Docker for Data Science
Docker is a tool that simplifies the installation process for software engineers. Coming from a statistics background I used to care very little about how to install software and would occasionally spend a few days trying to resolve system configuration issues. Enter the god-send Docker almighty.
Think of Docker as a light virtual machine (I apologise to the Docker gurus for using that term). Its underlying philosophy is that if it works on my machine it will work on yours.
What’s in it for Data Scientists
- Time: The amount of time that you save on not installing packages in itself makes this framework worth it.
- Reproducible Research: I think of Docker as akin to setting the seed in a report. This makes sure that the analysis that you are generating will run on any other analysts machine.
How Does it Work?
Docker employs the concept of (reusable) layers. So whatever line that you write inside the
Dockerfile is considered a layer. For example you would usually start with:
FROM ubuntu RUN apt-get install python3
This Dockerfile would install
python3 (as a layer) on top of the
What you essentially do is for each project you write all the
pip install etc. commands into your Dockerfile instead of executing it locally.
I recommend reading the tutorial on https://docs.docker.com/get-started/ to get started on Docker. The learning curve is minimal (2 days work at most) and the gains are enormous.
Lastly Dockerhub deserves a special mention. Personally Dockerhub is what makes Docker truly powerful. It’s what github is to git, a open platform to share your Docker images.
My Docker image for Machine Learning and data science is availale here: https://hub.docker.com/r/sachinruk/ml_class/comments powered by Disqus