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. 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.
Has anyone written a simple step-by-step guide to all this?- Jeremy Howard AugThe tweet that started this post I was working on an (un-)cool depth estimation project using Fast.ai, with a few friends when I stumbled upon this tweet by do you ensure you don't accidentally stop your image without committing, and losing all your changes? Do you use mounts? How do you keep your environment up to date (e.g CUDA updates, python lib updates, etc)? By the end of this post, you will have a 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.Ī lot has already been said about why Docker can improve your life as a data scientist.
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