Docker for Data Science — A Step by Step Guide

Docker for Data Science — A Step by Step Guide

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.

What is Docker?

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):

  • Docker Container — A single instance of the application, that is live and running. In our analogy, this is a cookie.

A Dancing Cookie. GIPHY

  • _Docker Image _— A blueprint for creating containers. Images are immutable and all containers created from the same image are exactly alike. In our analogy, this is the cookie-cutter mould.

data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...

32 Data Sets to Uplift your Skills in Data Science | Data Sets

Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.

Data Cleaning in R for Data Science

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.