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This page describes how to run RStudio in a container on Pawsey systems with Slurm. |
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For this example we're going to be using the rocker/tidyverse (external site) Docker image. At the time of writing the latest available R version is 3.6.1. It provides an R installation, the RStudio server, the R devtools and the Tidyverse collection for data science. This image ships with a startup script that allows for a number of runtime options to be specified: see the USE menu in the Rocker homepage (external site).
Setting up the job script
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title | Set the working directory within RStudio |
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| To read data from and write data to this directory, first manually change directory to this location from inside the RStudio session (see figure 2). |
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The following script launches an RStudio server on the compute node (download the template batch script). The first step in the script creates a working directory before launching Rstudio.
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language | bash |
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theme | Emacs |
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title | Listing 1. Slurm script for running RStudio in a container |
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collapse | true |
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| #!/bin/bash -l
# Allocate slurm resources, edit as necessary
#SBATCH --account=[your-project-name]=$PAWSEY_PROJECT
#SBATCH --ntasks=1
#SBATCH --nodes=1
#SBATCH --cpus-ntasksper-task=1
#SBATCH --mem=58G
#SBATCH --time=01:00:00
#SBATCH --job-name=rstudio_server
#SBATCH --partition=work
#SBATCH --export=NONE
# Set our working directory
# Should be in a writable path with some space, like /scratch
# You'll need to manually change dir to this one through the RStudio interface
# Session -> Set Working Directory -> Choose Directory -> ...
dir="${MYSCRATCH}/rstudio-dir"
# Set the image and tag we want to use
image="docker://rocker/tidyverse:3.6.1"
# Load Singularity. The version may change over time.
module load singularity/3.8.6-nompi
# You should not need to edit the lines below
# Prepare the working directory
mkdir -p $dir
cd ${dir}
# Get the image filename
imagename=${image##*/}
imagename=${imagename/:/_}.sif
# Create a user-specific tmp directory to avoid clashes between users
tmp_dir="/tmp/tmp_$USER"
mkdir -p $tmp_dir
# Get the hostname of the Zeus node
# We'll set up an SSH tunnel to connect to the RStudio server
host=$(hostname)
# Set the port for the SSH tunnel
# This part of the script uses a loop to search for available ports on the node;
# this will allow multiple instances of GUI servers to be run from the same host node
port="8787"
pfound="0"
while [ $port -lt 65535 ] ; do
check=$( netstatss -tuna | awk '{print $4}' | grep ":$port *" )
if [ "$check" == "" ] ; then
pfound="1"
break
fi
: $((++port))
done
if [ $pfound -eq 0 ] ; then
echo "No available communication port found to establish the SSH tunnel."
echo "Try again later. Exiting."
exit
fi
# Generate a random password for the session
export PASSWORD=$(openssl rand -base64 15)
# Load Singularity
module
load singularity
# Pull our Docker image in a folder
singularity pull $imagename $image
echo "*****************************************************"
echo "Setup - from your laptop do:"
echo "ssh -N -f -L ${port}:${host}:${port} $USER@zeus$USER@setonix.pawsey.org.au"
echo "*****"
echo "The launch directory is: $dir"
echo "*****"
echo "Secret for this session is: $PASSWORD"
echo "*****************************************************"
echo ""
echo "*****************************************************"
echo "Terminate - from your laptop do:"
echo "kill \$( ps x | grep 'ssh.*-L *${port}:${host}:${port}' | awk '{print \$1}' )"
echo "*****************************************************"
echo ""
# Launch our container
# Note that content of /home will be lost after runtime
# You'll need to manually change dir to the working dir through the RStudio interface
# Session -> Set Working Directory -> Choose Directory -> ...
srun -N $SLURM_JOB_NUM_NODES -n $SLURM_NTASKS -c $SLURM_CPUS_PER_TASK \
singularity exec -c \
-B ${tmp_dir}:/tmp \
-B ${dir}:$HOME \
${imagename} \
rserver --www-port ${port} --www-address 0.0.0.0 --auth-none=0 --auth-pam-helper-path=pam-helper
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