How to build your custom image in simple steps?

I had my first contact with Docker containers a few months ago and, since the very first image pull, I noticed how robust and easy to use this platform can be.

I’m currently running WSL2 and the Ubuntu distribution offered in the Microsoft Store. Installing WSL2 and a Linux distro is also an easy task, following the very detailed Microsoft documentation at: Install Windows Subsystem for Linux (WSL) on Windows 10 | Microsoft Docs

Since that, almost all my Data Science stuff is running on containers, including: Python environment, Jupyter and JupyterLab, Tensorflow, MySQL and even a docker compose with AirFlow and Postgres, with images pulled “as it is” from Docker Hub.

There are usually some tricks to make some of these tools running well on a container, but since you understand how a docker container works, things are getting easy to fix and build, specially when when you need to persist your data/settings, as containers are ephemeral and will not keep any data saved to it when you kill it.

But in this article I will show some very simple steps to create your own Docker image. Starting with a empty dockerfile or modifing an existing one.

You can find the customized dockerfile and other stuff in my GitHub repo: ruginski/DSDocker


Docker is an amazing platform that uses OS-level virtualization, what makes the images extremely light and flexible — compared to virtual machines. It helps automating the deployment of applications both on premises or to the cloud.

The image below shows a comparation between standard VMs and Docker containers:

Image captured from What is Docker? | Microsoft Docs

A dockerfile is simply a text file that contains all the commands and instructions that will be used by the docker build tool to create the image.

Project Jupyter is the official repository on GitHub. It has an amazing set of ready-to-run Docker images with Jupyter applications. Some examples of images are:

  • jupyter/base-notebook: is the base for all the other images.
  • jupyter/scipy-notebook: packages to scientific Python ecosystem.
  • jupyter/tensorflow-notebook: with Python libraries for deeplearning.
  • jupyter/R-notebook: with packages for R development.

The complete list and descriptions can be found at: Selecting an Image — docker-stacks.

So, let’s see how I managed to built my custom docker image using an existing dockerfile derived from jupyter/docker-stacks.

In my case, I merged both jupyter/minimal-notebook and jupyter/scipy-notebook as they have most of the libraries, dependencies and commands already in it. But the original scipy-notebook has the minimal-notebook as it’s base, that yet has the base-notebook as it’s base. Anda I was looking for one single dockerfile that was not dependent on so many other images.

Step 1 — Create the dockerfile

Create a folder that will contain the dockerfile and anyother files needed. The folder can be placed inside the WSL2 distro or on your host PC. Let´s make it simple and create it at the host PC as C:\docker\ds-notebook

Copy the dockerfile that you want to use as a base and copy to this folder. In this case, let’s copy the one from docker-stacks/scipy-notebook.

Use your preferred text editor to open it, but I suggest VS Code.

Step 2 — Make your changes

Now, all you have to do is edit the file according to your needs. You can add/remove OS updates and libraries, among many other fancy stuff.

As previously mentioned I merged both jupyter/minimal-notebook and jupyter/scipy-notebook and besides that, all that I did was to add more libraries to it and a few other changes. Final version can be found at: DSDocker/ds-notebook

Step 3 — Build the image

Now the fun part: let’s actually build the image!

Open your WSL console and head to the folder we’ve previously created:

cd /mnt/c/docker/ds-notebook/

Make sure Docker Desktop is up and running before proceeding!

Type the following command:

docker build -t ds-notebook .

Note: You can replace “ds-notebook” with the name you want for your image.

The building process will start. Docker build will download and install all the dependencies and libraries, so grab a coffee and come back in a few minutes.

Step 4 — Check if image was built

Use the docker images command and see if your new image is there:

Step 5 — Start the container

Use the docker run command as below to start you new container:

docker run -d -p 8888:8888 -e JUPYTER_RUNTIME_DIR=/tmp -v "$PWD":/home/jovyan --name DS-Notebook DS-Notebook:latest