Containerize a Machine Learning application
In recent years, Docker has revolutionized the way software applications are developed, shipped, and deployed. This powerful tool allows developers to package their applications and all of their dependencies into lightweight, portable containers, providing consistency across different environments and simplifying the deployment process. In this post, we’ll explore the basics of Docker and how it can streamline your development workflow.
What is Docker? Docker is an open-source platform that automates the deployment of applications inside software containers. Containers are lightweight and portable environments that contain everything needed to run an application, including code, runtime, libraries, and dependencies. Docker enables developers to build, ship, and run applications consistently across different environments, from development to production.
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Containers: Docker containers are isolated environments that encapsulate an application and its dependencies. They are lightweight, portable, and can run on any system that has Docker installed.
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Images: Docker images are read-only templates used to create containers. Images contain all the necessary files, libraries, and configurations needed to run an application.
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Dockerfile: A Dockerfile is a text file that contains instructions for building a Docker image. It specifies the base image, environment variables, dependencies, and commands needed to create the image.
Check it: Machine Learning with Docker
Sample applications: