r-job.sh) and then submit to the scheduler: sbatch r-job.sh #SBATCH -c 40 # number of core requested, decrease to maximum of 32 on Turing Here is an example SLURM batch file: #!/bin/bash You can submit an R script to the cluster using the SLURM scheduler. Just running crun Rscript will print the help information for Rscript. You can do this with the following command: crun Rscript This will load the R program and you will have access to the R interface. In order to run R interactively you should run: crun R You then have two options for running R: ¶ Running R interactively This loads the container environment and the required R singularity module. You will then use the following commands to load the R module. In order to run R from the command line, you use the following commands.įirst, you need to allocate a session on a compute node: salloc Please note that there is a time limit that can be adjusted on the length of your session. You will be placed into a new window with the RStudio session and you are ready to use the software. When the session is ready for launch you will see this:Ĭlick Connect to RStudio Server and your session with RStudio will be started.

This will reserve time on one of the compute or GPU nodes in the cluster. You can adjust this to a maximum of 24 hours.

Please note the Number of Hours field as this is the time limit of your session. Please choose the number of cores you need and the duration of your session and click Launch. In the top menu choose Interactive apps -> RStudio Server.Īfter choosing RStudio Server you will see the following window: You will login using your Midas account and password.Īfter logging in you will see this landing page: First you should login to the web interface here: Rstudio is provided using a web front end called open ondemand. This guide will show you how to run R in both graphical and command line modes. It provides a wide variety of statistical and graphical techniques, and is highly flexible.

R is a language and environment for statistical computing.

Rstudio server cloud