RStudio Server¶

RStudio Server provides a web-based IDE for R, running on a compute node and accessible through your local browser. You get the full RStudio experience with access to cluster resources, your files, and all installed R packages.

Step 1: Submit the batch script¶

Save the following as rstudio.slurm:

#!/bin/bash
#SBATCH --job-name=rstudio
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G
#SBATCH --time=08:00:00
#SBATCH --qos=regular
#SBATCH --output=rstudio-%j.out
#SBATCH --error=rstudio-%j.err

module load RStudio-Server
start_rstudio_server.sh

Submit it:

sbatch rstudio.slurm

If you are not familiar with the #SBATCH flags, the SLURM reference explains each directive in detail. Adjust --cpus-per-task, --mem, and --time to fit your needs.

Step 2: Check the log¶

Wait for the job to start running:

squeue -u $USER

Once the state changes to R (running), read the output file:

cat rstudio-<JOBID>.out

You will see something like this:

================================================================================
                         RSTUDIO SERVER SESSION INFO
================================================================================

  Job ID:       1234567
  Node:         hyperion-107.sw.ehu.es
  Port:         8945
  Username:     <username>
  Password:     xK9mPq2wL5nR

--------------------------------------------------------------------------------
  CONNECTION INSTRUCTIONS
--------------------------------------------------------------------------------

  1. Open a terminal on your local machine and run:

     ssh -N -L 9999:localhost:8945 -J <username>@hyperion.sw.ehu.es hyperion-107

  2. Open your web browser and navigate to:

     http://localhost:9999

  3. Log in with the credentials above.

================================================================================

The log gives you everything you need: the tunnel command, the URL, the username, and the password.

Step 3: Create the SSH tunnel¶

On your local machine, open a terminal and run the tunnel command from the log:

ssh -N -L 9999:localhost:8945 -J <username>@hyperion.sw.ehu.es hyperion-107

ssh -N -L 9999:localhost:8945 -J <username>@hyperion.sw.ehu.es hyperion-107

Replace the port, username, and node with the actual values from your log. The terminal will appear to hang with no output; that is normal. Keep it open while you use RStudio.

For more details on SSH tunneling, see the SSH tunneling section in the connection guide.

Step 4: Open RStudio in your browser¶

Navigate to:

http://localhost:9999

Enter the username and password from the log (not your cluster password). The full RStudio interface will load in your browser.

Tips for using R on the cluster¶

Parallel computing¶

Your R session has access to the CPU cores you requested. You can use them with:

library(parallel)
results <- mclapply(1:100, my_function, mc.cores = 4)

Other options include foreach/doParallel and future/future.apply.

Working directory¶

RStudio has direct access to all your cluster filesystems. For job-related data, work from /scratch:

setwd("/scratch/<username>/my_project")

See the Data transfer page for an overview of the available filesystems.

Large datasets¶

For large files, data.table::fread() and vroom::vroom() are significantly faster than base R functions. For saving R objects, saveRDS()/readRDS() or the qs package provide fast binary storage.

Stopping the server¶

To end your RStudio session, cancel the batch job:

scancel <JOBID>

You can find the job ID with squeue -u $USER.

Troubleshooting¶

See also¶