A working definition of a software container is A packaged user environment that can be “unpacked” and used across different systems, from laptops to cloud to HPC.
So, given our overview of JEDI Portability and use cases, containers seem like an ideal fit.
From a JEDI perspective, the main purpose of containers is Portability: they provide a uniform computing environment (software tools, libraries, compilers, etc) across different systems. So, users and developers can focus on working with the JEDI code without worrying about whether their version of cmake is up to date or whether or not NetCDF was configured with parallel HDF5 support (for example).
But containers offer other advantages as well, including the following.
BYOE: Bring Your Own Environment: Containers let JEDI masters pick and choose which software packages and versions to include and which configuration options will allow them to work optimally with the JEDI code. So, there is no need for users and developers to deal with compatibility issues.
Reproducibility: Like the JEDI code itself, containers can be tagged with public releases so that specific results such as forecasts/reanalyses or numerical experiments can be reproduced. For example, a researcher can specify the version of JEDI and the version of the container that was used in a particular publication so that others can reproduce the results presented. Singularity takes particular care to ensure that results are reproducible.
Workflow: Containers enable new JEDI users to get up and running quickly. They enable users to do code development on laptops and workstations, saving valuable HPC resources for production runs. And, they allow for the optimal support of particular use cases. For example, development containers include binary dependencies together with the compiler and MPI library that they were build with. Users/developers would then download the JEDI source code from GitHub and compile it within the container. By contrast, application containers include the compiled JEDI source code and dependencies, without the compilers themselves, ready to run (plug and play). For a list of currently available containers, consult JCSDA DockerHub page for Docker images and JCSDA Sylabs page for Singularity images.
In contrast to virtual machines, containers do not include the necessary software to build an entire operating system. Rather, they work with the host operating system to provide the desired functionality, including the libraries, applications, and other software tools that your code needs to run. So containers generally require much less memory to store and to set up than virtual machines. And, they are generally more efficient because they can interact with the hardware directly via the host kernel without the need for an intermediate interpretive layer called a hypervisor. However, one disadvantage of this is that containers are not entirely independent of the host system architecture. For instance, containers created on modern Apple M1 systems require a host OS that uses an aarch64/amd64 architecture, they will not run on Intel-based systems (x86_64).
The most popular container provider is Docker. This was introduced in 2013 and quickly became the industry standard, now supported by a wide variety of applications and computing platforms. But Docker has a fatal design flaw that makes it unsuitable for High Performance Computing (HPC). Namely, Docker containers run as a child process of a root daemon. This poses severe security risks on HPC systems because it could allow users to escalate their access privileges. This is unlikely to change because Docker was developed for business enterprise applications where this level of control is beneficial. See Kurtzer et al (2017) for further discussion.
By contrast, Singularity was developed by HPC professionals for HPC applications. Singularity includes HPC features such as native support for MPI schedulers (e.g. slurm) and GPU compute cores. Furthermore, Singularity can be built from Docker containers (or, more appropriately, from Docker images, which are multi-layered files that spawn Docker containers). JEDI Singulary containers are generated from a common Docker image.
However, there is one distinguishing feature of Docker is that is worth mentioning: it does not rely on the linux user namespaces and other features (for example, SetUID) that Singularity requires. This is what makes it unsuitable for HPC since it achieves containerization instead by means of the root daemon. However, these linux features are not yet supported by Mac OS and Windows. So, in short, Docker can run natively on laptops and PCs running Mac OS or Windows whereas Singularity cannot. Our recommendation for these systems is to use JEDI docker image directly. The images are publicly hosted on the Docker Hub. Users can also use Singularity within a virtual machine such as Vagrant.
docker pull jcsda/docker-<name>:latest
<name> specifies the compiler suite, mpi library, and container type
(e.g. development, application, or tutorial). For example, a name of
is used for the Docker image built with the gnu compiler suite and the openmpi mpi library.
For a list of currently available JEDI Docker containers, go to Docker Hub and search for
After pulling the Docker image you can start and login a Docker container using the command below:
docker run -it jcsda/docker-<name>:latest
-it flag will start an interactive session for you and your prompt will
change when you are in the container.
If you log in as root (the default) then the mpi tests will likely fail. We have
nonroot user in all of the JEDI containers. You can change
your user to
nonroot after logging into the container:
su - nonroot
Or log into the container as
docker run -u nonroot --rm -it jcsda/docker-<name>:latest
Please note that all the data in a Docker container will be lost if container is deleted.
You can avoid this by creating a shared volume between the host machine and Docker.
To create a shared volume you can use
docker run -it -v path/to/shared/folder/on/host:/home/nonroot/shared jcsda/docker-<name>:latest
You can find more information about Docker shared volume here.
Before starting the build of JEDI in the container you need to load the Spack modules:
export jedi_cmake_ROOT=/opt/view source /etc/profile.d/z10_spack_environment.sh
The public containers currently offered by JCSDA include:
Containers that include
-dev in their name are development containers
as described above. This means that they contain the
JEDI dependencies and compilers but not the JEDI code itself.
tutorial container is designed for use with the JEDI Tutorials.