A few years ago, Docker made containers popular. With the rise of Kubernetes container orchestration, Cloud Native Computing Foundation's (CNCF) newly adopted open-source Container Runtime Interface -- Orchestrator (CRI-O) runtime -- CRI-O may rise to the top of container deployments. That's because to run containers at scale you need an orchestration program. By the end of 2017, Kubernetes has become the most popular container orchestrator. You can, of course user Docker to run containers under Kubernetes.
Adopting a project-based approach, this book introduces you to a simple Python application to be developed and containerized with Docker. After an introduction to Containers and Docker, you'll be guided through Docker installation and configuration. You'll also learn basic functions and commands used in Docker by running a simple container using Docker commands.
If you regularly have to deal with specific versions of R, or different package combinations, or getting R set up to work with other databases or applications then, well, it can be a pain. You could dedicate a special machine for each configuration you need, I guess, but that's expensive and impractical. You could set up virtual machines in the cloud which works well for one-off situations, but gets tedious having to re-configure a new VM each time. Or, you could use Docker containers, which were expressly designed to make it quick easy to configure and launch an independent and secure collection of software and services. But the concepts are pretty simple.
This book is intended for seasoned solutions architects, developers, and programmers, system engineers, and administrators to help you troubleshoot common areas of Docker containerization. If you are looking to build production-ready Docker containers for automated deployment, you will be able to master and troubleshoot both the basic functions and the advanced features of Docker. Advanced familiarity with the Linux command line syntax, unit testing, the Docker Registry, Github, and leading container hosting platforms and Cloud Service Providers (CSP) are the prerequisites. This book will traverse some common best practices for complex application scenarios where troubleshooting can be successfully employed to provide the repeatable processes and advantages that containers can deliver.
Containers have a long history that dates back to the '60s. Over time, this technology has advanced to a great deal and has become one of the most useful tools in the software industry. Today, Docker has become synonymous for containers. In one of our previous articles, we discussed how Docker is helping in the Machine Learning space. Today, we will implement one of the many use cases of Docker in the development of ML applications.