RStudio

RStudio is an integrated development environment (IDE) for R and Python. It includes a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging, and workspace management. This app will allow you to run an RStudio server in a SLURM job and access it directly from your browser.

Before continuing, please first follow the guide to getting access to the OpenOndemand web portal.

Starting the app

Start by selecting the desired R version and the amount of resources that you expect to use and for how long:

If you need to use a node with specific features, for example if you need some fast and local scratch space, or otherwise need to pass any additional options to the Slurm sbatch command used to launch the job, you can enter them in the "additional job options" field. Then click Launch!

Accessing the app

When you've clicked Launch SLURM will immediately start finding a compute node with the requested amount of resources available, and you will see a Queued status. When the chosen compute node partition is not fully allocated this usually only takes a few seconds, however if it takes longer, you can check the job status and the reason why it's pending under the Jobs menu, or by using shell commands.

When the job has been granted a resource allocation the server needs a little while to start and you will see the status change to Starting

and when it finishes a button will appear to launch RStudio:

You can now start working:

Closing the window

If you close the window or browser tab while something is running, it will continue to run in the background. You can always reconnect to it by clicking the Connect to RStudio Server button again.

Stopping the app

When you are done with your work, it's important to stop the app to free up resources for other users. Before you do that, however, it's a good idea to first click the little red button inside RStudio in the top right corner to shut down the R session gracefully:

Then stop the job by clicking the red Cancel button under My Interactive Sessions, see the screenshots above.

Always inspect and optimize efficiency for next time!

When the job completes, !!!ALWAYS!!! inspect the CPU and memory usage of the job in either the notification email received or using these commands and adjust the next job accordingly! This is essential to avoid wasting resources which other people could used, and to reduce queue time.

Containerization

The RStudio server runs in the SLURM job from within a singularity/apptainer container, that is based on Rocker container images. This means that R packages installed from the RStudio app may not work with other R installations due to different base operating system packages, so the RStudio app uses a different R library location by default, which is located under $HOME/R/rstudio-server/R_MAJOR_VERSION.

Furthermore, because RStudio is running in an isolated Linux container, you cannot for example load your usual conda environments or issue SLURM commands from the integrated terminal, etc. Only the Ceph network storage mount points are available at their usual locations, but everything else is otherwise completely isolated from the host on which the container runs.