Reproducible Computational Environments Using Containers: Introduction to Docker: All in One View

Last updated on 2022-11-15 | Edit this page

Overview

Questions

What are containers, and why might they be useful to me?

Objectives

Show how software depending on other software leads to configuration management problems.
Identify the problems that software installation can pose for research.
Explain the advantages of containerization.
Explain how using containers can solve software configuration problems

Learning about Docker Containers

The Australian Research Data Commons has produced a short introductory video about Docker containers that covers many of the points below. Watch it before or after you go through this section to reinforce your understanding!

How can software containers help your research?

Australian Research Data Commons, 2021. How can software containers help your research?. [video] Available at: https://www.youtube.com/watch?v=HelrQnm3v4g DOI: http://doi.org/10.5281/zenodo.5091260

Scientific Software Challenges

What’s Your Experience?

Take a minute to think about challenges that you have experienced in using scientific software (or software in general!) for your research. Then, share with your neighbors and try to come up with a list of common gripes or challenges.

You may have come up with some of the following:

you want to use software that doesn’t exist for the operating system (Mac, Windows, Linux) you’d prefer.
you struggle with installing a software tool because you have to install a number of other dependencies first. Those dependencies, in turn, require other things, and so on (i.e. combinatoric explosion).
the software you’re setting up involves many dependencies and only a subset of all possible versions of those dependencies actually works as desired.
you’re not actually sure what version of the software you’re using because the install process was so circuitous.
you and a colleague are using the same software but get different results because you have installed different versions and/or are using different operating systems.
you installed everything correctly on your computer but now need to install it on a colleague’s computer/campus computing cluster/etc.
you’ve written a package for other people to use but a lot of your users frequently have trouble with installation.
you need to reproduce a research project from a former colleague and the software used was on a system you no longer have access to.

A lot of these characteristics boil down to one fact: the main program you want to use likely depends on many, many, different other programs (including the operating system!), creating a very complex, and often fragile system. One change or missing piece may stop the whole thing from working or break something that was already running. It’s no surprise that this situation is sometimes informally termed “dependency hell”.

Software and Science

Again, take a minute to think about how the software challenges we’ve discussed could impact (or have impacted!) the quality of your work. Share your thoughts with your neighbors. What can go wrong if our software doesn’t work?

Unsurprisingly, software installation and configuration challenges can have negative consequences for research:

you can’t use a specific tool at all, because it’s not available or installable.
you can’t reproduce your results because you’re not sure what tools you’re actually using.
you can’t access extra/newer resources because you’re not able to replicate your software set up.
others cannot validate and/or build upon your work because they cannot recreate your system’s unique configuration.

Thankfully there are ways to get underneath (a lot of) this mess: containers to the rescue! Containers provide a way to package up software dependencies and access to resources such as files and communications networks in a uniform manner.

What is a Container? What is Docker?

[Docker][Docker] is a tool that allows you to build what are called “containers.” It’s not the only tool that can create containers, but is the one we’ve chosen for this workshop. But what is a container?

To understand containers, let’s first talk briefly about your computer.

Your computer has some standard pieces that allow it to work – often what’s called the hardware. One of these pieces is the CPU or processor; another is the amount of memory or RAM that your computer can use to store information temporarily while running programs; another is the hard drive, which can store information over the long-term. All these pieces work together to do the “computing” of a computer, but we don’t see them because they’re hidden from view (usually).

Instead, what we see is our desktop, program windows, different folders, and files. These all live in what’s called the filesystem. Everything on your computer – programs, pictures, documents, the operating system itself – lives somewhere in the filesystem.

NOW, imagine you want to install some new software but don’t want to take the chance of making a mess of your existing system by installing a bunch of additional stuff (libraries/dependencies/etc.). You don’t want to buy a whole new computer because it’s too expensive. What if, instead, you could have another independent filesystem and running operating system that you could access from your main computer, and that is actually stored within this existing computer?

Or, imagine you have two tools you want to use in your groundbreaking research on cat memes: PurrLOLing, a tool that does AMAZINGLY well at predicting the best text for a meme based on the cat species and WhiskerSpot, the only tool available for identifying cat species from images. You want to send cat pictures to WhiskerSpot, and then send the species output to PurrLOLing. But there’s a problem: PurrLOLing only works on Ubuntu and WhiskerSpot is only supported for OpenSUSE so you can’t have them on the same system! Again, we really want another filesystem (or two) on our computer that we could use to chain together WhiskerSpot and PurrLOLing in a “pipeline”…

Container systems, like Docker, are special programs on your computer that make it possible! The term “container” can be usefully considered with reference to shipping containers. Before shipping containers were developed, packing and unpacking cargo ships was time consuming and error prone, with high potential for different clients’ goods to become mixed up. Just like shipping containers keep things together that should stay together, software containers standardize the description and creation of a complete software system: you can drop a container into any computer with the container software installed (the ‘container host’), and it should “just work”.

Virtualization

Containers are an example of what’s called virtualization – having a second “virtual” computer running and accessible from a main or host computer. Another example of virtualization are virtual machines or VMs. A virtual machine typically contains a whole copy of an operating system in addition to its own filesystem and has to get booted up in the same way a computer would. A container is considered a lightweight version of a virtual machine; underneath, the container is (usually) using the Linux kernel and simply has some flavour of Linux + the filesystem inside.

One final term: while the container is an alternative filesystem layer that you can access and run from your computer, the container image is the ‘recipe’ or template for a container. The container image has all the required information to start up a running copy of the container. A running container tends to be transient and can be started and shut down. The container image is more long-lived, as a definition for the container. You could think of the container image like a cookie cutter – it can be used to create multiple copies of the same shape (or container) and is relatively unchanging, where cookies come and go. If you want a different type of container (cookie) you need a different container image (cookie cutter).

Putting the Pieces Together

Think back to some of the challenges we described at the beginning. The many layers of scientific software installations make it hard to install and re-install scientific software – which ultimately, hinders reliability and reproducibility.

But now, think about what a container is – a self-contained, complete, separate computer filesystem. What advantages are there if you put your scientific software tools into containers?

This solves several of our problems:

documentation – there is a clear record of what software and software dependencies were used, from bottom to top.
portability – the container can be used on any computer that has Docker installed – it doesn’t matter whether the computer is Mac, Windows or Linux-based.
reproducibility – you can use the exact same software and environment on your computer and on other resources (like a large-scale computing cluster).
configurability – containers can be sized to take advantage of more resources (memory, CPU, etc.) on large systems (clusters) or less, depending on the circumstances.

The rest of this workshop will show you how to download and run containers from pre-existing container images on your own computer, and how to create and share your own container images.

Use cases for containers

Now that we have discussed a little bit about containers – what they do and the issues they attempt to address – you may be able to think of a few potential use cases in your area of work. Some examples of common use cases for containers in a research context include:

Using containers solely on your own computer to use a specific software tool or to test out a tool (possibly to avoid a difficult and complex installation process, to save your time or to avoid dependency hell).
Creating a Dockerfile that generates a container image with software that you specify installed, then sharing a container image generated using this Dockerfile with your collaborators for use on their computers or a remote computing resource (e.g. cloud-based or HPC system).
Archiving the container images so you can repeat analysis/modelling using the same software and configuration in the future – capturing your workflow.

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Key Points

Almost all software depends on other software components to function, but these components have independent evolutionary paths.
Small environments that contain only the software that is needed for a given task are easier to replicate and maintain.
Critical systems that cannot be upgraded, due to cost, difficulty, etc. need to be reproduced on newer systems in a maintainable and self-documented way.
Virtualization allows multiple environments to run on a single computer.
Containerization improves upon the virtualization of whole computers by allowing efficient management of the host computer’s memory and storage resources.
Containers are built from ‘recipes’ that define the required set of software components and the instructions necessary to build/install them within a container image.
Docker is just one software platform that can create containers and the resources they use.

Content from Introducing the Docker Command Line

Last updated on 2022-11-15 | Edit this page

Docker command line

Overview

Questions

How do I know Docker is installed and running?
How do I interact with Docker?

Objectives

Explain how to check that Docker is installed and is ready to use.
Demonstrate some initial Docker command line interactions.
Use the built-in help for Docker commands.

Start the Docker application that you installed in working through the setup instructions for this session. Note that this might not be necessary if your laptop is running Linux or if the installation added the Docker application to your startup process.

You may need to login to Docker Hub

The Docker application will usually provide a way for you to log in to the Docker Hub using the application’s menu (macOS) or systray icon (Windows) and it is usually convenient to do this when the application starts. This will require you to use your Docker Hub username and your password. We will not actually require access to the Docker Hub until later in the course but if you can login now, you should do so.

Determining your Docker Hub username

If you no longer recall your Docker Hub username, e.g., because you have been logging into the Docker Hub using your email address, you can find out what it is through the steps:

Open https://hub.docker.com/ in a web browser window
Sign-in using your email and password (don’t tell us what it is)
In the top-right of the screen you will see your username

Once your Docker application is running, open a shell (terminal) window, and run the following command to check that Docker is installed and the command line tools are working correctly. Below is the output for a Mac version, but the specific version is unlikely to matter much: it does not have to precisely match the one listed below.

BASH

$ docker --version

OUTPUT

Docker version 20.10.5, build 55c4c88

The above command has not actually relied on the part of Docker that runs containers, just that Docker is installed and you can access it correctly from the command line.

A command that checks that Docker is working correctly is the docker container ls command (we cover this command in more detail later in the course).

Without explaining the details, output on a newly installed system would likely be:

BASH

$ docker container ls

OUTPUT

CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

(The command docker system info could also be used to verify that Docker is correctly installed and operational but it produces a larger amount of output.)

However, if you instead get a message similar to the following

OUTPUT

Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?

then you need to check that you have started the Docker Desktop, Docker Engine, or however else you worked through the setup instructions.

Getting help

Often when working with a new command line tool, we need to get help. These tools often have some sort of subcommand or flag (usually help, -h, or --help) that displays a prompt describing how to use the tool. For Docker, it’s no different. If we run docker --help, we see the following output (running docker also produces the help message):

OUTPUT


Usage:  docker [OPTIONS] COMMAND

A self-sufficient runtime for containers

Options:
      --config string      Location of client config files (default "/Users/vini/.docker")
  -c, --context string     Name of the context to use to connect to the daemon (overrides DOCKER_HOST env var and default context set with "docker context use")
  -D, --debug              Enable debug mode
  -H, --host list          Daemon socket(s) to connect to
  -l, --log-level string   Set the logging level ("debug"|"info"|"warn"|"error"|"fatal") (default "info")
      --tls                Use TLS; implied by --tlsverify
      --tlscacert string   Trust certs signed only by this CA (default "/Users/vini/.docker/ca.pem")
      --tlscert string     Path to TLS certificate file (default "/Users/vini/.docker/cert.pem")
      --tlskey string      Path to TLS key file (default "/Users/vini/.docker/key.pem")
      --tlsverify          Use TLS and verify the remote
  -v, --version            Print version information and quit

Management Commands:
  app*        Docker App (Docker Inc., v0.9.1-beta3)
  builder     Manage builds
  buildx*     Build with BuildKit (Docker Inc., v0.5.1-docker)
  config      Manage Docker configs
  container   Manage containers
  context     Manage contexts
  image       Manage images
  manifest    Manage Docker image manifests and manifest lists
  network     Manage networks
  node        Manage Swarm nodes
  plugin      Manage plugins
  scan*       Docker Scan (Docker Inc., v0.6.0)
  secret      Manage Docker secrets
  service     Manage services
  stack       Manage Docker stacks
  swarm       Manage Swarm
  system      Manage Docker
  trust       Manage trust on Docker images
  volume      Manage volumes

Commands:
  attach      Attach local standard input, output, and error streams to a running container
  build       Build an image from a Dockerfile
  commit      Create a new image from a container's changes
  cp          Copy files/folders between a container and the local filesystem
  create      Create a new container
  diff        Inspect changes to files or directories on a container's filesystem
  events      Get real time events from the server
  exec        Run a command in a running container
  export      Export a container's filesystem as a tar archive
  history     Show the history of an image
  images      List images
  import      Import the contents from a tarball to create a filesystem image
  info        Display system-wide information
  inspect     Return low-level information on Docker objects
  kill        Kill one or more running containers
  load        Load an image from a tar archive or STDIN
  login       Log in to a Docker registry
  logout      Log out from a Docker registry
  logs        Fetch the logs of a container
  pause       Pause all processes within one or more containers
  port        List port mappings or a specific mapping for the container
  ps          List containers
  pull        Pull an image or a repository from a registry
  push        Push an image or a repository to a registry
  rename      Rename a container
  restart     Restart one or more containers
  rm          Remove one or more containers
  rmi         Remove one or more images
  run         Run a command in a new container
  save        Save one or more images to a tar archive (streamed to STDOUT by default)
  search      Search the Docker Hub for images
  start       Start one or more stopped containers
  stats       Display a live stream of container(s) resource usage statistics
  stop        Stop one or more running containers
  tag         Create a tag TARGET_IMAGE that refers to SOURCE_IMAGE
  top         Display the running processes of a container
  unpause     Unpause all processes within one or more containers
  update      Update configuration of one or more containers
  version     Show the Docker version information
  wait        Block until one or more containers stop, then print their exit codes

Run 'docker COMMAND --help' for more information on a command.

There is a list of commands and the end of the help message says: Run 'docker COMMAND --help' for more information on a command. For example, take the docker container ls command that we ran previously. We can see from the Docker help prompt that container is a Docker command, so to get help for that command, we run:

BASH

docker container --help  # or instead 'docker container'

OUTPUT


Usage:  docker container COMMAND

Manage containers

Commands:
  attach      Attach local standard input, output, and error streams to a running container
  commit      Create a new image from a container's changes
  cp          Copy files/folders between a container and the local filesystem
  create      Create a new container
  diff        Inspect changes to files or directories on a container's filesystem
  exec        Run a command in a running container
  export      Export a container's filesystem as a tar archive
  inspect     Display detailed information on one or more containers
  kill        Kill one or more running containers
  logs        Fetch the logs of a container
  ls          List containers
  pause       Pause all processes within one or more containers
  port        List port mappings or a specific mapping for the container
  prune       Remove all stopped containers
  rename      Rename a container
  restart     Restart one or more containers
  rm          Remove one or more containers
  run         Run a command in a new container
  start       Start one or more stopped containers
  stats       Display a live stream of container(s) resource usage statistics
  stop        Stop one or more running containers
  top         Display the running processes of a container
  unpause     Unpause all processes within one or more containers
  update      Update configuration of one or more containers
  wait        Block until one or more containers stop, then print their exit codes

Run 'docker container COMMAND --help' for more information on a command.

There’s also help for the container ls command:

BASH

docker container ls --help  # this one actually requires the '--help' flag

OUTPUT

Usage:  docker container ls [OPTIONS]

List containers

Aliases:
  ls, ps, list

Options:
  -a, --all             Show all containers (default shows just running)
  -f, --filter filter   Filter output based on conditions provided
      --format string   Pretty-print containers using a Go template
  -n, --last int        Show n last created containers (includes all states) (default -1)
  -l, --latest          Show the latest created container (includes all states)
      --no-trunc        Don't truncate output
  -q, --quiet           Only display container IDs
  -s, --size            Display total file sizes

You may notice that there are many commands that stem from the docker command. Instead of trying to remember all possible commands and options, it’s better to learn how to effectively get help from the command line. Although we can always search the web, getting the built-in help from our tool is often much faster and may provide the answer right away. This applies not only to Docker, but also to most command line-based tools.

Docker Command Line Interface (CLI) syntax

In this lesson we use the newest Docker CLI syntax introduced with the Docker Engine version 1.13. This new syntax combines commands into groups you will most often want to interact with. In the help example above you can see image and container management commands, which can be used to interact with your images and containers respectively. With this new syntax you issue commands using the following pattern docker [command] [subcommand] [additional options]

Comparing the output of two help commands above, you can see that the same thing can be achieved in multiple ways. For example to start a Docker container using the old syntax you would use docker run. To achieve the same with the new syntax, you use docker container run instead. Even though the old approach is shorter and still officially supported, the new syntax is more descriptive, less error-prone and is therefore recommended.

Exploring a command

Run docker --help and pick a command from the list. Explore the help prompt for that command. Try to guess how a command would work by looking at the Usage: section of the prompt.

Show me the solution

Suppose we pick the docker image build command:

BASH

docker image build --help

OUTPUT

Usage:  docker image build [OPTIONS] PATH | URL | -

Build an image from a Dockerfile

Options:
     --add-host list           Add a custom host-to-IP mapping (host:ip)
     --build-arg list          Set build-time variables
     --cache-from strings      Images to consider as cache sources
     --cgroup-parent string    Optional parent cgroup for the container
     --compress                Compress the build context using gzip
     --cpu-period int          Limit the CPU CFS (Completely Fair Scheduler) period
     --cpu-quota int           Limit the CPU CFS (Completely Fair Scheduler) quota
 -c, --cpu-shares int          CPU shares (relative weight)
     --cpuset-cpus string      CPUs in which to allow execution (0-3, 0,1)
     --cpuset-mems string      MEMs in which to allow execution (0-3, 0,1)
     --disable-content-trust   Skip image verification (default true)
 -f, --file string             Name of the Dockerfile (Default is 'PATH/Dockerfile')
     --force-rm                Always remove intermediate containers
     --iidfile string          Write the image ID to the file
     --isolation string        Container isolation technology
     --label list              Set metadata for an image
 -m, --memory bytes            Memory limit
     --memory-swap bytes       Swap limit equal to memory plus swap: '-1' to enable unlimited swap
     --network string          Set the networking mode for the RUN instructions during build (default "default")
     --no-cache                Do not use cache when building the image
     --pull                    Always attempt to pull a newer version of the image
 -q, --quiet                   Suppress the build output and print image ID on success
     --rm                      Remove intermediate containers after a successful build (default true)
     --security-opt strings    Security options
     --shm-size bytes          Size of /dev/shm
 -t, --tag list                Name and optionally a tag in the 'name:tag' format
     --target string           Set the target build stage to build.
     --ulimit ulimit           Ulimit options (default [])

We could try to guess that the command could be run like this:

BASH

docker image build .

BASH

docker image build https://github.com/docker/rootfs.git

Where https://github.com/docker/rootfs.git could be any relevant URL that supports a Docker image.

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Key Points

A toolbar icon indicates that Docker is ready to use (on Windows and macOS).
You will typically interact with Docker using the command line.
To learn how to run a certain Docker command, we can type the command followed by the --help flag.

Content from Exploring and Running Containers

Last updated on 2022-11-15 | Edit this page

Overview

Questions

How do I interact with Docker containers and container images on my computer?

Objectives

Use the correct command to see which Docker container images are on your computer.
Be able to download new Docker container images.
Demonstrate how to start an instance of a container from a container image.
Describe at least two ways to execute commands inside a running Docker container.

Reminder of terminology: container images and containers

Recall that a container image is the template from which particular instances of containers will be created.

Let’s explore our first Docker container. The Docker team provides a simple container image online called hello-world. We’ll start with that one.

Downloading Docker images

The docker image command is used to interact with Docker container images. You can find out what container images you have on your computer by using the following command (“ls” is short for “list”):

BASH

$ docker image ls

If you’ve just installed Docker, you won’t see any container images listed.

To get a copy of the hello-world Docker container image from the internet, run this command:

BASH

$ docker image pull hello-world

You should see output like this:

OUTPUT

Using default tag: latest
latest: Pulling from library/hello-world
1b930d010525: Pull complete
Digest: sha256:f9dfddf63636d84ef479d645ab5885156ae030f611a56f3a7ac7f2fdd86d7e4e
Status: Downloaded newer image for hello-world:latest
docker.io/library/hello-world:latest

Docker Hub

Where did the hello-world container image come from? It came from the Docker Hub website, which is a place to share Docker container images with other people. More on that in a later episode.

Exercise: Check on Your Images

What command would you use to see if the hello-world Docker container image had downloaded successfully and was on your computer? Give it a try before checking the solution.

Show me the solution

To see if the hello-world container image is now on your computer, run:

BASH

$ docker image ls

Note that the downloaded hello-world container image is not in the folder where you are in the terminal! (Run ls by itself to check.) The container image is not a file like our normal programs and documents; Docker stores it in a specific location that isn’t commonly accessed, so it’s necessary to use the special docker image command to see what Docker container images you have on your computer.

Running the `hello-world` container

To create and run containers from named Docker container images you use the docker container run command. Try the following docker container run invocation. Note that it does not matter what your current working directory is.

BASH

$ docker container run hello-world

OUTPUT

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
    (amd64)
 3. The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

What just happened? When we use the docker container run command, Docker does three things:

1. Starts a Running Container	2. Performs Default Action	3. Shuts Down the Container
Starts a running container, based on the container image. Think of this as the “alive” or “inflated” version of the container – it’s actually doing something.	If the container has a default action set, it will perform that default action. This could be as simple as printing a message (as above) or running a whole analysis pipeline!	Once the default action is complete, the container stops running (or exits). The container image is still there, but nothing is actively running.

The hello-world container is set up to run an action by default – namely to print this message.

Using `docker container run` to get the image

We could have skipped the docker image pull step; if you use the docker container run command and you don’t already have a copy of the Docker container image, Docker will automatically pull the container image first and then run it.

Running a container with a chosen command

But what if we wanted to do something different with the container? The output just gave us a suggestion of what to do – let’s use a different Docker container image to explore what else we can do with the docker container run command. The suggestion above is to use ubuntu, but we’re going to run a different type of Linux, alpine instead because it’s quicker to download.

Run the Alpine Docker container

Try downloading the alpine container image and using it to run a container. You can do it in two steps, or one. What are they?

What happened when you ran the Alpine Docker container?

BASH

$ docker container run alpine

If you have never used the alpine Docker container image on your computer, Docker probably printed a message that it couldn’t find the container image and had to download it. If you used the alpine container image before, the command will probably show no output. That’s because this particular container is designed for you to provide commands yourself. Try running this instead:

BASH

$ docker container run alpine cat /etc/os-release

You should see the output of the cat /etc/os-release command, which prints out the version of Alpine Linux that this container is using and a few additional bits of information.

Hello World, Part 2

Can you run a copy of the alpine container and make it print a “hello world” message?

Give it a try before checking the solution.

Show me the solution

Use the same command as above, but with the echo command to print a message.

BASH

$ docker container run alpine echo 'Hello World'

So here, we see another option – we can provide commands at the end of the docker container run command and they will execute inside the running container.

Running containers interactively

In all the examples above, Docker has started the container, run a command, and then immediately stopped the container. But what if we wanted to keep the container running so we could log into it and test drive more commands? The way to do this is by adding the interactive flags -i and -t (usually combined as -it) to the docker container run command and provide a shell (bash,sh, etc.) as our command. The alpine Docker container image doesn’t include bash so we need to use sh.

BASH

$ docker container run -it alpine sh

Technically…

Technically, the interactive flag is just -i – the extra -t (combined as -it above) is the “pseudo-TTY” option, a fancy term that means a text interface. This allows you to connect to a shell, like sh, using a command line. Since you usually want to have a command line when running interactively, it makes sense to use the two together.

Your prompt should change significantly to look like this:

BASH

/ #

That’s because you’re now inside the running container! Try these commands:

pwd
ls
whoami
echo $PATH
cat /etc/os-release

All of these are being run from inside the running container, so you’ll get information about the container itself, instead of your computer. To finish using the container, type exit.

BASH

/ # exit

Practice Makes Perfect

Can you find out the version of Ubuntu installed on the ubuntu container image? (Hint: You can use the same command as used to find the version of alpine.)

Can you also find the apt-get program? What does it do? (Hint: try passing --help to almost any command will give you more information.)

Solution 1 – Interactive

Run an interactive busybox container – you can use docker image pull first, or just run it with this command:

BASH

$ docker container run -it ubuntu sh

OR you can get the bash shell instead

BASH

$ docker container run -it ubuntu bash

Then try, running these commands

BASH

/# cat /etc/os-release
/# apt-get --help

Exit when you’re done.

BASH

/# exit

Solution 2 – Run commands

Run a ubuntu container, first with a command to read out the Linux version:

BASH

$ docker container run ubuntu cat /etc/os-release

Then run a container with a command to print out the apt-get help:

BASH

$ docker container run ubuntu apt-get --help

Even More Options

There are many more options, besides -it that can be used with the docker container run command! A few of them will be covered in later episodes and we’ll share two more common ones here:

--rm: this option guarantees that any running container is completely removed from your computer after the container is stopped. Without this option, Docker actually keeps the “stopped” container around, which you’ll see in a later episode. Note that this option doesn’t impact the container images that you’ve pulled, just running instances of containers.
--name=: By default, Docker assigns a random name and ID number to each container instance that you run on your computer. If you want to be able to more easily refer to a specific running container, you can assign it a name using this option.

Conclusion

So far, we’ve seen how to download Docker container images, use them to run commands inside running containers, and even how to explore a running container from the inside. Next, we’ll take a closer look at all the different kinds of Docker container images that are out there.

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Key Points

The docker image pull command downloads Docker container images from the internet.
The docker image ls command lists Docker container images that are (now) on your computer.
The docker container run command creates running containers from container images and can run commands inside them.
When using the docker container run command, a container can run a default action (if it has one), a user specified action, or a shell to be used interactively.

Content from Cleaning Up Containers

Last updated on 2022-11-15 | Edit this page

Removing images

Overview

Questions

How do I interact with a Docker container on my computer?
How do I manage my containers and container images?

Objectives

Explain how to list running and completed containers.
Know how to list and remove container images.

The container images and their corresponding containers can start to take up a lot of disk space if you don’t clean them up occasionally, so it’s a good idea to periodically remove containers and container images that you won’t be using anymore.

In order to remove a specific container image, you need to find out details about the container image, specifically, the “Image ID”. For example, say my laptop contained the following container image:

BASH

$ docker image ls

OUTPUT

REPOSITORY       TAG         IMAGE ID       CREATED          SIZE
hello-world      latest      fce289e99eb9   15 months ago    1.84kB

You can remove the container image with a docker image rm command that includes the Image ID, such as:

BASH

$ docker image rm fce289e99eb9

or use the container image name, like so:

BASH

$ docker image rm hello-world

However, you may see this output:

OUTPUT

Error response from daemon: conflict: unable to remove repository reference "hello-world" (must force) - container e7d3b76b00f4 is using its referenced image fce289e99eb9

This happens when Docker hasn’t cleaned up some of the previously running containers based on this container image. So, before removing the container image, we need to be able to see what containers are currently running, or have been run recently, and how to remove these.

What containers are running?

Working with containers, we are going to shift back to the command: docker container. Similar to docker image, we can list running containers by typing:

BASH

$ docker container ls

OUTPUT

CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

Notice that this command didn’t return any containers because our containers all exited and thus stopped running after they completed their work.

`docker ps`

The command docker ps serves the same purpose as docker container ls, and comes from the Unix shell command ps which describes running processes.

What containers have run recently?

There is also a way to list running containers, and those that have completed recently, which is to add the --all/-a flag to the docker container ls command as shown below.

BASH

$ docker container ls --all

OUTPUT

CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS                     PORTS               NAMES
9c698655416a        hello-world         "/hello"            2 minutes ago       Exited (0) 2 minutes ago                       zen_dubinsky
6dd822cf6ca9        hello-world         "/hello"            3 minutes ago       Exited (0) 3 minutes ago                       eager_engelbart

Keeping it clean

You might be surprised at the number of containers Docker is still keeping track of. One way to prevent this from happening is to add the --rm flag to docker container run. This will completely wipe out the record of the run container when it exits. If you need a reference to the running container for any reason, don’t use this flag.

How do I remove an exited container?

To delete an exited container you can run the following command, inserting the CONTAINER ID for the container you wish to remove. It will repeat the CONTAINER ID back to you, if successful.

BASH

$ docker container rm 9c698655416a

OUTPUT

9c698655416a

An alternative option for deleting exited containers is the docker container prune command. Note that this command doesn’t accept a container ID as an option because it deletes ALL exited containers! Be careful with this command as deleting the container is forever. Once a container is deleted you can not get it back. If you have containers you may want to reconnect to, you should not use this command. It will ask you if to confirm you want to remove these containers, see output below. If successful it will print the full CONTAINER ID back to you for each container it has removed.

BASH

$ docker container prune

OUTPUT

WARNING! This will remove all stopped containers.
Are you sure you want to continue? [y/N] y
Deleted Containers:
9c698655416a848278d16bb1352b97e72b7ea85884bff8f106877afe0210acfc
6dd822cf6ca92f3040eaecbd26ad2af63595f30bb7e7a20eacf4554f6ccc9b2b

Removing images, for real this time

Now that we’ve removed any potentially running or stopped containers, we can try again to delete the hello-world container image.

BASH

$ docker image rm hello-world

OUTPUT

Untagged: hello-world:latest
Untagged: hello-world@sha256:5f179596a7335398b805f036f7e8561b6f0e32cd30a32f5e19d17a3cda6cc33d
Deleted: sha256:fce289e99eb9bca977dae136fbe2a82b6b7d4c372474c9235adc1741675f587e
Deleted: sha256:af0b15c8625bb1938f1d7b17081031f649fd14e6b233688eea3c5483994a66a3

The reason that there are a few lines of output, is that a given container image may have been formed by merging multiple underlying layers. Any layers that are used by multiple Docker container images will only be stored once. Now the result of docker image ls should no longer include the hello-world container image.

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Key Points

docker container has subcommands used to interact and manage containers.
docker image has subcommands used to interact and manage container images.
docker container ls or docker ps can provide information on currently running containers.

Content from Finding Containers on Docker Hub

Last updated on 2022-11-15 | Edit this page

In the previous episode, we ran a few different containers derived from different container images: hello-world, alpine, and maybe busybox. Where did these container images come from? The Docker Hub!

Overview

Questions

What is the Docker Hub, and why is it useful?

Objectives

Explain how the Docker Hub augments Docker use.
Explore the Docker Hub webpage for a popular Docker container image.
Find the list of tags for a particular Docker container image.
Identify the three components of a container image’s identifier.

Introducing the Docker Hub

The Docker Hub is an online repository of container images, a vast number of which are publicly available. A large number of the container images are curated by the developers of the software that they package. Also, many commonly used pieces of software that have been containerized into images are officially endorsed, which means that you can trust the container images to have been checked for functionality, stability, and that they don’t contain malware.

Docker can be used without connecting to the Docker Hub

Note that while the Docker Hub is well integrated into Docker functionality, the Docker Hub is certainly not required for all types of use of Docker containers. For example, some organizations may run container infrastructure that is entirely disconnected from the Internet.

Exploring an Example Docker Hub Page

As an example of a Docker Hub page, let’s explore the page for the official Python language container images. The most basic form of containerized Python is in the python container image (which is endorsed by the Docker team). Open your web browser to https://hub.docker.com/_/python to see what is on a typical Docker Hub software page.

The top-left provides information about the name, short description, popularity (i.e., more than a billion downloads in the case of this container image), and endorsements.

The top-right provides the command to pull this container image to your computer.

The main body of the page contains many used headings, such as:

Which tags (i.e., container image versions) are supported;
Summary information about where to get help, which computer architectures are supported, etc.;
A longer description of the container image;
Examples of how to use the container image; and
The license that applies.

The “How to use the image” section of most container images’ pages will provide examples that are likely to cover your intended use of the container image.

Exploring Container Image Versions

A single Docker Hub page can have many different versions of container images, based on the version of the software inside. These versions are indicated by “tags”. When referring to the specific version of a container image by its tag, you use a colon, :, like this:

CONTAINER_IMAGE_NAME:TAG

So if I wanted to download the python container image, with Python 3.8, I would use this name:

BASH

$ docker image pull python:3.8

But if I wanted to download a Python 3.6 container image, I would use this name:

BASH

$ docker image pull python:3.6

The default tag (which is used if you don’t specify one) is called latest.

So far, we’ve only seen container images that are maintained by the Docker team. However, it’s equally common to use container images that have been produced by individual owners or organizations. Container images that you create and upload to Docker Hub would fall into this category, as would the container images maintained by organizations like ContinuumIO (the folks who develop the Anaconda Python environment) or community groups like rocker, a group that builds community R container images.

The name for these group- or individually-managed container images have this format:

OWNER/CONTAINER_IMAGE_NAME:TAG

Repositories

The technical name for the contents of a Docker Hub page is a “repository.” The tag indicates the specific version of the container image that you’d like to use from a particular repository. So a slightly more accurate version of the above example is:

OWNER/REPOSITORY:TAG

What’s in a name?

How would I download the Docker container image produced by the rocker group that has version 3.6.1 of R and the tidyverse installed?

Note: the container image described in this exercise is large and won’t be used later in this lesson, so you don’t actually need to pull the container image – constructing the correct docker pull command is sufficient.

Show me the solution

First, search for rocker in Docker Hub. Then look for their tidyverse container image. You can look at the list of tags, or just guess that the tag is 3.6.1. Altogether, that means that the name of the container image we want to download is:

BASH

$ docker image pull rocker/tidyverse:3.6.1

Finding Container Images on Docker Hub

There are many different container images on Docker Hub. This is where the real advantage of using containers shows up – each container image represents a complete software installation that you can use and access without any extra work!

The easiest way to find container images is to search on Docker Hub, but sometimes software pages have a link to their container images from their home page.

Note that anyone can create an account on Docker Hub and share container images there, so it’s important to exercise caution when choosing a container image on Docker Hub. These are some indicators that a container image on Docker Hub is consistently maintained, functional and secure:

The container image is updated regularly.
The container image associated with a well established company, community, or other group that is well-known.
There is a Dockerfile or other listing of what has been installed to the container image.
The container image page has documentation on how to use the container image.

If a container image is never updated, created by a random person, and does not have a lot of metadata, it is probably worth skipping over. Even if such a container image is secure, it is not reproducible and not a dependable way to run research computations.

What container image is right for you?

Find a Docker container image that’s relevant to you. Take into account the suggestions above of what to look for as you evaluate options. If you’re unsuccessful in your search, or don’t know what to look for, you can use the R or Python container image we’ve already seen.

Once you find a container image, use the skills from the previous episode to download the container image and explore it.

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Key Points

The Docker Hub is an online repository of container images.
Many Docker Hub container images are public, and may be officially endorsed.
Each Docker Hub page about a container image provides structured information and subheadings
Most Docker Hub pages about container images contain sections that provide examples of how to use those container images.
Many Docker Hub container images have multiple versions, indicated by tags.
The naming convention for Docker container images is: OWNER/CONTAINER_IMAGE_NAME:TAG

Content from Creating Your Own Container Images

Last updated on 2022-11-15 | Edit this page

There are lots of reasons why you might want to create your own Docker container image.

Overview

Questions

How can I make my own Docker container images?
How do I document the ‘recipe’ for a Docker container image?

Objectives

Explain the purpose of a Dockerfile and show some simple examples.
Demonstrate how to build a Docker container image from a Dockerfile.
Compare the steps of creating a container image interactively versus a Dockerfile.
Create an installation strategy for a container image.
Demonstrate how to upload (‘push’) your container images to the Docker Hub.
Describe the significance of the Docker Hub naming scheme.

You can’t find a container image with all the tools you need on Docker Hub.
You want to have a container image to “archive” all the specific software versions you ran for a project.
You want to share your workflow with someone else.

Interactive installation

Before creating a reproducible installation, let’s experiment with installing software inside a container. Start a container from the alpine container image we used before, interactively:

BASH

$ docker container run -it alpine sh

Because this is a basic container, there’s a lot of things not installed – for example, python3.

BASH

/# python3

OUTPUT

sh: python3: not found

Inside the container, we can run commands to install Python 3. The Alpine version of Linux has a installation tool called apk that we can use to install Python 3.

BASH

/# apk add --update python3 py3-pip python3-dev

We can test our installation by running a Python command:

BASH

/# python3 --version

Once Python is installed, we can add Python packages using the pip package installer:

BASH

/# pip install cython

Exercise: Searching for Help

Can you find instructions for installing R on Alpine Linux? Do they work?

Show me the solution

A quick search should hopefully show that the way to install R on Alpine Linux is:

BASH

/# apk add R

Once we exit, these changes are not saved to a new container image by default. There is a command that will “snapshot” our changes, but building container images this way is not easily reproducible. Instead, we’re going to take what we’ve learned from this interactive installation and create our container image from a reproducible recipe, known as a Dockerfile.

If you haven’t already, exit out of the interactively running container.

BASH

/# exit

Put installation instructions in a `Dockerfile`

A Dockerfile is a plain text file with keywords and commands that can be used to create a new container image.

From your shell, go to the folder you downloaded at the start of the lesson and print out the Dockerfile inside:

BASH

$ cd ~/Desktop/docker-intro/basic
$ cat Dockerfile

OUTPUT

FROM <EXISTING IMAGE>
RUN <INSTALL CMDS FROM SHELL>
RUN <INSTALL CMDS FROM SHELL>
CMD <CMD TO RUN BY DEFAULT>

Let’s break this file down:

The first line, FROM, indicates which container image we’re starting with. It is the “base” container image we are going to start from.
The next two lines RUN, will indicate installation commands we want to run. These are the same commands that we used interactively above.
The last line, CMD, indicates the default command we want a container based on this container image to run, if no other command is provided. It is recommended to provide CMD in exec-form (see the CMD section of the Dockerfile documentation for more details). It is written as a list which contains the executable to run as its first element, optionally followed by any arguments as subsequent elements. The list is enclosed in square brackets ([]) and its elements are double-quoted (") strings which are separated by commas. For example, CMD ["ls", "-lF", "--color", "/etc"] would translate to ls -lF --color /etc.

shell-form and exec-form for CMD

Another way to specify the parameter for the CMD instruction is the shell-form. Here you type the command as you would call it from the command line. Docker then silently runs this command in the image’s standard shell. CMD cat /etc/passwd is equivalent to CMD ["/bin/sh", "-c", "cat /etc/passwd"]. We recommend to prefer the more explicit exec-form because we will be able to create more flexible container image command options and make sure complex commands are unambiguous in this format.

Exercise: Take a Guess

Do you have any ideas about what we should use to fill in the sample Dockerfile to replicate the installation we did above?

Solution:

Based on our experience above, edit the Dockerfile (in your text editor of choice) to look like this:

FROM alpine
RUN apk add --update python3 py3-pip python3-dev
RUN pip install cython
CMD ["python3", "--version"]

The recipe provided by the Dockerfile shown in the solution to the preceding exercise will use Alpine Linux as the base container image, add Python 3 and the Cython library, and set a default command to request Python 3 to report its version information.

Create a new Docker image

So far, we only have a text file named Dockerfile – we do not yet have a container image. We want Docker to take this Dockerfile, run the installation commands contained within it, and then save the resulting container as a new container image. To do this we will use the docker image build command.

We have to provide docker image build with two pieces of information:

the location of the Dockerfile
the name of the new container image. Remember the naming scheme from before? You should name your new image with your Docker Hub username and a name for the container image, like this: USERNAME/CONTAINER_IMAGE_NAME.

All together, the build command that you should run on your computer, will have a similar structure to this:

BASH

$ docker image build -t USERNAME/CONTAINER_IMAGE_NAME .

The -t option names the container image; the final dot indicates that the Dockerfile is in our current directory.

For example, if my user name was alice and I wanted to call my container image alpine-python, I would use this command:

BASH

$ docker image build -t alice/alpine-python .

Build Context

Notice that the final input to docker image build isn’t the Dockerfile – it’s a directory! In the command above, we’ve used the current working directory (.) of the shell as the final input to the docker image build command. This option provides what is called the build context to Docker – if there are files being copied into the built container image more details in the next episode they’re assumed to be in this location. Docker expects to see a Dockerfile in the build context also (unless you tell it to look elsewhere).

Even if it won’t need all of the files in the build context directory, Docker does “load” them before starting to build, which means that it’s a good idea to have only what you need for the container image in a build context directory, as we’ve done in this example.

Exercise: Review!

Think back to earlier. What command can you run to check if your container image was created successfully? (Hint: what command shows the container images on your computer?)
We didn’t specify a tag for our container image name. What tag did Docker automatically use?
What command will run a container based on the container image you’ve created? What should happen by default if you run such a container? Can you make it do something different, like print “hello world”?

Show me the solution

To see your new image, run docker image ls. You should see the name of your new container image under the “REPOSITORY” heading.
In the output of docker image ls, you can see that Docker has automatically used the latest tag for our new container image.
We want to use docker container run to run a container based on a container image.

The following command should run a container and print out our default message, the version of Python:

BASH

$ docker container run alice/alpine-python

To run a container based on our container image and print out “Hello world” instead:

BASH

$ docker container run alice/alpine-python echo "Hello World"

While it may not look like you have achieved much, you have already effected the combination of a lightweight Linux operating system with your specification to run a given command that can operate reliably on macOS, Microsoft Windows, Linux and on the cloud!

Boring but important notes about installation

There are a lot of choices when it comes to installing software – sometimes too many! Here are some things to consider when creating your own container image:

Start smart, or, don’t install everything from scratch! If you’re using Python as your main tool, start with a Python container image. Same with R. We’ve used Alpine Linux as an example in this lesson, but it’s generally not a good container image to start with for initial development and experimentation because it is a less common distribution of Linux; using Ubuntu, Debian and CentOS are all good options for scientific software installations. The program you’re using might recommend a particular distribution of Linux, and if so, it may be useful to start with a container image for that distribution.
How big? How much software do you really need to install? When you have a choice, lean towards using smaller starting container images and installing only what’s needed for your software, as a bigger container image means longer download times to use.
Know (or Google) your Linux. Different distributions of Linux often have distinct sets of tools for installing software. The apk command we used above is the software package installer for Alpine Linux. The installers for various common Linux distributions are listed below:
- Ubuntu: apt or apt-get
- Debian: deb
- CentOS: yum Most common software installations are available to be installed via these tools. A web search for “install X on Y Linux” is usually a good start for common software installation tasks; if something isn’t available via the Linux distribution’s installation tools, try the options below.
Use what you know. You’ve probably used commands like pip or install.packages() before on your own computer – these will also work to install things in container images (if the basic scripting language is installed).
README. Many scientific software tools have a README or installation instructions that lay out how to install software. You want to look for instructions for Linux. If the install instructions include options like those suggested above, try those first.

In general, a good strategy for installing software is:

Make a list of what you want to install.
Look for pre-existing container images.
Read through instructions for software you’ll need to install.
Try installing everything interactively in your base container – take notes!
From your interactive installation, create a Dockerfile and then try to build the container image from that.

Container images that you release publicly can be stored on the Docker Hub for free. If you name your container image as described above, with your Docker Hub username, all you need to do is run the opposite of docker image pull – docker image push.

BASH

$ docker image push alice/alpine-python

Make sure to substitute the full name of your container image!

In a web browser, open https://hub.docker.com, and on your user page you should now see your container image listed, for anyone to use or build on.

Logging In

Technically, you have to be logged into Docker on your computer for this to work. Usually it happens by default, but if docker image push doesn’t work for you, run docker login first, enter your Docker Hub username and password, and then try docker image push again.

What’s in a name? (again)

You don’t have to name your containers images using the USERNAME/CONTAINER_IMAGE_NAME:TAG naming scheme. On your own computer, you can call container images whatever you want, and refer to them by the names you choose. It’s only when you want to share a container image that it needs the correct naming format.

You can rename container images using the docker image tag command. For example, imagine someone named Alice has been working on a workflow container image and called it workflow-test on her own computer. She now wants to share it in her alice Docker Hub account with the name workflow-complete and a tag of v1. Her docker image tag command would look like this:

BASH

$ docker image tag workflow-test alice/workflow-complete:v1

She could then push the re-named container image to Docker Hub, using docker image push alice/workflow-complete:v1

Key Points

Dockerfiles specify what is within Docker container images.
The docker image build command is used to build a container image from a Dockerfile.
You can share your Docker container images through the Docker Hub so that others can create Docker containers from your container images.

Content from Creating More Complex Container Images

Last updated on 2022-11-15 | Edit this page

In order to create and use your own container images, you may need more information than our previous example. You may want to use files from outside the container, that are not included within the container image, either by copying the files into the container image, or by making them visible within a running container from their existing location on your host system. You may also want to learn a little bit about how to install software within a running container or a container image. This episode will look at these advanced aspects of running a container or building a container image. Note that the examples will get gradually more and more complex – most day-to-day use of containers and container images can be accomplished using the first 1–2 sections on this page.

Overview

Questions

How can I make more complex container images?

Objectives

Explain how you can include files within Docker container images when you build them.
Explain how you can access files on the Docker host from your Docker containers.

Using scripts and files from outside the container

In your shell, change to the sum folder in the docker-intro folder and look at the files inside.

BASH

$ cd ~/Desktop/docker-intro/sum
$ ls

This folder has both a Dockerfile and a Python script called sum.py. Let’s say we wanted to try running the script using a container based on our recently created alpine-python container image.

Running containers

What command would we use to run Python from the alpine-python container?

If we try running the container and Python script, what happens?

BASH

$ docker container run alice/alpine-python python3 sum.py

OUTPUT

python3: can't open file 'sum.py': [Errno 2] No such file or directory

No such file or directory

What does the error message mean? Why might the Python inside the container not be able to find or open our script?

The problem here is that the container and its filesystem is separate from our host computer’s filesystem. When the container runs, it can’t see anything outside itself, including any of the files on our computer. In order to use Python (inside the container) and our script (outside the container, on our host computer), we need to create a link between the directory on our computer and the container.

This link is called a “mount” and is what happens automatically when a USB drive or other external hard drive gets connected to a computer – you can see the contents appear as if they were on your computer.

We can create a mount between our computer and the running container by using an additional option to docker container run. We’ll also use the variable ${PWD} which will substitute in our current working directory. The option will look like this

--mount type=bind,source=${PWD},target=/temp

What this means is: make my current working directory (on the host computer) – the source – visible within the container that is about to be started, and inside this container, name the directory /temp – the target.

Types of mounts

You will notice that we set the mount type=bind, there are other types of mount that can be used in Docker (e.g. volume and tmpfs). We do not cover other types of mounts or the differences between these mount types in the course as it is more of an advanced topic. You can find more information on the different mount types in the Docker documentation.

Let’s try running the command now:

BASH

$ docker container run --mount type=bind,source=${PWD},target=/temp alice/alpine-python python3 sum.py

But we get the same error!

OUTPUT

python3: can't open file 'sum.py': [Errno 2] No such file or directory

This final piece is a bit tricky – we really have to remember to put ourselves inside the container. Where is the sum.py file? It’s in the directory that’s been mapped to /temp – so we need to include that in the path to the script. This command should give us what we need:

BASH

$ docker container run --mount type=bind,source=${PWD},target=/temp alice/alpine-python python3 /temp/sum.py

Note that if we create any files in the /temp directory while the container is running, these files will appear on our host filesystem in the original directory and will stay there even when the container stops.

Other Commonly Used Docker Run Flags

Docker run has many other useful flags to alter its function. A couple that are commonly used include -w and -u.

The --workdir/-w flag sets the working directory a.k.a. runs the command being executed inside the directory specified. For example, the following code would run the pwd command in a container started from the latest ubuntu image in the /home/alice directory and print /home/alice. If the directory doesn’t exist in the image it will create it.

docker container run -w /home/alice/ ubuntu pwd

The --user/-u flag lets you specify the username you would like to run the container as. This is helpful if you’d like to write files to a mounted folder and not write them as root but rather your own user identity and group. A common example of the -u flag is --user $(id -u):$(id -g) which will fetch the current user’s ID and group and run the container as that user.

Exercise: Explore the script

What happens if you use the docker container run command above and put numbers after the script name?

Show me the solution

This script comes from the Python Wiki and is set to add all numbers that are passed to it as arguments.

Exercise: Checking the options

Our Docker command has gotten much longer! Can you go through each piece of the Docker command above and explain what it does? How would you characterize the key components of a Docker command?

Show me the solution

Here’s a breakdown of each piece of the command above

docker container run: use Docker to run a container
--mount type=bind,source=${PWD},target=/temp: connect my current working directory (${PWD}) as a folder inside the container called /temp
alice/alpine-python: name of the container image to use to run the container
python3 /temp/sum.py: what commands to run in the container

More generally, every Docker command will have the form: docker [action] [docker options] [docker container image] [command to run inside]

Exercise: Interactive jobs

Try using the directory mount option but run the container interactively. Can you find the folder that’s connected to your host computer? What’s inside?

Show me the solution

The docker command to run the container interactively is:

BASH

$ docker container run --mount type=bind,source=${PWD},target=/temp -it alice/alpine-python sh

Once inside, you should be able to navigate to the /temp folder and see that’s contents are the same as the files on your host computer:

BASH

/# cd /temp
/# ls

Mounting a directory can be very useful when you want to run the software inside your container on many different input files. In other situations, you may want to save or archive an authoritative version of your data by adding it to the container image permanently. That’s what we will cover next.

Including your scripts and data within a container image

Our next project will be to add our own files to a container image – something you might want to do if you’re sharing a finished analysis or just want to have an archived copy of your entire analysis including the data. Let’s assume that we’ve finished with our sum.py script and want to add it to the container image itself.

In your shell, you should still be in the sum folder in the docker-intro folder.

BASH

$ pwd

BASH

$ /Users/yourname/Desktop/docker-intro/sum

Let’s add a new line to the Dockerfile we’ve been using so far to create a copy of sum.py. We can do so by using the COPY keyword.

COPY sum.py /home

This line will cause Docker to copy the file from your computer into the container’s filesystem. Let’s build the container image like before, but give it a different name:

BASH

$ docker image build -t alice/alpine-sum .

The Importance of Command Order in a Dockerfile

When you run docker build it executes the build in the order specified in the Dockerfile. This order is important for rebuilding and you typically will want to put your RUN commands before your COPY commands.

Docker builds the layers of commands in order. This becomes important when you need to rebuild container images. If you change layers later in the Dockerfile and rebuild the container image, Docker doesn’t need to rebuild the earlier layers but will instead used a stored (called “cached”) version of those layers.

For example, in an instance where you wanted to copy multiply.py into the container image instead of sum.py. If the COPY line came before the RUN line, it would need to rebuild the whole image. If the COPY line came second then it would use the cached RUN layer from the previous build and then only rebuild the COPY layer.

Exercise: Did it work?

Can you remember how to run a container interactively? Try that with this one. Once inside, try running the Python script.

Show me the solution

You can start the container interactively like so:

BASH

$ docker container run -it alice/alpine-sum sh

You should be able to run the python command inside the container like this:

BASH

/# python3 /home/sum.py

This COPY keyword can be used to place your own scripts or own data into a container image that you want to publish or use as a record. Note that it’s not necessarily a good idea to put your scripts inside the container image if you’re constantly changing or editing them. Then, referencing the scripts from outside the container is a good idea, as we did in the previous section. You also want to think carefully about size – if you run docker image ls you’ll see the size of each container image all the way on the right of the screen. The bigger your container image becomes, the harder it will be to easily download.

Callout

## Security warning Login credentials including passwords, tokens, secure access tokens or other secrets must never be stored in a container. If secrets are stored, they are at high risk to be found and exploited when made public.

Copying alternatives

Another trick for getting your own files into a container image is by using the RUN keyword and downloading the files from the internet. For example, if your code is in a GitHub repository, you could include this statement in your Dockerfile to download the latest version every time you build the container image:

RUN git clone https://github.com/alice/mycode

Similarly, the wget command can be used to download any file publicly available on the internet:

RUN wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.10.0/ncbi-blast-2.10.0+-x64-linux.tar.gz

Note that the above RUN examples depend on commands (git and wget respectively) that must be available within your container: Linux distributions such as Alpine may require you to install such commands before using them within RUN statements.

More fancy `Dockerfile` options (optional, for presentation or as exercises)

We can expand on the example above to make our container image even more “automatic”. Here are some ideas:

Make the `sum.py` script run automatically

FROM alpine
RUN apk add --update python3 py3-pip python3-dev
COPY sum.py /home

# Run the sum.py script as the default command
CMD ["python3", "/home/sum.py"]

Build and test it:

BASH

$ docker image build -t alpine-sum:v1 .
$ docker container run alpine-sum:v1

You’ll notice that you can run the container without arguments just fine, resulting in sum = 0, but this is boring. Supplying arguments however doesn’t work:

BASH

docker container run alpine-sum:v1 10 11 12

results in

OUTPUT

docker: Error response from daemon: OCI runtime create failed:
container_linux.go:349: starting container process caused "exec:
\"10\": executable file not found in $PATH": unknown.

This is because the arguments 10 11 12 are interpreted as a command that replaces the default command given by CMD ["python3", "/home/sum.py"] in the image.

To achieve the goal of having a command that always runs when a container is run from the container image and can be passed the arguments given on the command line, use the keyword ENTRYPOINT in the Dockerfile.

FROM alpine

COPY sum.py /home
RUN apk add --update python3 py3-pip python3-dev

# Run the sum.py script as the default command and
# allow people to enter arguments for it
ENTRYPOINT ["python3", "/home/sum.py"]

# Give default arguments, in case none are supplied on
# the command-line
CMD ["10", "11"]

Build and test it:

BASH

$ docker image build -t alpine-sum:v2 .
# Most of the time you are interested in the sum of 10 and 11:
$ docker container run alpine-sum:v2
# Sometimes you have more challenging calculations to do:
$ docker container run alpine-sum:v2 12 13 14

Overriding the ENTRYPOINT

Sometimes you don’t want to run the image’s ENTRYPOINT. For example if you have a specialized container image that does only sums, but you need an interactive shell to examine the container:

BASH

$ docker container run -it alpine-sum:v2 /bin/sh

will yield

OUTPUT

Please supply integer arguments

You need to override the ENTRYPOINT statement in the container image like so:

BASH

$ docker container run -it --entrypoint /bin/sh alpine-sum:v2

Add the `sum.py` script to the `PATH` so you can run it directly:

FROM alpine

RUN apk add --update python3 py3-pip python3-dev

COPY sum.py /home
# set script permissions
RUN chmod +x /home/sum.py
# add /home folder to the PATH
ENV PATH /home:$PATH

Build and test it:

BASH

$ docker image build -t alpine-sum:v3 .
$ docker container run alpine-sum:v3 sum.py 1 2 3 4

Best practices for writing Dockerfiles

Take a look at Nüst et al.’s “Ten simple rules for writing Dockerfiles for reproducible data science” [1] for some great examples of best practices to use when writing Dockerfiles. The GitHub repository associated with the paper also has a set of example Dockerfiles demonstrating how the rules highlighted by the paper can be applied.

[1] Nüst D, Sochat V, Marwick B, Eglen SJ, Head T, et al. (2020) Ten simple rules for writing Dockerfiles for reproducible data science. PLOS Computational Biology 16(11): e1008316. https://doi.org/10.1371/journal.pcbi.1008316

Key Points

Docker allows containers to read and write files from the Docker host.
You can include files from your Docker host into your Docker container images by using the COPY instruction in your Dockerfile.

Content from Examples of Using Container Images in Practice

Last updated on 2022-11-15 | Edit this page

Now that we have learned the basics of working with Docker container images and containers, let’s apply what we learned to an example workflow.

Overview

Questions

How can I use Docker for my own work?

Objectives

Use existing container images and Docker in a research project.

You may choose one or more of the following examples to practice using containers.

Jekyll Website Example

In this Jekyll Website example, you can practice rendering this lesson website on your computer using the Jekyll static website generator in a Docker container. Rendering the website in a container avoids a complicated software installation; instead of installing Jekyll and all the other tools needed to create the final website, all the work can be done in the container. Additionally, when you no longer need to render the website, you can easily and cleanly remove the software from your computer.

GitHub Actions Example

In this GitHub Actions example, you can learn more about continuous integration in the cloud and how you can use container images with GitHub to automate repetitive tasks like testing code or deploying websites.

{% comment %}

{% endcomment %}

Using Containers on an HPC Cluster

It is possible to run containers on shared computing systems run by a university or national computing center. As a researcher, you can build container images and test containers on your own computer and then run your full-scale computing work on a shared computing system like a high performance cluster or high throughput grid.

The catch? Most university and national computing centers do not support running containers with Docker commands, and instead use a similar tool called Singularity or Shifter. However, both of these programs can be used to run containers based on Docker container images, so often people create their container image as a Docker container image, so they can run it using either of Docker or Singularity.

There isn’t yet a working example of how to use Docker container images on a shared computing system, partially because each system is slightly different, but the following resources show what it can look like:

Introduction to Singularity: See the episode titled “Running MPI parallel jobs using Singularity containers”
Container Workflows at Pawsey: See the episode titled “Run containers on HPC with Shifter (and Singularity)”

Seeking Examples

Do you have another example of using Docker in a workflow related to your field? Please [open a lesson issue] or submit a pull request to add it to this episode and the extras section of the lesson.

Key Points

There are many ways you might use Docker and existing container images in your research project.

Content from Containers in Research Workflows: Reproducibility and Granularity

Last updated on 2022-11-15 | Edit this page

Although this workshop is titled “Reproducible computational environments using containers”, so far we have mostly covered the mechanics of using Docker with only passing reference to the reproducibility aspects. In this section, we discuss these aspects in more detail.

Overview

Questions

How can I use container images to make my research more reproducible?
How do I incorporate containers into my research workflow?

Objectives

Understand how container images can help make research more reproducible.
Understand what practical steps I can take to improve the reproducibility of my research using containers.

Work in progress…

Note that reproducibility aspects of software and containers are an active area of research, discussion and development so are subject to many changes. We will present some ideas and approaches here but best practices will likely evolve in the near future.

Reproducibility

By reproducibility here we mean the ability of someone else (or your future self) being able to reproduce what you did computationally at a particular time (be this in research, analysis or something else) as closely as possible even if they do not have access to exactly the same hardware resources that you had when you did the original work.

Some examples of why containers are an attractive technology to help with reproducibility include:

The same computational work can be run across multiple different technologies seamlessly (e.g. Windows, macOS, Linux).
You can save the exact process that you used for your computational work (rather than relying on potentially incomplete notes).
You can save the exact versions of software and their dependencies in the container image.
You can access legacy versions of software and underlying dependencies which may not be generally available any more.
Depending on their size, you can also potentially store a copy of key data within the container image.
You can archive and share the container image as well as associating a persistent identifier with a container image to allow other researchers to reproduce and build on your work.

As we have already seen, the Docker Hub provides a platform for sharing container images publicly. Once you have uploaded a container image, you can point people to its public location and they can download and build upon it.

This is fine for working collaboratively with container images on a day-to-day basis but the Docker Hub is not a good option for long time archive of container images in support of research and publications as:

free accounts have a limit on how long a container image will be hosted if it is not updated
it does not support adding persistent identifiers to container images
it is easy to overwrite tagged container images with newer versions by mistake.

Archiving and persistently identifying container images using Zenodo

When you publish your work or make it publicly available in some way it is good practice to make container images that you used for computational work available in an immutable, persistent way and to have an identifier that allows people to cite and give you credit for the work you have done. Zenodo is one service that provides this functionality.

Zenodo supports the upload of tar archives and we can capture our Docker container images as tar archives using the docker image save command. For example, to export the container image we created earlier in this lesson:

BASH

docker image save alice/alpine-python:v1 -o alpine-python.tar

These tar container images can become quite large and Zenodo supports uploads up to 50GB so you may need to compress your archive to make it fit on Zenodo using a tool such as gzip (or zip):

BASH

gzip alpine-python.tar

Once you have your archive, you can deposit it on Zenodo and this will:

Create a long-term archive snapshot of your Docker container image which people (including your future self) can download and reuse or reproduce your work.
Create a persistent DOI (Digital Object Identifier) that you can cite in any publications or outputs to enable reproducibility and recognition of your work.

In addition to the archive file itself, the deposit process will ask you to provide some basic metadata to classify the container image and the associated work.

Note that Zenodo is not the only option for archiving and generating persistent DOIs for container images. There are other services out there – for example, some organizations may provide their own, equivalent, service.

Reproducibility good practice

Make use of container images to capture the computational environment required for your work.
Decide on the appropriate granularity for the container images you will use for your computational work – this will be different for each project/area. Take note of accepted practice from contemporary work in the same area. What are the right building blocks for individual container images in your work?
Document what you have done and why – this can be put in comments in the Dockerfile and the use of the container image described in associated documentation and/or publications. Make sure that references are made in both directions so that the container image and the documentation are appropriately linked.
When you publish work (in whatever way) use an archiving and DOI service such as Zenodo to make sure your container image is captured as it was used for the work and that is obtains a persistent DOI to allow it to be cited and referenced properly.

Container Granularity

As mentioned above, one of the decisions you may need to make when containerising your research workflows is what level of granularity you wish to employ. The two extremes of this decision could be characterized as:

Create a single container image with all the tools you require for your research or analysis workflow
Create many container images each running a single command (or step) of the workflow and use them together

Of course, many real applications will sit somewhere between these two extremes.

Positives and negatives

What are the advantages and disadvantages of the two approaches to container granularity for research workflows described above? Think about this and write a few bullet points for advantages and disadvantages for each approach in the course Etherpad.

Show me the solution

This is not an exhaustive list but some of the advantages and disadvantages could be:

Single large container image

Advantages:
- Simpler to document
- Full set of requirements packaged in one place
- Potentially easier to maintain (though could be opposite if working with large, distributed group)
Disadvantages:
- Could get very large in size, making it more difficult to distribute
  - Could use Docker multi-stage build to reduce size
- May end up with same dependency issues within the container image from different software requirements
- Potentially more complex to test
- Less re-useable for different, but related, work

Multiple smaller container images

Advantages:
- Individual components can be re-used for different, but related, work
- Individual parts are smaller in size making them easier to distribute
- Avoid dependency issues between different pieces of software
- Easier to test
Disadvantage:
- More difficult to document
- Potentially more difficult to maintain (though could be easier if working with large, distributed group)
- May end up with dependency issues between component container images if they get out of sync

Next steps with containers

Now that we’re at the end of the lesson material, take a moment to reflect on what you’ve learned, how it applies to you, and what to do next.

In your own notes, write down or diagram your understanding of Docker containers and container images: concepts, commands, and how they work.
In the workshop’s shared notes document, write down how you think you might use containers in your daily work. If there’s something you want to try doing with containers right away, what is a next step after this workshop to make that happen?

Key Points

Container images allow us to encapsulate the computation (and data) we have used in our research.
Using a service such as Docker Hub allows us to easily share computational work we have done.
Using container images along with a DOI service such as Zenodo allows us to capture our work and enables reproducibility.

Overview

Questions

Objectives

Learning about Docker Containers

Scientific Software Challenges

What’s Your Experience?

Software and Science

What is a Container? What is Docker?

Virtualization

Putting the Pieces Together

Use cases for containers

Key Points

Docker command line

Overview

Questions

Objectives

You may need to login to Docker Hub

Determining your Docker Hub username

BASH

OUTPUT

BASH

OUTPUT

OUTPUT

Getting help

OUTPUT

BASH

OUTPUT

BASH

OUTPUT

Docker Command Line Interface (CLI) syntax

Exploring a command

Show me the solution

BASH

OUTPUT

BASH

BASH

Key Points

Overview

Questions

Objectives

Reminder of terminology: container images and containers

Downloading Docker images

BASH

BASH

OUTPUT

Docker Hub

Exercise: Check on Your Images

Show me the solution

BASH

Running the hello-world container

BASH

OUTPUT

Using docker container run to get the image

Running a container with a chosen command

Run the Alpine Docker container

BASH

BASH

Hello World, Part 2

Show me the solution

BASH

Running containers interactively

BASH

Technically…

BASH

BASH

Practice Makes Perfect

Solution 1 – Interactive

BASH

BASH

BASH

BASH

Solution 2 – Run commands

BASH

BASH

Even More Options

Conclusion

Key Points

Removing images

Overview

Questions

Running the `hello-world` container

Using `docker container run` to get the image

`docker ps`

Put installation instructions in a `Dockerfile`