RStudio

Using RStudio on Walnut

If for some reason you do not or cannot use RStudio Server on a GCP instance, RStudio can be started up as a job on the cluster, allowing for a lot more resources than your desktop will allow. There are, however, a couple of complicating factors

  1. RStudio Server acts as an http server, which is how it delivers RStudio to you, the user.

  2. The version of RStudio and R available as environment modules may not be the latest or even versions that work with the software you need them to.

These can be worked around, though, by running RStudio from a Singularity container and setting up an ssh tunnel to the RStudio server. There are many prebuilt containers available from Dockerhub, though those from the Rocker Project, or building off that, Bioconductor are a good place to start (and on which to base your own containers).

Pre-written RStudio job script

Currently, in the /Volumes/guth_aci_informatics/software/containers/ folder, there is a bioconductor-3.11.sif that is built from from the bioconductor/bioconductor_docker:RELEASE_3_11 and in the /Volumes/guth_aci_informatics/software/ directory, there is an rstudio-server.job that will start and configure the server.

To run it

  1. Submit the script to slurm with :

    (base) smithm@walnut:~/$ sbatch /Volumes/guth_aci_informatics/software/rstudio-server.job

  2. After submitting the script, examine the file rstudio-server.job.log in your home directory:

    (base) smithm@walnut:~/$ cat ~/rstudio-server.job.log

  3. That log will have directions on how to setup a tunnel on your local desktop and how to log into the server.

  4. In a web browser, go to the address http://127.0.0.1:8787 and login using the credentials as detailed in the above log .

The script is a modified from of that from the Rocker Project’s guide to Singularity

#!/bin/sh
#SBATCH --time=08:00:00
#SBATCH --signal=USR2
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=64
#SBATCH --partition=highmem
#SBATCH --output=/home/%u/rstudio-server.job.log

export PASSWORD=$(openssl rand -base64 15)
# get unused socket per https://unix.stackexchange.com/a/132524
# tiny race condition between the python & singularity commands
readonly PORT=$(python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
readonly HOSTNAME=$(hostname -I)
cat 1>&2 <<END
1. SSH tunnel from your workstation using the following command:

ssh -N -L 8787:${HOSTNAME}:${PORT} ${USER}@walnut.rc.lan.omrf.org

and point your web browser to http://localhost:8787

2. log in to RStudio Server using the following credentials:

user: ${USER}
password: ${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

# User-installed R packages go into their home directory
if [ ! -e ${HOME}/.Renviron ]
then
printf '\nNOTE: creating ~/.Renviron file\n\n'
echo 'R_LIBS_USER=~/R/%p-library/%v' >> ${HOME}/.Renviron
fi

# This example bind mounts the /project directory on the host into the Singularity container.
# By default the only host file systems mounted within the container are $HOME, /tmp, /proc, /sys, and /dev.
singularity exec --bind=/s/guth-aci --bind=/Volumes/guth_aci_informatics/ /Volumes/guth_aci_informatics/software/rstudio.sif \
    rserver --www-port ${PORT} --auth-none=0 --auth-pam-helper-path=pam-helper
printf 'rserver exited' 1>&2