RStudio

Pre-launch

1. Login to the cluster and download a singularity/apptainer image from the rocker project (https://hub.docker.com/r/rocker/rstudio/tags). Pick one with the desired version of R. For example:

   • Download image for R 4.5

   [user@larcc-login1 ~]$ module load apptainer/1.3.4-gcc-11.5.0
   [user@larcc-login1 ~]$ apptainer pull docker://rocker/rstudio:4.5
   [user@larcc-login1 ~]$ ls
   rstudio_4.5.sif
   

   • Download image for latest R

   [user@larcc-login1 ~]$ module load apptainer/1.3.4-gcc-11.5.0
   [user@larcc-login1 ~]$ apptainer pull docker://rocker/rstudio:latest
   [user@larcc-login1 ~]$ ls
   rstudio_latest.sif
   

2. Create the following batch script, modifying the CHANGE ME!!! section and slurm parameter as appropriate

   #!/bin/sh
   #SBATCH --job-name="rstudio-server"
   #SBATCH --time=08:00:00
   #SBATCH --signal=USR2
   #SBATCH --nodes=1
   #SBATCH --partition=compute
   #SBATCH --mem=515002
   #SBATCH --output=/home/%u/rstudio-server.%j.out
   #SBATCH --error=/home/%u/rstudio-server.%j.err
   
   module load apptainer/1.3.4-gcc-11.5.0
   
   ## CHANGE ME!!! #############################################
   # You can set this to a specific R version if you would like
   # For example, VERSION_OF_R=4.5
   VERSION_OF_R=latest
   # Folder where the apptainer/singularity instances are saved
   FOLDER_CONTAINER_INSTANCES=$HOME
   ############################################################
   
   CONTAINER=${FOLDER_CONTAINER_INSTANCES}/rstudio_${VERSION_OF_R}.sif
   
   if ! [ -f $CONTAINER ]; then
       printf "Please run apptainer pull docker://rocker/rstudio:${VERSION_OF_R} and, once the download finishes, make sure the rstudio_${VERSION_OF_R}.sif file is placed in ${FOLDER_CONTAINER_INSTANCES}" 1>&2
   fi
   
   # Create temporary directory to be populated with directories to bind-mount in the container
   # where writable file systems are necessary. Adjust path as appropriate for your computing environment.
   workdir=$(mktemp -d)
   
   # Set R_LIBS_USER to an existing path specific to rocker/rstudio to avoid conflicts with
   # personal libraries from any R installation in the host environment
   
   cat > ${workdir}/rsession.sh <<END
   #!/bin/sh
   export R_LIBS_USER=\${HOME}/R/rocker-rstudio/${VERSION_OF_R}
   mkdir -p "\${R_LIBS_USER}"
   ## custom Rprofile & Renviron (default is \$HOME/.Rprofile and \$HOME/.Renviron)
   # export R_PROFILE_USER=/path/to/Rprofile
   # export R_ENVIRON_USER=/path/to/Renviron
   exec /usr/lib/rstudio-server/bin/rsession "\${@}"
   END
   
   chmod +x ${workdir}/rsession.sh
   
   export APPTAINER_BIND="${workdir}/rsession.sh:/etc/rstudio/rsession.sh"
   
   # Do not suspend idle sessions.
   # Alternative to setting session-timeout-minutes=0 in /etc/rstudio/rsession.conf
   # https://github.com/rstudio/rstudio/blob/v1.4.1106/src/cpp/server/ServerSessionManager.cpp#L126
   export APPTAINERENV_RSTUDIO_SESSION_TIMEOUT=0
   
   export APPTAINERENV_USER=$(id -un)
   export APPTAINERENV_PASSWORD=$(openssl rand -base64 15)
   # get unused socket per https://unix.stackexchange.com/a/132524
   # tiny race condition between the python & singularity commands
   readonly PORT=$(python3 -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
   cat 1>&2 <<END
   1. SSH tunnel from your workstation using the following command:
   
      ssh -N -L 8787:`hostname | sed 's/larcc-/larcc-hs-/'`:${PORT} ${APPTAINERENV_USER}@larcc.hpc.louisville.edu
   
      and point your web browser to http://localhost:8787
   
   2. log in to RStudio Server using the following credentials:
   
      user: ${APPTAINERENV_USER}
      password: ${APPTAINERENV_PASSWORD}
   
   When done using RStudio Server, terminate the job by:
   
   1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
   2. Issue the following command on the login node:
   
         scancel -f ${SLURM_JOB_ID}
   END
   
   singularity exec --cleanenv \
                    --scratch /run,/tmp,/var/lib/rstudio-server \
                    --workdir ${workdir} \
                    ${CONTAINER} \
       rserver --www-port ${PORT} \
               --auth-none=0 \
               --auth-pam-helper-path=pam-helper \
               --auth-stay-signed-in-days=30 \
               --auth-timeout-minutes=0 \
               --server-user=$(whoami) \
               --rsession-path=/etc/rstudio/rsession.sh
   printf 'rserver exited' 1>&2
   

Launch RStudio Server

1. Assume the script from the pre-launch step is located at ~/rstudio-server.sbatch. Then, submit it to slurm with sbatch ~/rstudio-server.sbatch.

2. The script will print to the standard error file (the one indicated in the #SBATCH --error option in the sbatch file) the instructions on how to connect to the RStudio web instance. Example output:

   1. SSH tunnel from your workstation using the following command:
   
      ssh -N -L 8787:larcc-hs-cpu1:48221 user@larcc.hpc.louisville.edu
   
      and point your web browser to http://localhost:8787
   
   2. log in to RStudio Server using the following credentials:
   
      user: user
   
      password: XnUPB9E0OVvjPfNH0nup
   

   Note

   48221 is a randomly picked port (see line 51 of the script) and the password XnUPB9E0OVvjPfNH0nup is randomly generated (see line 49 of the script). The ssh command in line 3 of the example output uses the login node as a proxy and reach the allocated compute node.