If you use Docker, you can have multiple versions of the same app running inside different containers- without any conflicts-in the same environment. When developing an application, it is also common to build and test multiple versions of the same app. And they can start containers they can pull the Docker image and start containers using a single command-without having to worry about complex installations-on the remote machine. DockerHub is the largest public registry, and all images are pulled from DockerHub by default.īecause containers provide an isolated environment for your applications, other developers now only need to have Docker set up on their machine. After containerizing an application into a Docker image, you can make it available for the developer community by pushing them to an image registry. So a running instance of an image is a container.ĭocker registry is a system for storing and distributing Docker images. When you run an image, you’re essentially getting the application running inside the container environment. Let’s go over a few concepts/terminologies:Ī Docker image is the portable artifact of your application. So you can define an isolated, reproducible, and consistent environment for your applications across the range of host machines.ĭocker Basics: Images, Containers, and Registries With Docker, you can package your application-along with the dependencies and configuration. These problems often arise from mismatched configuration and library versions-in the development environment-between the two machines. However, you may still run into problems when trying to run your application on another machine. You’ll also ensure that you’re using an updated version of Python that the libraries support. For example, in a data science project, you’ll install all the required libraries in your development environment (preferably inside a virtual environment). This guide will introduce you to the basics of Docker and teach you how to containerize data science applications with Docker.ĭocker is a containerization tool that lets you build and share applications as portable artifacts called images.Īside from source code, your application will have a set of dependencies, required configuration, system tools, and more. Docker simplifies the development process and facilitates seamless collaboration. Even small differences such as differing library versions can introduce breaking changes to the code. What if other developers want to run your code and contribute to the project? Well, other developers who want to replicate your data science application should first set up the project environment on their machine-before they can go ahead and run the code. When working on a data science project, you’ll have to spend substantial time installing the various libraries and keeping track of the version of the libraries you’re using amongst others. However, dependency management in Python is a challenge. Python and the suite of Python data analysis and machine learning libraries like pandas and scikit-learn help you develop data science applications with ease.
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