How to Dockerize Any Machine Learning Application

How to Dockerize Any Machine Learning Application

How to Dockerize Any Machine Learning Application. I use these 3 simple steps to do it over and over again.

A month in as a fresh graduate at work, the founder of our AI startup walks to me and asks “Arunn, I want you to be an expert in Docker, how long would you need?”. Not sure what Docker is, but unable to dodge the question, eventually I replied “Two weeks, 1 sprint”.

My manager who was also around tried interrupting to save me, but I had already done the damage, and all I had was the next two weeks.

Looking back, I was never an expert (nor am I now!), but I learned just enough to do what was required. In this article, I’m going to tell you what’s just enough to dockerize any machine learning web applications.

What is Docker?

*Docker *is a tool designed to create, deploy, and run applications by using containers. A *Container *is a standardized unit of software, in simple terms — nothing but a packaged bundle of application code along with required libraries, and other dependencies. A *Docker Image *is an executable package of software that includes everything needed to run an application and becomes a *Container *at runtime.

It was a lot of new technical terms when I tried to understand Docker. But the idea is actually simple.

Think of it like you get a fresh mini Ubuntu machine. Then you install some packages on top of it. Then you add some code on top of it. And finally, you execute the codes to create an application. All of this happens on top of your existing machine with the operating system of your choice. All you need is to have Docker installed in it.

If you do not have Docker installed on your machine, please find instructions here to set up Docker.

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