Using Containers on Rivanna [Singularity]

Logging in to Rivanna:

  • Connect to Rivanna
    • SSH client or FastX Web
  • Run hdquota
    • Make sure you have a few GBs of free space
  • Run allocations
    • Check if you have rivanna-training

Basic Singularity commands


To download a container hosted on a registry, use the pull command. Docker images are automatically converted into Singularity format.

singularity pull [<SIF>] <URI>

  • <URI> (Unified resource identifiers)
    • [library|docker|shub]://[<user>/]<repo>[:<tag>]
    • Default prefix: library ( Singularity Library)
    • user: optional; may be empty (e.g. singularity pull ubuntu)
    • tag: optional; default: latest
  • <SIF> (Singularity image format)
    • Optional
    • Rename image; default: <repo>_<tag>.sif

Pull lolcow from Docker Hub

singularity pull docker://rsdmse/lolcow


Inspect an image before running it via inspect.

singularity inspect <SIF>

$ singularity inspect lolcow_latest.sif amd64 Friday_5_August_2022_9:54:5_EDT
org.label-schema.schema-version: 1.0
org.label-schema.usage.singularity.deffile.bootstrap: docker
org.label-schema.usage.singularity.deffile.from: rsdmse/lolcow
org.label-schema.usage.singularity.version: 3.7.1

Inspect runscript

This is the default command of the container. (Docker ENTRYPOINT is preserved.)

singularity inspect --runscript <SIF>

$ singularity inspect --runscript lolcow_latest.sif 
OCI_ENTRYPOINT='"/bin/sh" "-c" "fortune | cowsay | lolcat"'


There are three ways to run a container: run, shell, exec.


Execute the default command in inspect --runscript.

CPU: singularity run <SIF> = ./<SIF>

GPU: singularity run --nv <SIF> (later)



Start a Singularity container interactively in its shell.

singularity shell <SIF>

$ singularity shell lolcow_latest.sif

The change in prompt indicates you are now inside the container.

To exit the container shell, type exit.


Execute custom commands without shelling into the container.

singularity exec <SIF> <command>

$ singularity exec lolcow_latest.sif which fortune

Bind mount

  • Singularity bind mounts these host directories at runtime:
    • Personal directories: /home, /scratch
    • Leased storage shared by your research group: /project, /nv
    • Some system directories: /tmp, /sys, /proc, /dev, /usr
    • Your current working directory
  • Other directories inside the container are owned by root
  • To bind mount additional host directories/files, use --bind/-B:
singularity run|shell|exec -B <host_path>[:<container_path>] <SIF>


  1. For each of the three executables fortune, cowsay, lolcat, run which both inside and outside the lolcow container. Which one exists on both the host and the container?
  2. a) Run ls -l for your home directory both inside and outside the container. Verify that you get the same result. b) To disable all bind mounting, use run|shell|exec -c. Verify that $HOME is now empty.
  3. View the content of /etc/os-release both inside and outside the container. Are they the same or different? Why?
  4. (Advanced) Let’s see if we can run the host gcc inside the lolcow container. First load the module: module load gcc
    • Verify that the path to gcc (hint: which) is equal to $EBROOTGCC/bin.
    • Verify that $EBROOTGCC/bin is in your PATH.
    • Now shell into the container (hint: -B /apps) and examine the environment variables $EBROOTGCC and $PATH. Are they the same as those on the host? Why (not)?
    • In the container, add $EBROOTGCC/bin to PATH (hint: export). Is it detectable by which? Can you launch gcc? Why (not)?

Container Modules

Singularity module

On Rivanna, the singularity module serves as a “toolchain” that will activate container modules. You must load singularity before loading container modules.

See what modules are available by default:

module purge
module avail

Check the module version of Singularity:

module spider singularity

Load the Singularity module and check what modules are available:

module load singularity
module avail

You can now load container modules.

Container modules under singularity toolchain

The corresponding run command is displayed upon loading a module.

$ module load tensorflow
To execute the default application inside the container, run:
singularity run --nv $CONTAINERDIR/tensorflow-2.10.0.sif

$ module list
Currently Loaded Modules:
  1) singularity/3.7.1   2) tensorflow/2.10.0
  • $CONTAINERDIR is an environment variable. It is the directory where containers are stored on Rivanna.
  • After old container module versions are deprecated, the corresponding containers are placed in $CONTAINERDIR/archive. These are inaccessible through the module system, but you are welcome to use them if necessary.


  1. What happens if you load a container module without loading Singularity first?
    module purge
    module list
    module load tensorflow
  2. Check the versions of tensorflow via module spider tensorflow. How would you load a non-default version?
  3. What is the default command of the tensorflow container? Where was it pulled from?

Container Slurm job (TensorFlow on GPU)

  • Computationally intensive tasks must be performed on compute nodes.
  • Slurm is Rivanna’s resource manager.
  • Prepare a Slurm script to submit a job.

Copy these files:

cp /share/resources/tutorials/singularity_ws/tensorflow-2.10.0.slurm .
cp /share/resources/tutorials/singularity_ws/mnist_example.{ipynb,py} .

Examine Slurm script:

#SBATCH -A rivanna-training      # account name
#SBATCH -p gpu                   # partition/queue
#SBATCH --gres=gpu:1             # request 1 gpu
#SBATCH -c 1                     # request 1 cpu core
#SBATCH -t 00:05:00              # time limit: 5 min
#SBATCH -J tftest                # job name
#SBATCH -o tftest-%A.out         # output file
#SBATCH -e tftest-%A.err         # error file

# start with clean environment
module purge
module load singularity tensorflow/2.10.0

singularity run --nv $CONTAINERDIR/tensorflow-2.10.0.sif

Submit job:

sbatch tensorflow-2.10.0.slurm

What does --nv do?

See Singularity GPU user guide

$ singularity shell $CONTAINERDIR/tensorflow-2.10.0.sif
Singularity> ls /.singularity.d/libs

$ singularity shell --nv $CONTAINERDIR/tensorflow-2.10.0.sif
Singularity> ls /.singularity.d/libs

Custom Jupyter Kernel

“Can I use my own container on JupyterLab?”

Suppose you need to use TensorFlow 2.11.0 on JupyterLab. First, note we do not have tensorflow/2.11.0 as a module:

module spider tensorflow

Go to TensorFlow’s Docker Hub page and search for the tag (i.e. version). You’ll want to use one that has the -gpu-jupyter suffix. Pull the container in your Rivanna account.



  1. Create kernel directory
mkdir -p $DIR
cd $DIR
  1. Write kernel.json
 "argv": [
 "display_name": "Tensorflow 2.11",
 "language": "python"
  1. Write
module load singularity
singularity exec --nv /path/to/sif python -m ipykernel $@
  1. Change into an executable
chmod +x

Easy to automate!


This tool is currently limited to Python. The container must have the ipykernel Python package.

Usage: jkrollout sif display_name [gpu]
    sif          = file name of *.sif
    display_name = name of Jupyter kernel
    gpu          = enable gpu (default: false)
jkrollout /path/to/sif "Tensorflow 2.11" gpu

Test your new kernel

  • Go to
  • Select JupyterLab
    • Rivanna Partition: GPU
    • Work Directory: (location of your mnist_example.ipynb)
    • Allocation: rivanna-training
  • Select the new “TensorFlow 2.11” kernel
  • Run mnist_example.ipynb

Remove a custom kernel

rm -rf ~/.local/share/jupyter/kernels/tensorflow-2.11.0