RStudio Server

Quick Start Guide

Example SLURM Script to launch RStudio Server.

The example will use the srlab-bioinformatics-container-2bd5d44.sif container in the SLURM script called rstudio-server.job.

User needs to set/change the following in the SLURM script before starting script:
- #SBATCH --time=02:00:00
- #SBATCH --mem=20G
- --chdir=/gscratch/scrubbed/${USER}/<add_rest_of_path>
- RSTUDIO_SIF="srlab-bioinformatics-container-2bd5d44.sif" # UPDATE THIS LINE
Users should add the following line in ~/.Renviron. If you don't have a ~/.Renviron, you can create it like this: touch ~/.Renviron
- R_LIBS_USER in ~/.Renviron. Example:
```
cat ~/.Renviron 
# Set local library installation path
R_LIBS_USER=/gscratch/srlab/${USER}/R_libs_apptainer
```
After submitting script (sbatch rstudio-server.job), view the SLURM output file located in --chdir=/gscratch/scrubbed/${USER}/<add_rest_of_path> for information on:
1. How to create tunnel to Mox node.
2. NOTE: When logging into the tunnel, the terminal will not acknowledge when you've logged in. You need to leave this Terminal window open.
3. What address to direct your web browser to (localhost:8787).
4. Username/password to enter into RStudio Server interface.
5. How to terminate RStudio Server and the SLURM job.
Example script uses the following Apptainer container image: srlab-bioinformatics-container-2bd5d44.sif.

$ cat rstudio-server.job 


#!/bin/sh

#SBATCH --job-name=rstudio-server
#SBATCH --account=srlab
#SBATCH --partition=cpu-g2-mem2x #update this line - use hyakalloc to find partitions you can use
#SBATCH --time=02:00:00
#SBATCH --nodes=1
#SBATCH --mem=20G
#SBATCH --signal=USR2
#SBATCH --output=%x_%j.out
## Specify the working directory for this job
#SBATCH --chdir=/gscratch/scrubbed/${USER}

# This script will request a single CPU with four threads with 20GB of RAM for 2 hours. 
# You can adjust --time, --nodes, --ntasks, and --mem above to adjust these settings for your session.

# --output=%x_%j.out creates a output file called rstudio-server_NNNNNNNN.out 
# where the %x is short hand for --job-name above and the N's are an 8-digit 
# jobID assigned by SLURM when our job is submitted.

RSTUDIO_CWD="/gscratch/srlab/containers" # UPDATE THIS LINE
RSTUDIO_SIF="srlab-bioinformatics-container-2bd5d44.sif" # UPDATE THIS LINE

# Create temp directory for ephemeral content to bind-mount in the container
RSTUDIO_TMP=$(/usr/bin/python3 -c 'import tempfile; print(tempfile.mkdtemp())')

mkdir -p -m 700 \
        ${RSTUDIO_TMP}/run \
        ${RSTUDIO_TMP}/tmp \
        ${RSTUDIO_TMP}/var/lib/rstudio-server

cat > ${RSTUDIO_TMP}/database.conf <<END
provider=sqlite
directory=/var/lib/rstudio-server
END

# Set OMP_NUM_THREADS to prevent OpenBLAS (and any other OpenMP-enhanced
# libraries used by R) from spawning more threads than the number of processors
# allocated to the job.
#
# Set R_LIBS_USER to a path specific to rocker/rstudio to avoid conflicts with
# personal libraries from any R installation in the host environment

cat > ${RSTUDIO_TMP}/rsession.sh <<END
#!/bin/sh

export OMP_NUM_THREADS=${SLURM_JOB_CPUS_PER_NODE}
export R_LIBS_USER=${RSTUDIO_CWD}/R
exec /usr/lib/rstudio-server/bin/rsession "\${@}"
END

chmod +x ${RSTUDIO_TMP}/rsession.sh

export APPTAINER_BIND="${RSTUDIO_CWD}:${RSTUDIO_CWD},/gscratch:/gscratch,${RSTUDIO_TMP}/run:/run,${RSTUDIO_TMP}/tmp:/tmp,${RSTUDIO_TMP}/database.conf:/etc/rstudio/database.conf,${RSTUDIO_TMP}/rsession.sh:/etc/rstudio/rsession.sh,${RSTUDIO_TMP}/var/lib/rstudio-server:/var/lib/rstudio-server"

# Do not suspend idle sessions.
# Alternative to setting session-timeout-minutes=0 in /etc/rstudio/rsession.conf
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 & apptainer commands
readonly PORT=$(/mmfs1/sw/pyenv/versions/3.9.5/bin/python -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:${HOSTNAME}:${PORT} ${APPTAINERENV_USER}@klone.hyak.uw.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

source /etc/bashrc
module load apptainer

apptainer exec --cleanenv --home ${RSTUDIO_CWD} ${RSTUDIO_CWD}/${RSTUDIO_SIF} \
    rserver --www-port ${PORT} \
            --auth-none=0 \
            --auth-pam-helper-path=pam-helper \
            --auth-stay-signed-in-days=30 \
            --auth-timeout-minutes=0 \
            --rsession-path=/etc/rstudio/rsession.sh \
            --server-user=${APPTAINERENV_USER}

APPTAINER_EXIT_CODE=$?
echo "rserver exited $APPTAINER_EXIT_CODE" 1>&2
exit $APPTAINER_EXIT_CODE

Create/customize your own Apptainer Rstudio Server container

NOTE: These instructions are written to be performed on Klone (Hyak).

Create an Apptainer definition file:

Example filename: rstudio-4.4.1.def
Here's an example with a good set of basic installations for R 4.4.1:

Bootstrap: docker
From: rocker/rstudio:4.4.1
%files
    # Load file with R package installation commands in to container at /tmp
    # Expects file called "r_packages_installs.R" to be in current directory.
    r_packages_installs.R /tmp/

%post
    # Install additional system packages in container
    # Most are needed for R/RStudio dependencies
    apt -y update
    apt -y install libxml2 libz-dev libbz2-dev liblzma-dev libxtst6 libxt6

    # Run R package installation script file
    Rscript /tmp/r_packages_installs.R

Create file r_packages_installs.R containing R package installation instructions.

NOTE: The container already has a base set of R packages (e.g. ggplot2 installed).
Here's an example with a set of commonly used packages:

# Update base packages
update.packages(ask = FALSE)

# Install BioConductor package manager
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "3.19")

# Install tidyverse
install.packages("tidyverse")

# Install matrixStats 0.61.0 (needed for DESeq2)
install.packages("https://cran.rstudio.com/src/contrib/matrixStats_0.61.0.tar.gz", repos=NULL, type="source")

# Install remotes package (allows for package installs from GitHub)
BiocManager::install("remotes")

# Install GSEABase (a dependency for numerous gene ontology/enrichment analysis)
BiocManager::install("Bioconductor/GSEABase")

# Install qvalue package
BiocManager::install("qvalue")

# Install GO.db (annotation maps for Gene Ontology data)
BiocManager::install("GO.db")

# Install MatrixGenerics (needed for DESeq2)
BiocManager::install("MatrixGenerics")

# Install Methylkit
BiocManager::install("methylKit")

# Install GOseq
BiocManager::install("goseq")

# Install WGCNA
BiocManager::install("WGCNA")

# Install DESeq2
BiocManager::install("DESeq2")

Initiate an interactive node.
Build the container from the definition file:
- NOTE: Resulting container name will be rstudio-4.4.1.sjw-v1.0.sif
apptainer build --fakeroot rstudio-4.4.1.sjw-v1.0.sif rstudio-4.4.1.sjw-v1.0.def
Exit the interactive node.
Use the SLURM script above to start the container.
- NOTE: Be sure to update the script line to reflect your container name:
```
# Set container name
container="rstudio-4.4.1.sif"
```