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Reproducible work environments using Docker

This article covers the basics of using Docker to control dependencies ranging from operating system to packages. While we use Python as an example here, the concepts are equally applicable to any other programming language.

A common pitfall that Python users fall into is dependency management. People are often unsure of how to set up virtual environments or how to reproduce an environment. Often, I see people simply running  pip install any library onto the local environment as a global dependency. 

 These are common challenges that anyone would face in the real world:
  • Needing different versions of the same library for different projects
  • Losing track of the required libraries for a specific project
  • Requiring a different Python version
  • Setting up projects on a new team member’s machine (which could have a different OS as well) being painful and time-consuming
  • Automating deployment was impossible given that the entire process of setting up was convoluted and manual

I’m sure these are common issues that other developers face as well, which explains the existence of numerous tools that solve the same problems. Tools for managing Python dependencies include `pyenv`, `venv`, `virtualenv` and `pyvenv` (which is deprecated in favor of venv since Python 3.6). Unfortunately, many of these tools have very similar names and some have identical purposes. It can be a steep learning curve for a newcomer to the project and/or Python to understand these tools.

Next, we have command line interface (CLI) tools dependencies as well. Let’s take an example of one of the most common CLI tools we use in software development, git. In our example scenario, we want to replicate our working environment on a machine without git. One approach is to write a script to install the git CLI tool. In Mac OS X, we can add the command brew install git to our script. All our team members now have access to git as long as they run the setup script.

In more complicated projects, we have team members using different operating systems such as Linux and Windows. Our old setup script for Mac OS X no longer works. We could add a conditional check in our script to check for the type of OS and run a corresponding command to install git before we realize in horror that there’s no straightforward way to install git on Windows using a script on the default command prompt. (It’s still possible to automate this on Windows, but it requires other dependencies and I leave it up to you to discover the details.)

Keeping track of all these tools and branching scripts that we use to ensure similar working environments can become a pain too. Each time we have a new CLI tool, we have to write different installation scripts for each OS. On top of that, there are also OS limitations that are difficult to overcome.

Wouldn’t it be great if there was one single tool that could set up our entire working environment?

Docker to the rescue!

Docker allows us to manage the following dependencies in a single place:
  • OS dependencies
  • CLI tools dependencies
  • Python dependencies
By properly defining a Dockerfile in our project repository, we can create an environment separate from our local environment that contains all the dependencies we need. This eliminates all possible conflicts with user preferences and local machine set up, as long as the users make use of the Docker container. Let us now look in detail on how we can set up a Dockerfile that solves the above issues.

Before we begin, a few words about the Dockerfile:
  1. It is a step-by-step instruction that tells Docker how to build a Docker image
  2. There are a standard set of instructions it can run, such as FROM, RUN, COPY, WORKDIR.
  3. After preparing the Dockerfile, the image is built by running docker build <path to Dockerfile> or docker build . from the directory of the Dockerfile.

Defining OS dependencies

The first thing we do is to specify our base image. Let us choose from a list of suitable Docker images for Python. We shall use the 3.6-slim version for our example. Our first line in our Dockerfile will look like this:
FROM python:3.6-slim
How do we find out what exact OS this image is using? We can find the Dockerfile of the 3.6-slim version from the official Python Docker images, which specifies how this base image was built. The first line of that Dockerfile states:
FROM debian:stretch-slim
This tells us that the OS is a Debian Linux distribution.

Defining Python dependencies

Since we are creating an entirely separate environment using a Docker image, we don’t need to worry about managing Python versions or virtual environments. What we need to do is define our Python dependencies in a text file. In the following example, we’ll name our text file requirements.txt. We can manually define the packages we need or extract the existing dependencies from a working environment using the following command:
pip freeze > requirements.txt
After we have our requirements.txt ready, we can add the following commands to our Dockerfile:
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
The first line will copy the requirements.txt file in our local working directory to the Docker image’s working directory. The second line runs the command pip install -r requirements.txt, which will install all the libraries you need in your Docker image.

Defining CLI tools dependencies

We can run commands during the building of our Docker image to install CLI tools. Since we are using a Debian Linux distribution, we can use apt-get to install curl and git. The snippet below shows what we need to add to our Dockerfile to do so.
RUN apt-get update 
    && apt-get install -y curl git
Any other CLI tools you need can be installed in a similar fashion. Just add the commands that you need to run into the Dockerfile.

Working with the Docker image

Next, in order to run our code in the Docker image, we will mount our local code directory to a working directory. We can set a working directory in our Dockerfile, like so:
WORKDIR /workdir
You can replace /workdir with any working directory you prefer. WORKDIR does multiple things including setting the default directory of running further Dockerfile commands and setting the default entry point to the container. 

Now let us take a look again at our Dockerfile.
FROM python:3.6-slim
FROM apt-get update 
     && apt-get install -y git
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
WORKDIR /workdir
After defining all the above dependencies in the Dockerfile, it’s time to build it. Run the following command:
docker build . -t my_project_image
This searches your current directory for a Dockerfile and tags the image with the name my_project_image

Next, run the Docker image as a container:
docker run -it 
-v $(pwd):/workdir
my_project_image bash
Line by line explanation of the above command:
  1. The default docker run command followed by the -i and -t flags when used together (as -it) allows us to run the Docker container as an interactive process
  2. The -v flag allows us to mount our directory on to the container’s directory. Here we specify our current directory with $(pwd) followed by a colon (:) and specify the Docker container’s directory /workdir
  3. In our last line, we specify the image name to run as a container and the entry process bash
Our Docker container is now up and running with everything we need for developing our project (e.g.OS dependencies, Python dependencies and CLI tools). The simplified setup process no longer requires OS-specific setup and Python version/virtual environment management tools. The only requirements are to have Docker installed on the target machine and running the docker build and docker run commands.

I hope that this article has shown how Docker can be used to effectively manage various dependencies in a project. While the example here is targeted towards setting up an environment in a Python project, using Docker to ensure reproducible environments is just as applicable to any other project.

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

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