Content from Singularity: Getting started

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• What is Singularity and why might I want to use it?

Objectives

• Understand what Singularity is and when you might want to use it.
• Undertake your first run of a simple Singularity container.

The episodes in this lesson will introduce you to the Singularity container platform and demonstrate how to set up and use Singularity.

This material is split into 2 parts:

Part I: Basic usage, working with images

1. Singularity: Getting started: This introductory episode

Working with Singularity containers:

2. The singularity cache: Why, where and how does Singularity cache images locally?
3. Running commands within a Singularity container: How to run commands within a Singularity container.
4. Working with files and Singularity containers: Moving files into a Singularity container; accessing files on the host from within a container.
5. Using Docker images with Singularity: How to run Singularity containers from Docker images.

Part II: Creating images, running parallel codes

6. Preparing to build Singularity images: Getting started with the Docker Singularity container.
7. Building Singularity images: Explaining how to build and share your own Singularity images.
8. Running MPI parallel jobs using Singularity containers: Explaining how to run MPI parallel codes from within Singularity containers.

Work in progress…

This lesson is new material that is under ongoing development. We will introduce Singularity and demonstrate how to work with it. As the tools and best practices continue to develop, elements of this material are likely to evolve. We welcome any comments or suggestions on how the material can be improved or extended.

Singularity - Part I

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What is Singularity?

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Singularity is a container platform that allows software engineers and researchers to easily share their work with others by packaging and deploying their software applications in a portable and reproducible manner. When you download a Singularity container image, you essentially receive a virtual computer disk that contains all of the necessary software, libraries and configuration to run one or more applications or undertake a particular task, e.g. to support a specific research project. This saves you the time and effort of installing and configuring software on your own system or setting up a new computer from scratch, as you can simply run a Singularity container from the image and have a virtual environment that is identical to the one used by the person who created the image. Container platforms like Singularity provide a convenient and consistent way to access and run software and tools. Singularity is increasingly widely used in the research community for supporting research projects as it allows users to isolate their software environments from the host operating system and can simplify tasks such as running multiple experiments simultaneously.

You may be familiar with Docker, another container platform that is now used widely. If you are, you will see that in some ways, Singularity is similar to Docker. However, in others, particularly in the system’s architecture, it is fundamentally different. These differences mean that Singularity is particularly well-suited to running on distributed, High Performance Computing (HPC) infrastructure, as well as a Linux laptop or desktop!

Later in this material, when we come to look at building Singularity images ourselves, we will make use of Docker to provide an environment in which we can run Singularity with administrative privileges. In this context, some basic knowledge of Docker is strongly recommended. If you are covering this module independently, or as part of a course that hasn’t covered Docker, you can find an introduction to Docker in the “Reproducible Computational Environments Using Containers: Introduction to Docker” lesson.

System administrators will not, generally, install Docker on shared computing platforms such as lab desktops, research clusters or HPC platforms because the design of Docker presents potential security issues for shared platforms with multiple users. Singularity, on the other hand, can be run by end-users entirely within “user space”, that is, no special administrative privileges need to be assigned to a user in order for them to run and interact with containers on a platform where Singularity has been installed.

What is the relationship between Singularity, SingularityCE and Apptainer?

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Singularity is open source and was initially developed within the research community. The company Sylabs was founded in 2018 to provide commercial support for Singularity. In May 2021, Sylabs “forked” the codebase to create a new project called SingularityCE (where CE means “Community Edition”). This in effect marks a common point from which two projects—SingularityCE and Singularity—developed. Sylabs continue to develop both the free, open source SingularityCE and a Pro/Enterprise edition of the software. In November 2021, the original open source Singularity project renamed itself to Apptainer and joined the Linux Foundation.

At the time of writing, in the context of the material covered in this lesson, Apptainer and Singularity are effectively interchangeable. If you are working on a platform that now has Apptainer installed, you might find that the only change you need to make when working through this material is to use the the command apptainer instead of singularity. This course will continue to refer to Singularity until differences between the projects warrant choosing one project or the other for the course material.

Getting started with Singularity

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Initially developed within the research community, Singularity is open source and the repository is currently available in the “The Next Generation of High Performance Computing” GitHub organisation. Part I of this Singularity material is intended to be undertaken on a remote platform where Singularity has been pre-installed.

If you’re attending a taught version of this course, you will be provided with access details for a remote platform made available to you for use for Part I of the Singularity material. This platform will have the Singularity software pre-installed.

Installing Singularity on your own laptop/desktop

If you have a Linux system on which you have administrator access and you would like to install Singularity on this system, some information is provided at the start of Part II of the Singularity material.

Sign in to the remote platform, with Singularity installed, that you’ve been provided with access to. Check that the singularity command is available in your terminal:

Loading a module

HPC systems often use modules to provide access to software on the system so you may need to use the command:

BASH

$ module load singularity

before you can use the singularity command on the system.

BASH

$ singularity --version

OUTPUT

singularity version 3.5.3

Depending on the version of Singularity installed on your system, you may see a different version. At the time of writing, v3.5.3 is the latest release of Singularity.

Images and containers

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We’ll start with a brief note on the terminology used in this section of the course. We refer to both images and containers. What is the distinction between these two terms?

Images are bundles of files including an operating system, software and potentially data and other application-related files. They may sometimes be referred to as a disk image or container image and they may be stored in different ways, perhaps as a single file, or as a group of files. Either way, we refer to this file, or collection of files, as an image.

A container is a virtual environment that is based on an image. That is, the files, applications, tools, etc that are available within a running container are determined by the image that the container is started from. It may be possible to start multiple container instances from an image. You could, perhaps, consider an image to be a form of template from which running container instances can be started.

Getting an image and running a Singularity container

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If you recall from learning about Docker, Docker images are formed of a set of layers that make up the complete image. When you pull a Docker image from Docker Hub, you see the different layers being downloaded to your system. They are stored in your local Docker repository on your system and you can see details of the available images using the docker command.

Singularity images are a little different. Singularity uses the Singularity Image Format (SIF) and images are provided as single SIF files (with a .sif filename extension). Singularity images can be pulled from Singularity Hub, a registry for container images. Singularity is also capable of running containers based on images pulled from Docker Hub and some other sources. We’ll look at accessing containers from Docker Hub later in the Singularity material.

Singularity Hub

Note that in addition to providing a repository that you can pull images from, Singularity Hub can also build Singularity images for you from a recipe - a configuration file defining the steps to build an image. We’ll look at recipes and building images later.

Let’s begin by creating a test directory, changing into it and pulling a test Hello World image from Singularity Hub:

BASH

$ mkdir test
$ cd test
$ singularity pull hello-world.sif shub://vsoch/hello-world

OUTPUT

INFO:    Downloading shub image
 59.75 MiB / 59.75 MiB [===============================================================================================================] 100.00% 52.03 MiB/s 1s

What just happened?! We pulled a SIF image from Singularity Hub using the singularity pull command and directed it to store the image file using the name hello-world.sif in the current directory. If you run the ls command, you should see that the hello-world.sif file is now present in the current directory. This is our image and we can now run a container based on this image:

BASH

$ singularity run hello-world.sif

OUTPUT

RaawwWWWWWRRRR!! Avocado!

The above command ran the hello-world container from the image we downloaded from Singularity Hub and the resulting output was shown.

How did the container determine what to do when we ran it?! What did running the container actually do to result in the displayed output?

When you run a container from a Singularity image without using any additional command line arguments, the container runs the default run script that is embedded within the image. This is a shell script that can be used to run commands, tools or applications stored within the image on container startup. We can inspect the image’s run script using the singularity inspect command:

BASH

$ singularity inspect -r hello-world.sif

OUTPUT

#!/bin/sh

exec /bin/bash /rawr.sh

This shows us the script within the hello-world.sif image configured to run by default when we use the singularity run command.

That concludes this introductory Singularity episode. The next episode looks in more detail at running containers.

Key Points

• Singularity is another container platform and it is often used in cluster/HPC/research environments.
• Singularity has a different security model to other container platforms, one of the key reasons that it is well suited to HPC and cluster environments.
• Singularity has its own container image format (SIF).
• The singularity command can be used to pull images from Singularity Hub and run a container from an image file.

Content from The Singularity cache

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• Why does Singularity use a local cache?
• Where does Singularity store images?

Objectives

• Learn about Singularity’s image cache.
• Learn how to manage Singularity images stored locally.

Singularity’s image cache

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While Singularity doesn’t have a local image repository in the same way as Docker, it does cache downloaded image files. As we saw in the previous episode, images are simply .sif files stored on your local disk.

If you delete a local .sif image that you have pulled from a remote image repository and then pull it again, if the image is unchanged from the version you previously pulled, you will be given a copy of the image file from your local cache rather than the image being downloaded again from the remote source. This removes unnecessary network transfers and is particularly useful for large images which may take some time to transfer over the network. To demonstrate this, remove the hello-world.sif file stored in your test directory and then issue the pull command again:

BASH

$ rm hello-world.sif
$ singularity pull hello-world.sif shub://vsoch/hello-world

OUTPUT

INFO:    Use image from cache

As we can see in the above output, the image has been returned from the cache and we don’t see the output that we saw previously showing the image being downloaded from Singularity Hub.

How do we know what is stored in the local cache? We can find out using the singularity cache command:

BASH

$ singularity cache list

OUTPUT

There are 1 container file(s) using 62.65 MB and 0 oci blob file(s) using 0.00 kB of space
Total space used: 62.65 MB

This tells us how many container files are stored in the cache and how much disk space the cache is using but it doesn’t tell us what is actually being stored. To find out more information we can add the -v verbose flag to the list command:

BASH

$ singularity cache list -v

OUTPUT

NAME                     DATE CREATED           SIZE             TYPE
hello-world_latest.sif   2020-04-03 13:20:44    62.65 MB         shub

There are 1 container file(s) using 62.65 MB and 0 oci blob file(s) using 0.00 kB of space
Total space used: 62.65 MB

This provides us with some more useful information about the actual images stored in the cache. In the TYPE column we can see that our image type is shub because it’s a SIF image that has been pulled from Singularity Hub.

Cleaning the Singularity image cache

We can remove images from the cache using the singularity cache clean command. Running the command without any options will display a warning and ask you to confirm that you want to remove everything from your cache.

You can also remove specific images or all images of a particular type. Look at the output of singularity cache clean --help for more information.

Cache location

By default, Singularity uses $HOME/.singularity/cache as the location for the cache. You can change the location of the cache by setting the SINGULARITY_CACHEDIR environment variable to the cache location you want to use.

Key Points

• Singularity caches downloaded images so that an unchanged image isn’t downloaded again when it is requested using the singularity pull command.
• You can free up space in the cache by removing all locally cached images or by specifying individual images to remove.

Content from Using Singularity containers to run commands

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• How do I run different commands within a container?
• How do I access an interactive shell within a container?

Objectives

• Learn how to run different commands when starting a container.
• Learn how to open an interactive shell within a container environment.

Running specific commands within a container

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We saw earlier that we can use the singularity inspect command to see the run script that a container is configured to run by default. What if we want to run a different command within a container?

If we know the path of an executable that we want to run within a container, we can use the singularity exec command. For example, using the hello-world.sif container that we’ve already pulled from Singularity Hub, we can run the following within the test directory where the hello-world.sif file is located:

BASH

$ singularity exec hello-world.sif /bin/echo Hello World!

OUTPUT

Hello World!

Here we see that a container has been started from the hello-world.sif image and the /bin/echo command has been run within the container, passing the input Hello World!. The command has echoed the provided input to the console and the container has terminated.

Note that the use of singularity exec has overriden any run script set within the image metadata and the command that we specified as an argument to singularity exec has been run instead.

Basic exercise: Running a different command within the “hello-world” container

Can you run a container based on the hello-world.sif image that prints the current date and time?

Show me the solution

BASH

$ singularity exec hello-world.sif /bin/date

OUTPUT

Fri Jun 26 15:17:44 BST 2020

#### The difference between singularity run and singularity exec

Above we used the singularity exec command. In earlier episodes of this course we used singularity run. To clarify, the difference between these two commands is:

• singularity run: This will run the default command set for containers based on the specfied image. This default command is set within the image metadata when the image is built (we’ll see more about this in later episodes). You do not specify a command to run when using singularity run, you simply specify the image file name. As we saw earlier, you can use the singularity inspect command to see what command is run by default when starting a new container based on an image.

• singularity exec: This will start a container based on the specified image and run the command provided on the command line following singularity exec <image file name>. This will override any default command specified within the image metadata that would otherwise be run if you used singularity run.

Opening an interactive shell within a container

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If you want to open an interactive shell within a container, Singularity provides the singularity shell command. Again, using the hello-world.sif image, and within our test directory, we can run a shell within a container from the hello-world image:

BASH

$ singularity shell hello-world.sif

OUTPUT

Singularity> whoami
[<your username>]
Singularity> ls
hello-world.sif
Singularity> 

As shown above, we have opened a shell in a new container started from the hello-world.sif image. Note that the shell prompt has changed to show we are now within the Singularity container.

Discussion: Running a shell inside a Singularity container

Q: What do you notice about the output of the above commands entered within the Singularity container shell?

Q: Does this differ from what you might see within a Docker container?

Use the exit command to exit from the container shell.

Key Points

• The singularity exec is an alternative to singularity run that allows you to start a container running a specific command.
• The singularity shell command can be used to start a container and run an interactive shell within it.

Content from Files in Singularity containers

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• How do I make data available in a Singularity container?
• What data is made available by default in a Singularity container?

Objectives

• Understand that some data from the host system is usually made available by default within a container
• Learn more about how Singularity handles users and binds directories from the host filesystem.

The way in which user accounts and access permissions are handeld in Singularity containers is very different from that in Docker (where you effectively always have superuser/root access). When running a Singularity container, you only have the same permissions to access files as the user you are running as on the host system.

In this episode we’ll look at working with files in the context of Singularity containers and how this links with Singularity’s approach to users and permissions within containers.

Users within a Singularity container

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The first thing to note is that when you ran whoami within the container shell you started at the end of the previous episode, you should have seen the username that you were signed in as on the host system when you ran the container.

For example, if my username were jc1000, I’d expect to see the following:

BASH

$ singularity shell hello-world.sif
Singularity> whoami
jc1000

But hang on! I downloaded the standard, public version of the hello-world.sif image from Singularity Hub. I haven’t customised it in any way. How is it configured with my own user details?!

If you have any familiarity with Linux system administration, you may be aware that in Linux, users and their Unix groups are configured in the /etc/passwd and /etc/group files respectively. In order for the shell within the container to know of my user, the relevant user information needs to be available within these files within the container.

Assuming this feature is enabled within the installation of Singularity on your system, when the container is started, Singularity appends the relevant user and group lines from the host system to the /etc/passwd and /etc/group files within the container \[1\].

This means that the host system can effectively ensure that you cannot access/modify/delete any data you should not be able to on the host system and you cannot run anything that you would not have permission to run on the host system since you are restricted to the same user permissions within the container as you are on the host system.

Files and directories within a Singularity container

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Singularity also binds some directories from the host system where you are running the singularity command into the container that you’re starting. Note that this bind process is not copying files into the running container, it is making an existing directory on the host system visible and accessible within the container environment. If you write files to this directory within the running container, when the container shuts down, those changes will persist in the relevant location on the host system.

There is a default configuration of which files and directories are bound into the container but ultimate control of how things are set up on the system where you are running Singularity is determined by the system administrator. As a result, this section provides an overview but you may find that things are a little different on the system that you’re running on.

One directory that is likely to be accessible within a container that you start is your home directory. You may also find that the directory from which you issued the singularity command (the current working directory) is also mapped.

The mapping of file content and directories from a host system into a Singularity container is illustrated in the example below showing a subset of the directories on the host Linux system and in a Singularity container:

OUTPUT

Host system:                                                      Singularity container:
-------------                                                     ----------------------
/                                                                 /
├── bin                                                           ├── bin
├── etc                                                           ├── etc
│   ├── ...                                                       │   ├── ...
│   ├── group  ─> user's group added to group file in container ─>│   ├── group
│   └── passwd ──> user info added to passwd file in container ──>│   └── passwd
├── home                                                          ├── usr
│   └── jc1000 ───> user home directory made available ──> ─┐     ├── sbin
├── usr                 in container via bind mount         │     ├── home
├── sbin                                                    └────────>└── jc1000
└── ...                                                           └── ...

Questions and exercises: Files in Singularity containers

Q1: What do you notice about the ownership of files in a container started from the hello-world image? (e.g. take a look at the ownership of files in the root directory (/))

Exercise 1: In this container, try editing (for example using the editor vi which should be avaiable in the container) the /rawr.sh file. What do you notice?

If you’re not familiar with vi there are many quick reference pages online showing the main commands for using the editor, for example this one.

Exercise 2: In your home directory within the container shell, try and create a simple text file. Is it possible to do this? If so, why? If not, why not?! If you can successfully create a file, what happens to it when you exit the shell and the container shuts down?

Answers

A1: Use the ls -l command to see a detailed file listing including file ownership and permission details. You should see that most of the files in the / directory are owned by root, as you’d probably expect on any Linux system. If you look at the files in your home directory, they should be owned by you.

A Ex1: We’ve already seen from the previous answer that the files in / are owned by root so we wouldn’t expect to be able to edit them if we’re not the root user. However, if you tried to edit /rawr.sh you probably saw that the file was read only and, if you tried for example to delete the file you would have seen an error similar to the following: cannot remove '/rawr.sh': Read-only file system. This tells us something else about the filesystem. It’s not just that we don’t have permission to delete the file, the filesystem itself is read-only so even the root user wouldn’t be able to edit/delete this file. We’ll look at this in more detail shortly.

A Ex2: Within your home directory, you should be able to successfully create a file. Since you’re seeing your home directory on the host system which has been bound into the container, when you exit and the container shuts down, the file that you created within the container should still be present when you look at your home directory on the host system.

Binding additional host system directories to the container

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You will sometimes need to bind additional host system directories into a container you are using over and above those bound by default. For example:

• There may be a shared dataset in a shard location that you need access to in the container
• You may require executables and software libraries in the container

The -B option to the singularity command is used to specify additonal binds. For example, to bind the /work/z19/shared directory into a container you could use (note this directory is unlikely to exist on the host system you are using so you’ll need to test this using a different directory):

BASH

$ singularity shell -B /work/z19/shared hello-world.sif
Singularity> ls /work/z19/shared

OUTPUT

CP2K-regtest	    cube	     eleanor		   image256x192.pgm		kevin		    pblas			    q-e-qe-6.7
ebe		    evince.simg	     image512x384.pgm	   low_priority.slurm           pblas.tar.gz	                                    q-qe
Q1529568	    edge192x128.pgm  extrae		   image768x1152.pgm		mkdir		    petsc			    regtest-ls-rtp_forCray
adrianj		    edge256x192.pgm  gnuplot-5.4.1.tar.gz  image768x768.pgm		moose.job	    petsc-hypre			    udunits-2.2.28.tar.gz
antlr-2.7.7.tar.gz  edge512x384.pgm  hj			   job-defmpi-cpe-21.03-robust	mrb4cab		    petsc-hypre-cpe21.03	    xios-2.5
cdo-archer2.sif     edge768x768.pgm  image192x128.pgm	   jsindt			paraver		    petsc-hypre-cpe21.03-gcc10.2.0

Note that, by default, a bind is mounted at the same path in the container as on the host system. You can also specify where a host directory is mounted in the container by separating the host path from the container path by a colon (:) in the option:

BASH

$ singularity shell -B /work/z19/shared:/shared-data hello-world.sif
Singularity> ls /shared-data

OUTPUT

CP2K-regtest	    cube	     eleanor		   image256x192.pgm		kevin		    pblas			    q-e-qe-6.7
ebe		    evince.simg	     image512x384.pgm	   low_priority.slurm           pblas.tar.gz	                                    q-qe
Q1529568	    edge192x128.pgm  extrae		   image768x1152.pgm		mkdir		    petsc			    regtest-ls-rtp_forCray
adrianj		    edge256x192.pgm  gnuplot-5.4.1.tar.gz  image768x768.pgm		moose.job	    petsc-hypre			    udunits-2.2.28.tar.gz
antlr-2.7.7.tar.gz  edge512x384.pgm  hj			   job-defmpi-cpe-21.03-robust	mrb4cab		    petsc-hypre-cpe21.03	    xios-2.5
cdo-archer2.sif     edge768x768.pgm  image192x128.pgm	   jsindt			paraver		    petsc-hypre-cpe21.03-gcc10.2.0

You can also specify multiple binds to -B by separating them by commas (,).

You can also copy data into a container image at build time if there is some static data required in the image. We cover this later in the section on building Singularity containers.

References

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\[1\] Gregory M. Kurzer, Containers for Science, Reproducibility and Mobility: Singularity P2. Intel HPC Developer Conference, 2017. Available at: https://www.intel.com/content/dam/www/public/us/en/documents/presentation/hpc-containers-singularity-advanced.pdf

Key Points

• Your current directory and home directory are usually available by default in a container.
• You have the same username and permissions in a container as on the host system.
• You can specify additional host system directories to be available in the container.

Content from Using Docker images with Singularity

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• How do I use Docker images with Singularity?

Objectives

• Learn how to run Singularity containers based on Docker images.

Using Docker images with Singularity

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Singularity can also start containers directly from Docker images, opening up access to a huge number of existing container images available on Docker Hub and other registries.

While Singularity doesn’t actually run a container using the Docker image (it first converts it to a format suitable for use by Singularity), the approach used provides a seamless experience for the end user. When you direct Singularity to run a container based on pull a Docker image, Singularity pulls the slices or layers that make up the Docker image and converts them into a single-file Singularity SIF image.

For example, moving on from the simple Hello World examples that we’ve looked at so far, let’s pull one of the official Docker Python images. We’ll use the image with the tag 3.9.6-slim-buster which has Python 3.9.6 installed on Debian’s Buster (v10) Linux distribution:

BASH

$ singularity pull python-3.9.6.sif docker://python:3.9.6-slim-buster

OUTPUT

INFO:    Converting OCI blobs to SIF format
INFO:    Starting build...
Getting image source signatures
Copying blob 33847f680f63 done
Copying blob b693dfa28d38 done
Copying blob ef8f1a8cefd1 done
Copying blob 248d7d56b4a7 done
Copying blob 478d2dfa1a8d done
Copying config c7d70af7c3 done
Writing manifest to image destination
Storing signatures
2021/07/27 17:23:38  info unpack layer: sha256:33847f680f63fb1b343a9fc782e267b5abdbdb50d65d4b9bd2a136291d67cf75
2021/07/27 17:23:40  info unpack layer: sha256:b693dfa28d38fd92288f84a9e7ffeba93eba5caff2c1b7d9fe3385b6dd972b5d
2021/07/27 17:23:40  info unpack layer: sha256:ef8f1a8cefd144b4ee4871a7d0d9e34f67c8c266f516c221e6d20bca001ce2a5
2021/07/27 17:23:40  info unpack layer: sha256:248d7d56b4a792ca7bdfe866fde773a9cf2028f973216160323684ceabb36451
2021/07/27 17:23:40  info unpack layer: sha256:478d2dfa1a8d7fc4d9957aca29ae4f4187bc2e5365400a842aaefce8b01c2658
INFO:    Creating SIF file...

Note how we see singularity saying that it’s “Converting OCI blobs to SIF format”. We then see the layers of the Docker image being downloaded and unpacked and written into a single SIF file. Once the process is complete, we should see the python-3.9.6.sif image file in the current directory.

We can now run a container from this image as we would with any other singularity image.

Running the Python 3.9.6 image that we just pulled from Docker Hub

Try running the Python 3.9.6 image. What happens?

Try running some simple Python statements…

Running the Python 3.9.6 image

BASH

$ singularity run python-3.9.6.sif

This should put you straight into a Python interactive shell within the running container:

Python 3.9.6 (default, Jul 22 2021, 15:24:21)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

Now try running some simple Python statements:

PYTHON

>>> import math
>>> math.pi
3.141592653589793
>>> 

In addition to running a container and having it run the default run script, you could also start a container running a shell in case you want to undertake any configuration prior to running Python. This is covered in the following exercise:

Open a shell within a Python container

Try to run a shell within a singularity container based on the python-3.9.6.sif image. That is, run a container that opens a shell rather than the default Python interactive console as we saw above. See if you can find more than one way to achieve this.

Within the shell, try starting the Python interactive console and running some Python commands.

Show me the solution

Recall from the earlier material that we can use the singularity shell command to open a shell within a container. To open a regular shell within a container based on the python-3.9.6.sif image, we can therefore simply run:

BASH

$ singularity shell python-3.9.6.sif

OUTPUT

Singularity> echo $SHELL
/bin/bash
Singularity> cat /etc/issue
Debian GNU/Linux 10 \n \l

Singularity> python
Python 3.9.6 (default, Jul 22 2021, 15:24:21)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> print('Hello World!')
Hello World!
>>> exit()

Singularity> exit
$ 

It is also possible to use the singularity exec command to run an executable within a container. We could, therefore, use the exec command to run /bin/bash:

BASH

$ singularity exec python-3.9.6.sif /bin/bash

OUTPUT

Singularity> echo $SHELL
/bin/bash

You can run the Python console from your container shell simply by running the python command.

This concludes the fifth episode and Part I of the Singularity material. Part II contains a further three episodes where we’ll look at creating your own images and then more advanced use of containers for running MPI parallel applications.

References

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\[1\] Gregory M. Kurzer, Containers for Science, Reproducibility and Mobility: Singularity P2. Intel HPC Developer Conference, 2017. Available at: https://www.intel.com/content/dam/www/public/us/en/documents/presentation/hpc-containers-singularity-advanced.pdf

Key Points

• Singularity can start a container from a Docker image which can be pulled directly from Docker Hub.

Content from Preparing to build Singularity images

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• What environment do I need to build a Singularity image and how do I set it up?

Objectives

• Understand how to the Docker Singularity image provides an environment for building Singularity images.
• Understand different ways to run containers based on the Docker Singularity image.

Singularity - Part II

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Brief recap

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In the five episodes covering Part I of this Singularity material we’ve seen how Singularity can be used on a computing platform where you don’t have any administrative privileges. The software was pre-installed and it was possible to work with existing images such as Singularity image files already stored on the platform or images obtained from a remote image repository such as Singularity Hub or Docker Hub.

It is clear that between Singularity Hub and Docker Hub there is a huge array of images available, pre-configured with a wide range of software applications, tools and services. But what if you want to create your own images or customise existing images?

In this first of three episodes in Part II of the Singularity material, we’ll look at preparing to build Singularity images.

Preparing to use Singularity for building images

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So far you’ve been able to work with Singularity from your own user account as a non-privileged user. This part of the Singularity material requires that you use Singularity in an environment where you have administrative (root) access. While it is possible to build Singularity containers without root access, it is highly recommended that you do this as the root user, as highlighted in this section of the Singularity documentation. Bear in mind that the system that you use to build containers doesn’t have to be the system where you intend to run the containers. If, for example, you are intending to build a container that you can subsequently run on a Linux-based cluster, you could build the container on your own Linux-based desktop or laptop computer. You could then transfer the built image directly to the target platform or upload it to an image repository and pull it onto the target platform from this repository.

There are three different options for accessing a suitable environment to undertake the material in this part of the course:

1. Run Singularity from within a Docker container - this will enable you to have the required privileges to build images
2. Install Singularity locally on a system where you have administrative access
3. Use Singularity on a system where it is already pre-installed and you have administrative (root) access

We’ll focus on the first option in this part of the course - running singularity from within a Docker container. If you would like to install Singularity directly on your system, see the box below for some further pointers. However, please note that the installation process is an advanced task that is beyond the scope of this course so we won’t be covering this.

Installing Singularity on your local system (optional) \[Advanced task\]

If you are running Linux and would like to install Singularity locally on your system, the source code is provided via the The Next Generation of High Performance Computing (HPCng) community’s Singularity repository. See the releases here. You will need to install various dependencies on your system and then build Singularity from source code.

If you are not familiar with building applications from source code, it is strongly recommended that you use the Docker Singularity image, as described below in the “Getting started with the Docker Singularity image” section rather than attempting to build and install Singularity yourself. The installation process is an advanced task that is beyond the scope of this session.

However, if you have Linux systems knowledge and would like to attempt a local install of Singularity, you can find details in the INSTALL.md file within the Singularity repository that explains how to install the prerequisites and build and install the software. Singularity is written in the Go programming language and Go is the main dependency that you’ll need to install on your system. The process of installing Go and any other requirements is detailed in the INSTALL.md file.

Note

If you do not have access to a system with Docker installed, or a Linux system where you can build and install Singularity but you have administrative privileges on another system, you could look at installing a virtualisation tool such as VirtualBox on which you could run a Linux Virtual Machine (VM) image. Within the Linux VM image, you will be able to install Singularity. Again this is beyond the scope of the course.

If you are not able to access/run Singularity yourself on a system where you have administrative privileges, you can still follow through this material as it is being taught (or read through it in your own time if you’re not participating in a taught version of the course) since it will be helpful to have an understanding of how Singularity images can be built.

You could also attempt to follow this section of the lesson without using root and instead using the singularity command’s --fakeroot option. However, you may encounter issues with permissions when trying to build images and run your containers and this is why running the commands as root is strongly recommended and is the approach described in this lesson.

Getting started with the Docker Singularity image

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The Singularity Docker image is available from Quay.io.

Familiarise yourself with the Docker Singularity image

• Using your previously acquired Docker knowledge, get the Singularity image for v3.5.3 and ensure that you can run a Docker container using this image. For this exercise, we recommend using the image with the v3.5.3-slim tag since it’s a much smaller image.

• Create a directory (e.g. $HOME/singularity_data) on your host machine that you can use for storage of definition files (we’ll introduce these shortly) and generated image files.

  This directory should be bind mounted into the Docker container at the location /home/singularity every time you run it - this will give you a location in which to store built images so that they are available on the host system once the container exits. (take a look at the -v switch to the docker run command)

Hint: To be able to build an image using the Docker Singularity container, you’ll need to add the --privileged switch to your docker command line.

Hint: If you want to run a shell within the Docker Singularity container, you’ll need to override the entrypoint to tell the container to run /bin/bash - take a look at Docker’s --entrypoint switch.

Questions / Exercises:

1. Can you run a container from the Docker Singularity image? What is happening when you run the container?
2. Can you run an interactive /bin/sh shell in the Docker Singularity container?
3. Can you run an interactive Singularity shell in a Singularity container, within the Docker Singularity container?!

Running a container from the image

Answers:

1. Can you run a container from the Docker Singularity image? What is happening when you run the container?

The name/tag of the Docker Singularity image we’ll be using is: quay.io/singularity/singularity:v3.5.3-slim

Having a bound directory from the host system accessible within your running Docker Singularity container will give you somewhere to place created Singularity images so that they are accessible on the host system after the container exits. Begin by changing into the directory that you created above for storing your definiton files and built images (e.g. $HOME/singularity_data).

Running a Docker container from the image and binding the current directory to /home/singularity within the container can be achieved as follows:

docker run --privileged --rm -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim

Note that the image is configured to run the singularity command by default. So, when you run a container from it with no arguments, you see the singularity help output as if you had Singularity installed locally and had typed singularity on the command line.

To run a Singularity command, such as singularity cache list, within the docker container directly from the host system’s terminal you’d enter:

docker run --privileged --rm -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim cache list

The following diagram shows how the Docker Singularity image is being used to run a container on your host system and how a Singularity container can, in turn, be started within the Docker container:

2. Can you run an interactive shell in the Docker Singularity container?

To start a shell within the Singularity Docker container where you can then run the singularity command directly:

docker run -it --entrypoint=/bin/sh --privileged --rm -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim

Here we use the --entrypoint switch to the docker command to override the default behaviour when starting the container and instead of running the singularity command directly, we run a ‘sh’ shell. We also add the -it switch to provide an interactive terminal connection.

3. Can you run an interactive Singularity shell in a Singularity container, within the Docker Singularity container?!

As shown in the diagram above, you can do this. It is necessary to run singularity shell <image file name> within the Docker Singularity container. You would use a command similar to the following (assuming that my_test_image.sif is in the current directory where you run this command):

docker run --rm -it --privileged -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim shell --contain /home/singularity/my_test_image.sif

You may notice there’s a flag being passed to singularity shell (--contain - -c is the short form and also works). What is this doing? When running a singularity container, you may remember that we highlighted that some key files/directories from the host system are mapped into containers by default when you start them. The configuration in the Docker Singularity container attempts to mount the file /etc/localtime into the Singularity container but there is not a timezone configuration present in the Docker Singularity container and this file doesn’t exist resulting in an error. --contain prevents the default mounting of some key files/directories into the container and prevents this error from occurring. Later in this material, there’s an example of how to rectify the issue by creating a timezone configuration in the Docker Singularity container so that the --contain switch is no longer needed.

Summary / Comments:

You may choose to:

• open a shell within the Docker image so you can work at a command prompt and run the singularity command directly
• use the docker run command to run a new container instance every time you want to run the singularity command (the Docker Singularity image is configured with the singularity command as its entrypoint).

Either option is fine for this section of the material.

To make things easier to read in the remainder of the material, command examples will use the singularity command directly, e.g. singularity cache list. If you’re running a shell in the Docker Singularity container, you can enter the commands as they appear. If you’re using the container’s default run behaviour and running a container instance for each run of the command, you’ll need to replace singularity with docker run --privileged -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim or similar.

This can be a little cumbersome to work with. However, if you’re using Linux or macOS on your host system, you can add a command alias to alias the command singularity on your host system to run the Docker Singularity container, e.g. (for bash shells - syntax for other shells varies):

alias singularity='docker run --privileged -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim'

This means you’ll only have to type singularity at the command line as shown in the examples throughout this section of the material

Key Points

• A Docker image is provided to run Singularity - this avoids the need to have a local Singularity installation on your system.
• The Docker Singularity image can be used to build containers on Linux, macOS and Windows.
• You can also run Singularity containers within the Docker Singularity image.

Content from Building Singularity images

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• How do I create my own Singularity images?

Objectives

• Understand the different Singularity container file formats.
• Understand how to build and share your own Singularity containers.

Building Singularity images

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Introduction

As a platform that is widely used in the scientific/research software and HPC communities, Singularity provides great support for reproducibility. If you build a Singularity image for some scientific software, it’s likely that you and/or others will want to be able to reproduce exactly the same environment again. Maybe you want to verify the results of the code or provide a means that others can use to verify the results to support a paper or report. Maybe you’re making a tool available to others and want to ensure that they have exactly the right version/configuration of the code.

Similarly to Docker and many other modern software tools, Singularity follows the “Configuration as code” approach and a container configuration can be stored in a file which can then be committed to your version control system alongside other code. Assuming it is suitably configured, this file can then be used by you or other individuals (or by automated build tools) to reproduce a container with the same configuration at some point in the future.

Different approaches to building images

There are various approaches to building Singularity images. We highlight two different approaches here and focus on one of them:

• Building within a sandbox: You can build a container interactively within a sandbox environment. This means you get a shell within the container environment and install and configure packages and code as you wish before exiting the sandbox and converting it into a container image.
• Building from a Singularity Definition File: This is Singularity’s equivalent to building a Docker container from a Dockerfile and we’ll discuss this approach in this section.

You can take a look at Singularity’s “Build a Container” documentation for more details on different approaches to building containers.

Why look at Singularity Definition Files?

Why do you think we might be looking at the definition file approach here rather than the sandbox approach?

Discussion

The sandbox approach is great for prototyping and testing out an image configuration but it doesn’t provide the best support for our ultimate goal of reproducibility. If you spend time sitting at your terminal in front of a shell typing different commands to add configuration, maybe you realise you made a mistake so you undo one piece of configuration and change it. This goes on until you have your completed, working configuration but there’s no explicit record of exactly what you did to create that configuration.

Say your container image file gets deleted by accident, or someone else wants to create an equivalent image to test something. How will they do this and know for sure that they have the same configuration that you had? With a definition file, the configuration steps are explicitly defined and can be easily stored (and re-run).

Definition files are small text files while container files may be very large, multi-gigabyte files that are difficult and time consuming to move around. This makes definition files ideal for storing in a version control system along with their revisions.

Creating a Singularity Definition File

A Singularity Definition File is a text file that contains a series of statements that are used to create a container image. In line with the configuration as code approach mentioned above, the definition file can be stored in your code repository alongside your application code and used to create a reproducible image. This means that for a given commit in your repository, the version of the definition file present at that commit can be used to reproduce a container with a known state. It was pointed out earlier in the course, when covering Docker, that this property also applies for Dockerfiles.

We’ll now look at a very simple example of a definition file:

BASH

Bootstrap: docker
From: ubuntu:20.04

%post
    apt-get -y update && apt-get install -y python

%runscript
    python -c 'print("Hello World! Hello from our custom Singularity image!")'

A definition file has a number of optional sections, specified using the % prefix, that are used to define or undertake different configuration during different stages of the image build process. You can find full details in Singularity’s Definition Files documentation. In our very simple example here, we only use the %post and %runscript sections.

Let’s step through this definition file and look at the lines in more detail:

BASH

Bootstrap: docker
From: ubuntu:20.04

These first two lines define where to bootstrap our image from. Why can’t we just put some application binaries into a blank image? Any applications or tools that we want to run will need to interact with standard system libraries and potentially a wide range of other libraries and tools. These need to be available within the image and we therefore need some sort of operating system as the basis for our image. The most straightforward way to achieve this is to start from an existing base image containing an operating system. In this case, we’re going to start from a minimal Ubuntu 20.04 Linux Docker image. Note that we’re using a Docker image as the basis for creating a Singularity image. This demonstrates the flexibility in being able to start from different types of images when creating a new Singularity image.

The Bootstrap: docker line is similar to prefixing an image path with docker:// when using, for example, the singularity pull command. A range of different bootstrap options are supported. From: ubuntu:20.04 says that we want to use the ubuntu image with the tag 20.04 from Docker Hub.

Next we have the %post section of the definition file:

BASH

%post
    apt-get -y update && apt-get install -y python3

In this section of the file we can do tasks such as package installation, pulling data files from remote locations and undertaking local configuration within the image. The commands that appear in this section are standard shell commands and they are run within the context of our new container image. So, in the case of this example, these commands are being run within the context of a minimal Ubuntu 20.04 image that initially has only a very small set of core packages installed.

Here we use Ubuntu’s package manager to update our package indexes and then install the python3 package along with any required dependencies. The -y switches are used to accept, by default, interactive prompts that might appear asking you to confirm package updates or installation. This is required because our definition file should be able to run in an unattended, non-interactive environment.

Finally we have the %runscript section:

BASH

%runscript
    python3 -c 'print("Hello World! Hello from our custom Singularity image!")'

This section is used to define a script that should be run when a container is started based on this image using the singularity run command. In this simple example we use python3 to print out some text to the console.

We can now save the contents of the simple defintion file shown above to a file and build an image based on it. In the case of this example, the definition file has been named my_test_image.def. (Note that the instructions here assume you’ve bound the image output directory you created to the /home/singularity directory in your Docker Singularity container, as explained in the “Getting started with the Docker Singularity image” section above.):

BASH

$ singularity build /home/singularity/my_test_image.sif /home/singularity/my_test_image.def

Recall from the details at the start of this section that if you are running your command from the host system command line, running an instance of a Docker container for each run of the command, your command will look something like this:

BASH

$ docker run --privileged --rm -v ${PWD}:/home/singularity quay.io/singularity/singularity:v3.5.3-slim build /home/singularity/my_test_image.sif /home/singularity/my_test_image.def

The above command requests the building of an image based on the my_test_image.def file with the resulting image saved to the my_test_image.sif file. Note that you will need to prefix the command with sudo if you’re running a locally installed version of Singularity and not running via Docker because it is necessary to have administrative privileges to build the image. You should see output similar to the following:

OUTPUT

INFO:    Starting build...
Getting image source signatures
Copying blob d51af753c3d3 skipped: already exists
Copying blob fc878cd0a91c skipped: already exists
Copying blob 6154df8ff988 skipped: already exists
Copying blob fee5db0ff82f skipped: already exists
Copying config 95c3f3755f done
Writing manifest to image destination
Storing signatures
2020/04/29 13:36:35  info unpack layer: sha256:d51af753c3d3a984351448ec0f85ddafc580680fd6dfce9f4b09fdb367ee1e3e
2020/04/29 13:36:36  info unpack layer: sha256:fc878cd0a91c7bece56f668b2c79a19d94dd5471dae41fe5a7e14b4ae65251f6
2020/04/29 13:36:36  info unpack layer: sha256:6154df8ff9882934dc5bf265b8b85a3aeadba06387447ffa440f7af7f32b0e1d
2020/04/29 13:36:36  info unpack layer: sha256:fee5db0ff82f7aa5ace63497df4802bbadf8f2779ed3e1858605b791dc449425
INFO:    Running post scriptlet
+ apt-get -y update
Get:1 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB]
...
  [Package update output truncated]
...
Fetched 13.4 MB in 2s (5575 kB/s)
Reading package lists... Done
+ apt-get install -y python3
Reading package lists... Done
...
  [Package install output truncated]
...Processing triggers for libc-bin (2.31-0ubuntu9) ...
INFO:    Adding runscript
INFO:    Creating SIF file...
INFO:    Build complete: my_test_image.sif
$ 

You should now have a my_test_image.sif file in the current directory. Note that in the above output, where it says INFO: Starting build... there is a series of skipped: already exists messages for the Copying blob lines. This is because the Docker image slices for the Ubuntu 20.04 image have previously been downloaded and are cached on the system where this example is being run. On your system, if the image is not already cached, you will see the slices being downloaded from Docker Hub when these lines of output appear.

Permissions of the created image file

You may find that the created Singularity image file on your host filesystem is owned by the root user and not your user. In this case, you won’t be able to change the ownership/permissions of the file directly if you don’t have root access.

However, the image file will be readable by you and you should be able to take a copy of the file under a new name which you will then own. You will then be able to modify the permissions of this copy of the image and delete the original root-owned file since the default permissions should allow this.

Cluster platform configuration for running Singularity containers

Note to instructors: Add details into this box of any custom configuration that needs to be done on the cluster platform or other remote system that you’re providing access to for the purpose of undertaking this course. If singularity does not require any custom configuration by the user on the host platform, you can remove this box.

It is recommended that you move the created .sif file to a platform with an installation of Singularity, rather than attempting to run the image using the Docker container. However, if you do wish to try using the Docker container, see the notes below on “Using singularity run from within the Docker container” for further information.

If you have access to a remote platform with Singularity installed on it, you should now move your created .sif image file to this platform. You could, for example, do this using the command line secure copy command scp.

Using scp (secure copy) to copy files between systems

scp is a widely used tool that uses the SSH protocol to securely copy files between systems. As such, the syntax is similar to that of SSH.

For example, if you want to copy the my_image.sif file from the current directory on your local system to your home directory (e.g. /home/myuser/) on a remote system (e.g. hpc.myinstitution.ac.uk) where an SSH private key is required for login, you would use a command similar to the following:

scp -i /path/to/keyfile/id_mykey ./my_image.sif myuser@hpc.myinstitution.ac.uk:/home/myuser/

Note that if you leave off the /home/myuser and just end the command with the :, the file will, by default, be copied to your home directory.

We can now attempt to run a container from the image that we built:

BASH

$ singularity run my_test_image.sif

If everything worked successfully, you should see the message printed by Python:

OUTPUT

Hello World! Hello from our custom Singularity image!

Using singularity run from within the Docker container

It is strongly recommended that you don’t use the Docker container for running Singularity images, only for creating them, since the Singularity command runs within the container as the root user.

However, for the purposes of this simple example, and potentially for testing/debugging purposes it is useful to know how to run a Singularity container within the Docker Singularity container. You may recall from the Running a container from the image section in the previous episode that we used the --contain switch with the singularity command. If you don’t use this switch, it is likely that you will get an error relating to /etc/localtime similar to the following:

OUTPUT

WARNING: skipping mount of /etc/localtime: no such file or directory
FATAL:   container creation failed: mount /etc/localtime->/etc/localtime error: while mounting /etc/localtime: mount source /etc/localtime doesn't exist

This occurs because the /etc/localtime file that provides timezone configuration is not present within the Docker container. If you want to use the Docker container to test that your newly created image runs, you can use the --contain switch, or you can open a shell in the Docker container and add a timezone configuration as described in the Alpine Linux documentation:

BASH

$ apk add tzdata
$ cp /usr/share/zoneinfo/Europe/London /etc/localtime

The singularity run command should now work successfully without needing to use --contain. Bear in mind that once you exit the Docker Singularity container shell and shutdown the container, this configuration will not persist.

More advanced definition files

Here we’ve looked at a very simple example of how to create an image. At this stage, you might want to have a go at creating your own definition file for some code of your own or an application that you work with regularly. There are several definition file sections that were not used in the above example, these are:

• %setup
• %files
• %environment
• %startscript
• %test
• %labels
• %help

The Sections part of the definition file documentation details all the sections and provides an example definition file that makes use of all the sections.

Additional Singularity features

Singularity has a wide range of features. You can find full details in the Singularity User Guide and we highlight a couple of key features here that may be of use/interest:

Remote Builder Capabilities: If you have access to a platform with Singularity installed but you don’t have root access to create containers, you may be able to use the Remote Builder functionality to offload the process of building an image to remote cloud resources. You’ll need to register for a cloud token via the link on the Remote Builder page.

Signing containers: If you do want to share container image (.sif) files directly with colleagues or collaborators, how can the people you send an image to be sure that they have received the file without it being tampered with or suffering from corruption during transfer/storage? And how can you be sure that the same goes for any container image file you receive from others? Singularity supports signing containers. This allows a digital signature to be linked to an image file. This signature can be used to verify that an image file has been signed by the holder of a specific key and that the file is unchanged from when it was signed. You can find full details of how to use this functionality in the Singularity documentation on Signing and Verifying Containers.

Key Points

• Singularity definition files are used to define the build process and configuration for an image.
• Singularity’s Docker container provides a way to build images on a platform where Singularity is not installed but Docker is available.
• Existing images from remote registries such as Docker Hub and Singularity Hub can be used as a base for creating new Singularity images.

Content from Running MPI parallel jobs using Singularity containers

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Last updated on 2024-09-17 | Edit this page

Overview

Questions

• How do I set up and run an MPI job from a Singularity container?

Objectives

• Learn how MPI applications within Singularity containers can be run on HPC platforms
• Understand the challenges and related performance implications when running MPI jobs via Singularity

Running MPI parallel codes with Singularity containers

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MPI overview

MPI - Message Passing Interface - is a widely used standard for parallel programming. It is used for exchanging messages/data between processes in a parallel application. If you’ve been involved in developing or working with computational science software, you may already be familiar with MPI and running MPI applications.

When working with an MPI code on a large-scale cluster, a common approach is to compile the code yourself, within your own user directory on the cluster platform, building against the supported MPI implementation on the cluster. Alternatively, if the code is widely used on the cluster, the platform administrators may build and package the application as a module so that it is easily accessible by all users of the cluster.

MPI codes with Singularity containers

We’ve already seen that building Singularity containers can be impractical without root access. Since we’re highly unlikely to have root access on a large institutional, regional or national cluster, building a container directly on the target platform is not normally an option.

If our target platform uses OpenMPI, one of the two widely used source MPI implementations, we can build/install a compatible OpenMPI version on our local build platform, or directly within the image as part of the image build process. We can then build our code that requires MPI, either interactively in an image sandbox or via a definition file.

If the target platform uses a version of MPI based on MPICH, the other widely used open source MPI implementation, there is ABI compatibility between MPICH and several other MPI implementations. In this case, you can build MPICH and your code on a local platform, within an image sandbox or as part of the image build process via a definition file, and you should be able to successfully run containers based on this image on your target cluster platform.

As described in Singularity’s MPI documentation, support for both OpenMPI and MPICH is provided. Instructions are given for building the relevant MPI version from source via a definition file and we’ll see this used in an example below.

Container portability and performance on HPC platforms

While building a container on a local system that is intended for use on a remote HPC platform does provide some level of portability, if you’re after the best possible performance, it can present some issues. The version of MPI in the container will need to be built and configured to support the hardware on your target platform if the best possible performance is to be achieved. Where a platform has specialist hardware with proprietary drivers, building on a different platform with different hardware present means that building with the right driver support for optimal performance is not likely to be possible. This is especially true if the version of MPI available is different (but compatible). Singularity’s MPI documentation highlights two different models for working with MPI codes. The hybrid model that we’ll be looking at here involves using the MPI executable from the MPI installation on the host system to launch singularity and run the application within the container. The application in the container is linked against and uses the MPI installation within the container which, in turn, communicates with the MPI daemon process running on the host system. In the following section we’ll look at building a Singularity image containing a small MPI application that can then be run using the hybrid model.

Building and running a Singularity image for an MPI code

Building and testing an image

This example makes the assumption that you’ll be building a container image on a local platform and then deploying it to a cluster with a different but compatible MPI implementation. See Singularity and MPI applications in the Singularity documentation for further information on how this works.

We’ll build an image from a definition file. Containers based on this image will be able to run MPI benchmarks using the OSU Micro-Benchmarks software.

In this example, the target platform is a remote HPC cluster that uses Intel MPI. The container can be built via the Singularity Docker image that we used in the previous episode of the Singularity material.

Begin by creating a directory and, within that directory, downloading and saving the “tarballs” for version 5.7.1 of the OSU Micro-Benchmarks from the OSU Micro-Benchmarks page and for MPICH version 3.4.2 from the MPICH downloads page.

In the same directory, save the following definition file content to a .def file, e.g. osu_benchmarks.def:

OUTPUT

Bootstrap: docker
From: ubuntu:20.04

%files
    /home/singularity/osu-micro-benchmarks-5.7.1.tgz /root/
    /home/singularity/mpich-3.4.2.tar.gz /root/

%environment
    export SINGULARITY_MPICH_DIR=/usr
    export OSU_DIR=/usr/local/osu/libexec/osu-micro-benchmarks/mpi

%post
    apt-get -y update && DEBIAN_FRONTEND=noninteractive apt-get -y install build-essential libfabric-dev libibverbs-dev gfortran
    cd /root
    tar zxvf mpich-3.4.2.tar.gz && cd mpich-3.4.2
    echo "Configuring and building MPICH..."
    ./configure --prefix=/usr --with-device=ch3:nemesis:ofi && make -j2 && make install
    cd /root
    tar zxvf osu-micro-benchmarks-5.7.1.tgz
    cd osu-micro-benchmarks-5.7.1/
    echo "Configuring and building OSU Micro-Benchmarks..."
    ./configure --prefix=/usr/local/osu CC=/usr/bin/mpicc CXX=/usr/bin/mpicxx
    make -j2 && make install

%runscript
    echo "Rank ${PMI_RANK} - About to run: ${OSU_DIR}/$*"
    exec ${OSU_DIR}/$*

A quick overview of what the above definition file is doing:

• The image is being bootstrapped from the ubuntu:20.04 Docker image.
• In the %files section: The OSU Micro-Benchmarks and MPICH tar files are copied from the current directory into the /root directory within the image.
• In the %environment section: Set a couple of environment variables that will be available within all containers run from the generated image.
• In the %post section:
  • Ubuntu’s apt-get package manager is used to update the package directory and then install the compilers and other libraries required for the MPICH build.
  • The MPICH .tar.gz file is extracted and the configure, build and install steps are run. Note the use of the --with-device option to configure MPICH to use the correct driver to support improved communication performance on a high performance cluster.
  • The OSU Micro-Benchmarks .tar.gz file is extracted and the configure, build and install steps are run to build the benchmark code from source.
• In the %runscript section: A runscript is set up that will echo the rank number of the current process and then run the command provided as a command line argument.

Note that base path of the the executable to run ($OSU_DIR) is hardcoded in the run script. The command line parameter that you provide when running a container instance based on the image is then added to this base path. Example command line parameters include: startup/osu_hello, collective/osu_allgather, pt2pt/osu_latency, one-sided/osu_put_latency.

Build and test the OSU Micro-Benchmarks image

Using the above definition file, build a Singularity image named osu_benchmarks.sif.

Once the image has finished building, test it by running the osu_hello benchmark that is found in the startup benchmark folder.

NOTE: If you’re not using the Singularity Docker image to build your Singularity image, you will need to edit the path to the .tar.gz file in the %files section of the definition file.

Show me the solution

You should be able to build an image from the definition file as follows:

BASH

$ singularity build osu_benchmarks.sif osu_benchmarks.def

Note that if you’re running the Singularity Docker container directly from the command line to undertake your build, you’ll need to provide the full path to the .def file within the container - it is likely that this will be different to the file path on your host system. For example, if you’ve bind mounted the directory on your local system containing the file to /home/singularity within the container, the full path to the .def file will be /home/singularity/osu_benchmarks.def.

Assuming the image builds successfully, you can then try running the container locally and also transfer the SIF file to a cluster platform that you have access to (that has Singularity installed) and run it there.

Let’s begin with a single-process run of startup/osu_hello on your local system (where you built the container) to ensure that we can run the container as expected. We’ll use the MPI installation within the container for this test. Note that when we run a parallel job on an HPC cluster platform, we use the MPI installation on the cluster to coordinate the run so things are a little different…

Start a shell in the Singularity container based on your image and then run a single process job via mpirun:

BASH

$ singularity shell --contain /home/singularity/osu_benchmarks.sif
Singularity> mpirun -np 1 $OSU_DIR/startup/osu_hello

You should see output similar to the following:

OUTPUT

# OSU MPI Hello World Test v5.7.1
This is a test with 1 processes

Running Singularity containers via MPI

Assuming the above tests worked, we can now try undertaking a parallel run of one of the OSU benchmarking tools within our container image.

This is where things get interesting and we’ll begin by looking at how Singularity containers are run within an MPI environment.

If you’re familiar with running MPI codes, you’ll know that you use mpirun (as we did in the previous example), mpiexec or a similar MPI executable to start your application. This executable may be run directly on the local system or cluster platform that you’re using, or you may need to run it through a job script submitted to a job scheduler. Your MPI-based application code, which will be linked against the MPI libraries, will make MPI API calls into these MPI libraries which in turn talk to the MPI daemon process running on the host system. This daemon process handles the communication between MPI processes, including talking to the daemons on other nodes to exchange information between processes running on different machines, as necessary.

When running code within a Singularity container, we don’t use the MPI executables stored within the container (i.e. we DO NOT run singularity exec mpirun -np <numprocs> /path/to/my/executable). Instead we use the MPI installation on the host system to run Singularity and start an instance of our executable from within a container for each MPI process. Without Singularity support in an MPI implementation, this results in starting a separate Singularity container instance within each process. This can present some overhead if a large number of processes are being run on a host. Where Singularity support is built into an MPI implementation this can address this potential issue and reduce the overhead of running code from within a container as part of an MPI job.

Ultimately, this means that our running MPI code is linking to the MPI libraries from the MPI install within our container and these are, in turn, communicating with the MPI daemon on the host system which is part of the host system’s MPI installation. In the case of MPICH, these two installations of MPI may be different but as long as there is ABI compatibility between the version of MPI installed in your container image and the version on the host system, your job should run successfully.

We can now try running a 2-process MPI run of a point to point benchmark osu_latency. If your local system has both MPI and Singularity installed and has multiple cores, you can run this test on that system. Alternatively you can run on a cluster. Note that you may need to submit this command via a job submission script submitted to a job scheduler if you’re running on a cluster. If you’re attending a taught version of this course, some information will be provided below in relation to the cluster that you’ve been provided with access to.

Undertake a parallel run of the osu_latency benchmark (general example)

Move the osu_benchmarks.sif Singularity image onto the cluster (or other suitable) platform where you’re going to undertake your benchmark run.

You should be able to run the benchmark using a command similar to the one shown below. However, if you are running on a cluster, you may need to write and submit a job submission script at this point to initiate running of the benchmark.

BASH

$ mpirun -np 2 singularity run osu_benchmarks.sif pt2pt/osu_latency

Expected output and discussion

As you can see in the mpirun command shown above, we have called mpirun on the host system and are passing to MPI the singularity executable for which the parameters are the image file and any parameters we want to pass to the image’s run script, in this case the path/name of the benchmark executable to run.

The following shows an example of the output you should expect to see. You should have latency values shown for message sizes up to 4MB.

OUTPUT

Rank 1 - About to run: /.../mpi/pt2pt/osu_latency
Rank 0 - About to run: /.../mpi/pt2pt/osu_latency
# OSU MPI Latency Test v5.6.2
# Size          Latency (us)
0                       0.38
1                       0.34
...

Undertake a parallel run of the osu_latency benchmark (taught course cluster example)

Note to instructors: Add details into this box relating to running the above example on your chosen cluster platform. The example SLURM script provided here is based on the UK’s ARCHER2 HPC platform, you should replace the template file in the files directory of the repository with a submission script suited to your platform, if one is required.

This version of the exercise, for undertaking a parallel run of the osu_latency benchmark with your Singularity container that contains an MPI build, is specific to this run of the course.

The information provided here is specifically tailored to the HPC platform that you’ve been given access to for this taught version of the course.

Move the osu_benchmarks.sif Singularity image onto the cluster where you’re going to undertake your benchmark run. You should use scp or a similar utility to copy the file.

The platform you’ve been provided with access to uses Slurm to schedule jobs to run on the platform. You now need to create a Slurm job submission script to run the benchmark.

Download this template script and edit it to suit your configuration.

Submit the modified job submission script to the Slurm scheduler using the sbatch command.

BASH

$ sbatch osu_latency.slurm

Expected output and discussion

As you will have seen in the commands using the provided template job submission script, we have called mpirun on the host system and are passing to MPI the singularity executable for which the parameters are the image file and any parameters we want to pass to the image’s run script. In this case, the parameters are the path/name of the benchmark executable to run.

The following shows an example of the output you should expect to see. You should have latency values shown for message sizes up to 4MB.

OUTPUT

INFO:    Convert SIF file to sandbox...
INFO:    Convert SIF file to sandbox...
Rank 1 - About to run: /.../mpi/pt2pt/osu_latency
Rank 0 - About to run: /.../mpi/pt2pt/osu_latency
# OSU MPI Latency Test v5.6.2
# Size          Latency (us)
0                       1.49
1                       1.50
2                       1.50
...
4194304               915.44
INFO:    Cleaning up image...
INFO:    Cleaning up image...

This has demonstrated that we can successfully run a parallel MPI executable from within a Singularity container. However, in this case, the two processes will almost certainly have run on the same physical node so this is not testing the performance of the interconnects between nodes.

You could now try running a larger-scale test. You can also try running a benchmark that uses multiple processes, for example try collective/osu_gather.

Investigate performance when using a container image built on a local system and run on a cluster

To get an idea of any difference in performance between the code within your Singularity image and the same code built natively on the target HPC platform, try building the OSU benchmarks from source, locally on the cluster. Then try running the same benchmark(s) that you ran via the singularity container. Have a look at the outputs you get when running collective/osu_gather or one of the other collective benchmarks to get an idea of whether there is a performance difference and how significant it is.

Try running with enough processes that the processes are spread across different physical nodes so that you’re making use of the cluster’s network interconnects.

What do you see?

Discussion

You may find that performance is significantly better with the version of the code built directly on the HPC platform. Alternatively, performance may be similar between the two versions.

How big is the performance difference between the two builds of the code?

What might account for any difference in performance between the two builds of the code?

If performance is an issue for you with codes that you’d like to run via Singularity, you are advised to take a look at using the bind model for building/running MPI applications through Singularity.

Singularity wrap-up

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This concludes the 8 episodes of the course covering Singularity. We hope you found this information useful and that it has inspired you to use Singularity to help enhance the way you build/work with research software.

As a new set of material, we appreciate that there are likely to be improvements that can be made to enhance the quality of this material. We welcome your thoughts, suggestions and feedback on improvements that could be made to help others making use of these lessons.

Key Points

• Singularity images containing MPI applications can be built on one platform and then run on another (e.g. an HPC cluster) if the two platforms have compatible MPI implementations.
• When running an MPI application within a Singularity container, use the MPI executable on the host system to launch a Singularity container for each process.
• Think about parallel application performance requirements and how where you build/run your image may affect that.