Singularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. This means that you don’t have to ask your cluster admin to install anything for you - you can put it in a Singularity container and run. Did you already invest in Docker? The Singularity software can import your Docker images without having Docker installed or being a superuser. Need to share your code? Put it in a Singularity container and your collaborator won’t have to go through the pain of installing missing dependencies. Do you need to run a different operating system entirely? You can “swap out” the operating system on your host for a different one within a Singularity container. As the user, you are in control of the extent to which your container interacts with its host. There can be seamless integration, or little to no communication at all. They have extensive documentation at their website.
Here are some of the use cases we support using Singularity:
- You already use Docker and want to run your jobs on HPC
- You want to preserve your environment so that a system change will not affect your work
- You need newer or different libraries than are offered on HPC systems
- Someone else developed the workflow using a different version of linux
- You prefer to use a Linux distribution other than CentOS, perhaps Ubuntu
- You want a container with a database server like MariaDB.
$ singularity --help
Linux container platform optimized for High Performance Computing (HPC)
singularity [global options...]
Singularity containers provide an application virtualization layer enabling
of compute via both application and environment portability. With one is
capable of building a root file system that runs on any Linux system where
Singularity is installed.
-d, --debug print debugging information (highest verbosity)
-h, --help help for singularity
-q, --quiet suppress normal output
-s, --silent only print errors
-t, --tokenfile string path to the file holding your sylabs
authentication token (default
-v, --verbose print additional information
--version version for singularity
build Build a new Singularity container
capability Manage Linux capabilities on containers
exec Execute a command within container
help Help about any command
inspect Display metadata for container if available
instance Manage containers running in the background
keys Manage OpenPGP key stores
pull Pull a container from a URI
push Push a container to a Library URI
run Launch a runscript within container
run-help Display help for container if available
search Search the library
shell Run a Bourne shell within container
sign Attach cryptographic signatures to container
test Run defined tests for this particular container
verify Verify cryptographic signatures on container
version Show application version
$ singularity help <command>
Additional help for any Singularity subcommand can be seen by appending
the subcommand name to the above command.
Singularity Changes in Version 3.x
Overview of the new version of Singularity
For existing users, two of the biggest changes are; that the file type of images now is ".sif" for Singularity Image Format for cryptographically signed and verifiable container images; and that your container may not run outside your home directory unless you include binding to other directories like /extra
Singularity Hub lets you build and keep containers at their Hub. You maintain your recipes there and each time you need to pull one, it gets built there and then you retrieve the container.
This is very convenient for the scenario where you do not have access to root authority to build the container. The build takes place through the Hub.
This also lets you share containers
Singularity Remote Builder (root access)
An earlier limitation of Singularity was the requirement for access to a root account to build a container. You will not have root access on a HPC cluster. Singularity 3.0 introduced the ability to build a container in the cloud precluding the root requirement.
There are tutorials located here. Or you can go to the Sylabs GitHub site with files and instructions for creating sample containers.
Singularity, Nvidia and GPU's
One of the most significant use cases for Singularity is to support machine learning workflows. The details are in the GPU section.
You can register at their NGC GPU Cloud site and pull your own containers. You can do this from HPC. Follow these instructions: