Getting Started with Docker for Data Scientists

Getting Started with Docker for Data Scientists

A quick start guide and best practices to working with Docker. As developers, data scientists, software engineers, we work on complex code bases that depend on many items in the background.

A quick start guide and best practices to working with Docker

We have all been there,

“_It worked on my machine!!_”.

Who wasn’t on either end of that statement?

As developers, data scientists, software engineers, we work on complex code bases that depend on _many _items in the background. When we want to share our code with colleagues or put up on Github as an opensource project, we need to _ensure _that the code will work on all different environments.

Sometimes — more often than we would like to admit — we try to run a friend’s code or a code that we got from the internet when the computer yells at us “Import Error.” That error means that the code needs more information that it can’t find on your computer.

The solution for this is using Docker. *Docker *is a container management system that aims to facilitate sharing projects and to run them across different environments. Basically, Docker makes it easy to write and run codes smoothly on other machines with different operating systems by encapsulating the code and all its dependencies in a container.

This container makes the code self-contained and independent from the operating system.

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Image by the author (made using Canva)

Why we use dockers?

When we write code for data science or machine learning applications, we often have many concerns that make using a Docker the best option for our applications. These concerns are:

  1. Ensure that the application will work on all environments in the same manner.
  2. Save those who will use/ run your application the trouble of handling dependencies and installation problems.
  3. Avoid working with virtual machines.
  4. Focus on building the application instead of worrying about managing dependencies.

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