Singularity is useful for running containers as an unprivileged user, especially in multi-user environments like High-Performance Computing clusters. Rocker images can be imported and run using Singularity, with optional custom password support.

Importing a Rocker Image

Use the singularity pull command to import the desired Rocker image from Docker Hub into a SquashFS (compressed, read-only) image:

singularity pull --name rstudio.simg docker://rocker/rstudio:latest

If additional Debian software packages are needed, see the Singularity documentation for building a writable image or writable sandbox directory (note that sudo privileges are required). A writable image is not needed for installing R packages into a personal library in the user’s home directory.

Running a Rocker Singularity container (localhost, no password)

singularity exec rstudio.simg rserver --www-address=

This will run rserver in a Singularity container. The --www-address= option binds to localhost (the default is, or all IP addresses on the host). listening on

Running a Rocker Singularity container with password authentication

To enable password authentication, set the PASSWORD environment variable and add the --auth-none=0 --auth-pam-helper-path=pam-helper options:

PASSWORD='...' singularity exec rstudio.simg rserver --auth-none=0  --auth-pam-helper-path=pam-helper

After pointing your browser to http://hostname:8787, enter your local user ID on the system as the username, and the custom password specified in the PASSWORD environment variable.

SLURM job script

On an HPC cluster, a Rocker Singularity container can be started on a compute node using the cluster’s job scheduler, allowing it to access compute, memory, and storage resources that may far exceed those found in a typical desktop workstation. The following example illustrates how this may be done with a SLURM job script.

#SBATCH --time=08:00:00
#SBATCH --signal=USR2
#SBATCH --ntasks=1
#SBATCH --cpus-per-tasks=2
#SBATCH --mem=8192
#SBATCH --output=/home/%u/rstudio-server.job.%j

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

   ssh -N -L 8787:${HOSTNAME}:${PORT} ${USER}@LOGIN-HOST

   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}

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

# 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=/project rstudio.simg \
    rserver --www-port ${PORT} --auth-none=0 --auth-pam-helper-path=pam-helper
printf 'rserver exited' 1>&2

The job script is submitted using the SLURM sbatch command:

$ sbatch rstudio-server.job
Submitted batch job 123456

After the scheduled job begins execution, rserver is started in a Singularity container, and the connection information (including the compute node hostname, TCP port, and a randomly-generated custom password) is sent in the job script stderr to a file in the user’s home directory named rstudio-server.job.123456.

The rserver process (and resulting rsession process after login) will persist until: 1. The job wall time (--time=08:00:00, or 8 hours) is reached. + The --signal=USR2 directive tells SLURM to send SIGUSR2 approximately 60 seconds before the wall time limit is reached. This causes the rsession process to save user’s session state to their home directory, so it can be resumed in a subsequent job. 2. The SLURM scancel command is used to cancel the job.