This is the first in a series of posts where I’ll be noting down my findings while exploring Docker and how and if it can help for everything Data Science related. In this post I’ll try to build a simple container that will have a jupyter notebook installed.

Contents

What is Docker?

From Wikipedia:

Docker is a software technology providing containers, promoted by the company Docker, Inc. Docker provides an additional layer of abstraction and automation of operating-system-level virtualization on Windows and Linux. Docker uses the resource isolation features of the Linux kernel such as cgroups and kernel namespaces, and a union-capable file system such as OverlayFS and others to allow independent “containers” to run within a single Linux instance, avoiding the overhead of starting and maintaining virtual machines (VMs).

So Docker is a program that provides containers, and containers are one way of performing what is called Operating System Level Virtualization:

Operating-system-level virtualization, also known as containerization, refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances. Such instances, called containers, partitions, virtualization engines (VEs) or jails (FreeBSD jail or chroot jail), may look like real computers from the point of view of programs running in them. A computer program running on an ordinary person’s computer’s operating system can see all resources (connected devices, files and folders, network shares, CPU power, quantifiable hardware capabilities) of that computer. However, programs running inside a container can only see the container’s contents and devices assigned to the container.

This is achieved using the Linux cgroups and namespaces feaures which respectively are described as:

cgroups (abbreviated from control groups) is a Linux kernel feature that limits, accounts for, and isolates the resource usage (CPU, memory, disk I/O, network, etc.) of a collection of processes. and Namespaces are a feature of the Linux kernel that isolate and virtualize system resources of a collection of processes. Examples of resources that can be virtualized include process IDs, hostnames, user IDs, network access, interprocess communication, and filesystems. Namespaces are a fundamental aspect of containers on Linux.

This means that docker is not the only way to create and use containers. Another similar software is rkt. However since Docker is the dominant container technology I will explore this option for now.

Getting started with Docker

To use docker it needs to be installed on your system (duh). The official instructions pretty much cover anything, but there are two main points to notice:

Docker is provided in the Community Edition and Enterprise Edition. For the majority of use cases the Community Edition is enough as it provides the whole container, orchecstration, networking and security functionality.

Docker is available for all 3 major OSs however since the container technology is native to Linux, the docker versions for Mac and Windows will use a Virtual Machine on top of which docker will run. This is mostly important when deploying the containers as if they are not deployed on a Linux machine docker, the Virtual Machine that is created to run docker will have a performance cost on the host machine where docker is installed.

Once docker is installed open up a shell to confirm it:

$ docker --version Docker version 17.09.0-ce, build afdb6d4

And the typical hello world example is running a hello world container:

$ docker run hello-world 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. 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://cloud.docker.com/ For more examples and ideas, visit: https://docs.docker.com/engine/userguide/

In the output of the command there’s a nice high level explanation of what happened here. So to proceed I need a good tutorial.

Fortunately the official docker guide is such a tutorial for getting a feeling for how Docker works. I’ll add here the bits that I found interesting while going through it.

First note to keep in mind is the difference between a Docker Image and a Docker Container:

An image is a lightweight, stand-alone, executable package that includes everything needed to run a piece of software, including the code, a runtime, libraries, environment variables, and config files.

A container is a runtime instance of an image—what the image becomes in memory when actually executed. It runs completely isolated from the host environment by default, only accessing host files and ports if configured to do so.

Containers run apps natively on the host machine’s kernel. They have better performance characteristics than virtual machines that only get virtual access to host resources through a hypervisor. Containers can get native access, each one running in a discrete process, taking no more memory than any other executable.

Docker images

So in order to run a container we need to have an image. But how are images created? This is where the Dockerfiles come in. Dockerfiles describe the way that an image is being build, in layers. Every instruction will build a new layer with the changes of that instruction.

Usually the first command in a dockerfile is a FROM command. This defines a base image upon which the current image will be expanded (as mentioned in layers). The command is followed by the image name, a colon and a tag which defines different versions of the image. From that point on the rest of the commands build upon this image, with pretty much every linux command. The available commands can be found [here]](https://docs.docker.com/engine/reference/builder/).

To find available base images the best place is the Docker store. Most of the images there are free, however there are also paid ones. You can search and find an image containing pretty much every software package available including images for databases, webservers, messaging systems etc. You can also push your own images to the store for others to use.

For a simple example let’s create an image that contains python3 and jupyter. I’ll be using the ubuntu image as the base image here. Next we use the RUN command to execute the system update and installation of python3 and the pip package manager. The next layer installs jupyter. Then we add another layer which creates a new system user, another layer to change to this user with the USER command, and finally using the ENTRYPOINT command we will define the command to start jupyter (using the --ip=* param to allow incoming traffic to jupyter from any IP). By default the filename of a dockerfile is, well, Dockerfile .

# Use the latest ubuntu image as base for the new image # ubuntu is the image name and latest is a tag that # references a particular version of the image. # In this case latest is always the latest LTS image # at the time of writing this, it's 16.04. FROM ubuntu:latest # Run a system update to get it up to speed # Then install python3 and pip3 RUN apt-get update && apt-get install -y python3 \ python3-pip # Install jupyter RUN pip3 install jupyter # Create a new system user RUN useradd -ms /bin/bash jupyter # Change to this new user USER jupyter # Set the container working directory to the user home folder WORKDIR /home/jupyter # Start the jupyter notebook ENTRYPOINT ["jupyter", "notebook", "--ip=*"]

Now that we have the dockerfile ready let’s build the image. Running the command docker images lists the available images in the system. The ubuntu base image is not in our local system so when we will build our own image, it will be downloaded.

$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE hello-world latest 1815c82652c0 3 months ago 1.84kB

So let’s see how we build images. The basic command is docker build and the path to the dockerfile. Usually we also provide the -t param which defines the new image tag. You can find details regarding the docker build command here. Running the command produces the following output (it has been truncated):

$ docker build . -t jupyter Sending build context to Docker daemon 2.56kB Step 1/6 : FROM ubuntu:latest ---> 2d696327ab2e Step 2/6 : RUN apt-get update && apt-get install -y python3 python3-pip ---> Running in bb404ad1f745 Get:1 http://security.ubuntu.com/ubuntu xenial-security InRelease [102 kB] Get:2 http://archive.ubuntu.com/ubuntu xenial InRelease [247 kB] Get:3 http://security.ubuntu.com/ubuntu xenial-security/universe Sources [49.7 kB] Get:4 http://archive.ubuntu.com/ubuntu xenial-updates InRelease [102 kB] Get:5 http://security.ubuntu.com/ubuntu xenial-security/main amd64 Packages [456 kB] ... ... ... Removing intermediate container 4fc4f28aa4a7 Step 4/6 : RUN useradd -ms /bin/bash jupyter ---> Running in 1dc139392c0d ---> 61a9386d2b4d Removing intermediate container 1dc139392c0d Step 5/6 : USER jupyter ---> Running in a3d0480587cd ---> 2516f84b585d Removing intermediate container a3d0480587cd Step 6/6 : ENTRYPOINT jupyter notebook --ip=* ---> Running in 0eceeb09bb7d ---> ff2f03f5aaea Removing intermediate container 0eceeb09bb7d Successfully built ff2f03f5aaea Successfully tagged jupyter:latest

Once it is finished we can run again the docker images command to see the newly created image:

$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE jupyter latest ff2f03f5aaea About a minute ago 541MB ubuntu latest 2d696327ab2e 2 weeks ago 122MB hello-world latest 1815c82652c0 3 months ago 1.84kB

Now we have our own brand new image to use. As mentioned before we can push this image to the store for others to use. Alternatively we can use a private docker registry (all the major cloud providers have similar services) if the image contains proprietary code, passwords, ssh keys etc. For all commercial applications, all companies use a private registry to privately store the images.

Docker containers

The main thing we do with an image is run a container. This is done with the docker run command. The full details for how to use the command can be found here. For the image we have beed building here we can run docker run -it -p 8888:8888 ff2f03f5aaea /bin/bash where ff2f03f5aaea is the image id that we got from the docker images command.

$ docker run -it -p 8888:8888 ff2f03f5aaea /bin/bash [I 20:47:25.881 NotebookApp] Writing notebook server cookie secret to /home/jupyter/.local/share/jupyter/runtime/notebook_cookie_secret [W 20:47:25.897 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended. [I 20:47:25.904 NotebookApp] Serving notebooks from local directory: /bin [I 20:47:25.904 NotebookApp] 0 active kernels [I 20:47:25.904 NotebookApp] The Jupyter Notebook is running at: [I 20:47:25.905 NotebookApp] http://[all ip addresses on your system]:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX [I 20:47:25.905 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [W 20:47:25.905 NotebookApp] No web browser found: could not locate runnable browser. [C 20:47:25.906 NotebookApp] Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://localhost:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

If you open a browser window and go to the above url you’ll see the notebook live. It’s a new clean environment that we can shape as we like and destroy once we are done.

Going to a new terminal and run the docker ps command (which lists the running containers) we can see our container! Notice the -p 8888:8888 parameter. This maps the container’s inner port to the host machine’s port. This is done so that we can access the jupyter notebook running inside the container from our machine. By default everything is isolated inside the container and we need to manually export any resources that need to be exposed as to be accessed.

$ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 911acd30ed36 ff2f03f5aaea "jupyter notebook ..." 18 minutes ago Up 18 minutes 0.0.0.0:8888->8888/tcp sleepy_hugle

Furthermore the way we ran the container above though we need to keep the terminal open otherwise the container will stop. Typing ctrl+c to kill the container we can see that there’s no longer any container:

$ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES

To work around this issue we can add the -d parameter when we run a container which detaches it from the terminal so that it runs in the background even after the terminal has closed.

$ docker run -d -p 8888:8888 ff2f03f5aaea 98890a46a5c2375b8aa3a36111ceee941992391b4c19f2878d9906e4ff5ce699

In this case we get the terminal back on the host system which means it has not been attached to the container. However to confirm that the container is running let’s run the docker ps command again:

$ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 98890a46a5c2 ff2f03f5aaea "jupyter notebook ..." 40 seconds ago Up 38 seconds 0.0.0.0:8888->8888/tcp hungry_euclid

It is indeed running. But how do we get the url from Jupyter? We use another docker command, docker logs <container> . Notice that instead of the container id we can also use the container name obtained above.

$ docker logs hungry_euclid [I 19:38:32.023 NotebookApp] Writing notebook server cookie secret to /home/jupyter/.local/share/jupyter/runtime/notebook_cookie_secret [W 19:38:32.059 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended. [I 19:38:32.075 NotebookApp] Serving notebooks from local directory: / [I 19:38:32.075 NotebookApp] 0 active kernels [I 19:38:32.076 NotebookApp] The Jupyter Notebook is running at: [I 19:38:32.076 NotebookApp] http://[all ip addresses on your system]:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX [I 19:38:32.076 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [W 19:38:32.076 NotebookApp] No web browser found: could not locate runnable browser. [C 19:38:32.077 NotebookApp] Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://localhost:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Finally to stop the container we need to manually do it by running the command docker stop <container id> :

$ docker stop hungry_euclid hungry_euclid

And we can see that indeed there are no longer any containers running:

$ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES

Removing containers & images

One last thing that is useful to know is how to remove containers and images. The commands respectively are docker rm <container> and docker rmi <image> . The way to find containers that are stopped is docker ps -a .

$ docker ps -a 98890a46a5c2 ff2f03f5aaea "jupyter notebook ..." 10 minutes ago Exited (0) 12 minutes ago hungry_euclid $ docker rm hungry_euclid hungry_euclid

Now to remove the image we have built:

$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE jupyter latest ff2f03f5aaea 2 hours ago 541MB ubuntu latest 2d696327ab2e 3 weeks ago 122MB hello-world latest 1815c82652c0 3 months ago 1.84kB $ docker rmi ff2f03f5aaea Untagged: jupyter:latest Deleted: sha256:ff2f03f5aaea4ce5f2acd36d7ab2fde0337fa34d2921aaac3e51630778f4bba1 Deleted: sha256:2516f84b585d62c71514ce373baa93f6842c50573eb160983793b63b45a16bbe Deleted: sha256:61a9386d2b4d569787f53b02b93060ee8bb25cf3b21e833e179dda8cfc395a9c Deleted: sha256:4c69651b94bbbca80e6521cc4576ccf5ff5cfa4a10dc1acae87a047b91d0001a Deleted: sha256:e5c0bb29999d46880dbe106099f4f1cc265227cf5b16a29a98983ace01db3d97 Deleted: sha256:5fa718810d8afb400449e9967c1f0fc46bbe2f4273b67e4e61a18f159e859dcb Deleted: sha256:3d5ec8b404c085bc7548a32d8077e5ec58e25e1059e78c88be5effab9414932b Deleted: sha256:b55b2ae6bd8065f43e39f179773f82e0bb1df302162796bddd48cb606e6e6c1b

User gvkalra on Reddit noted the importance of mounted storage so I think this needs to be included here as well. So the basic question reagarding this issue is that as mentioned everything about a container is ephemeral, that’s the whole point of creating a detached, clean environment in the first place. Then what happens if we have data that we need to persist after the container has been killed? Data could be either code or perhaps the output of calculations that we have performed. Docker solves this issue by the use of three persistent storage techniques. The techniques are volumes, bind mounts and tmpfs. Each of these have their specific use cases.

Volumes

The first, most common and preferred one is volumes. These are isolated spaces in the docker host which are stored under /var/lib/docker/volumes . The volume is mounted when a container is ran and mapped to a location on the container filesystem. Whatever is written in that location by the container will persist after the container has been killed. It’s also useful to know that volumes can be shared across many different containers. Volumes can be created either explicitly and independent of any container with a standalone command, or they can be created if they passed as a docker run parameter. To create a new volume run the command docker volume create jupyter_store where jupyter_store should be whatever name the new volume should have. Running docker volume ls lists all the available volumes:

$ docker volume ls DRIVER VOLUME NAME local jupyter_store

To get additional information for our new volume we use docker volume inspect jupyter_store :

$ docker volume inspect jupyter_store [ { "CreatedAt": "2017-10-21T15:36:23+03:00", "Driver": "local", "Labels": {}, "Mountpoint": "/var/lib/docker/volumes/jupyter_store/_data", "Name": "jupyter_store", "Options": {}, "Scope": "local" } ]

So now we have a volume that we can use. Let’s start a new container and connect to jupyter:

$ docker run -d -p 8888:8888 -v jupyter_store:/home/jupyter d189bcaf0613 2bafcd956a5f04039b81a4c1dbb3b9b6375b858d68ab9930c5a9c505d79ddca5 $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 2bafcd956a5f d189bcaf0613 "jupyter notebook ..." 7 seconds ago Up 5 seconds 0.0.0.0:8888->8888/tcp elastic_curran $ docker logs elastic_curran [I 16:29:26.100 NotebookApp] Writing notebook server cookie secret to /home/jupyter/.local/share/jupyter/runtime/notebook_cookie_secret [W 16:29:26.118 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended. [I 16:29:26.124 NotebookApp] Serving notebooks from local directory: /home/jupyter [I 16:29:26.124 NotebookApp] 0 active kernels [I 16:29:26.124 NotebookApp] The Jupyter Notebook is running at: [I 16:29:26.125 NotebookApp] http://[all ip addresses on your system]:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX [I 16:29:26.125 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [W 16:29:26.125 NotebookApp] No web browser found: could not locate runnable browser. [C 16:29:26.125 NotebookApp] Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://localhost:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Now let’s create a new notebook called test and save it locally (on the container). Then I will stop the container and remove it altogether:

$ docker stop elastic_curran elastic_curran $ docker rm elastic_curran elastic_curran

Then I will create a new container and connect to jupyter:

$ docker run -d -p 8888:8888 -v jupyter_store:/home/jupyter d189bcaf0613 99d953e9ea5960e88e7c756275a3f0746ebac55fce566e2f3bc7e0d6e4a671be $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 99d953e9ea59 d189bcaf0613 "jupyter notebook ..." 12 seconds ago Up 10 seconds 0.0.0.0:8888->8888/tcp suspicious_saha $ docker logs suspicious_saha [W 16:32:04.903 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended. [I 16:32:04.910 NotebookApp] Serving notebooks from local directory: /home/jupyter [I 16:32:04.910 NotebookApp] 0 active kernels [I 16:32:04.910 NotebookApp] The Jupyter Notebook is running at: [I 16:32:04.910 NotebookApp] http://[all ip addresses on your system]:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX [I 16:32:04.910 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [W 16:32:04.910 NotebookApp] No web browser found: could not locate runnable browser. [C 16:32:04.910 NotebookApp] Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://localhost:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

We can see that the previously created notebook is available. The fact that volumes are independent of the containers means that we can delete containers, create containers from new images altogether without having to worry about our data getting corrupted. Obviously this is also applicable with containers that contain databases.

Finally let’s explore the folder that was assigned to this volume. Listing the folder files (we need sudo to access it because permissions to the docker volumes folder are restricted for the normal linux users):

$ sudo ls /var/lib/docker/volumes/jupyter_store/_data test.ipynb

Our jupyter notebook is saved there as expected. To get more details about the intricacies of volumes please refer to the full guide on the docker site here. Before we move on I should note the command to remove volumes is:

$ docker volume rm jupyter_store

Bind Mount

The second way to achieve data persistence on a container is using bind mounts. This is a pretty similar technique which instead of creating an isolated storage space for the containers, instead uses a folder from the docker host machine that is specified when we start a container. For example to use the current working directory we should run:

$ docker run -d -p 8888:8888 -v "$(PWD)":/home/jupyter d189bcaf0613

or if we wanted to mount the /tmp/source/input folder (the folder should exist on the docker host machine) we would use:

$ docker run -d -p 8888:8888 -v /tmp/source/input/:/home/jupyter d189bcaf0613

Obviously since these folders are not restricted to be used by docker alone, run the risk of being overwritten by other processes running on the docker host and hence this method of using persistence is not preferred except in cases where we have sensitive data on the docker host that we don’t want to include on the docker image, but we do need on the container, like configuration files or password data. To find out more about bind mounts please refer again to the docker site page.

tmpfs

The last way we can use persistence is with tmpfs. This is really a pseudo-persistence technique since we are using the docker host machine memory to create a mount point. This is useful in cases where we want to store data on the container just for lifecycle of the container but we also don’t want to keep these on the container memory. To use this mount we run a container with the --tmpfs param instead of -v and provide the container mount point:

$ docker run -d -p 8888:8888 -tmpfs /home/jupyter d189bcaf0613

This is really a niche use case, if you want find out more about it check the docs.

Conclusion

This was a small introduction into the docker basics: images and containers. The next post in the series will cover a multi-container system, where containers really shine.

If you prefer the more classical approach to learning more about Docker via a book I would suggest The Docker Book (affiliate link).