By the end of this post, you will have an ML workspace running on your machine via Docker, packed with the ML libraries you need, VSCode, Jupyter Lab + Hub, and a lot of other goodies.
A lot has already been said about why Docker can improve your life as a data scientist. I was working on an (un-)cool depth estimation project using Fast.ai with a few friends when I stumbled upon this tweet by @jeremyphoward.
It so happens that we were using Docker to create our data science workspace for the project, so I thought it would make sense to address Jeremy’s questions and share this knowledge with the community.
I’ll very briefly review the core concepts and advantages of Docker, and then show a step-by-step example for setting up an entire data science workspace using Docker.
If you already know what Docker is and why it’s awesome, skip to the step-by-step tutorial.
Docker is a tool for creating and deploying isolated environments (read: virtual machines) for running applications with their dependencies.
A few terms you should be familiar with (including a baking analogy for ease of understanding):
A Dancing Cookie. GIPHY
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