Cascading Docker images adapted from Matrix DS to create a reproducible, standard, consistent environment to run datascience projects and cater to development and production modes. The images enable deploying dashboard frameworks like shiny with rapidity & ease.
Note taken on [2020-02-24 Mon 07:21] Rewritten to improve clarity and grammatical corrections. Using the docker package in Emacs has saved several minutes of my time (for each command) related to docker, and just as important - a tonne of effort involved in hunting for docker container names, command history, copying the container ID’s and so on that are very typical steps of messing around with docker. Anybody learning docker will know that these commands are used so frequently that it becomes rather annoying quickly.
This blog post goes through the process of setting up Continuous Integration for building docker images via Dockerhub and Github, and via Github Actions. It also contains a condensed summary of important notes from the documentation that will be handy as ready reference.
Goal: Gain an overview of using Continuous Integration (CI) for automated builds of the docker images that built for a data science toolbox based on R (for now).
Docker is a fascinating concept that could be potentially useful in many ways, especially in Data science, and making reproducible workflows / environments. There are several articles which have great introductions and examples of using docker in data science
This is an evolving summary of my exploration with Docker. It should prove to be a handy refresher of commands and concepts.
TODO What is Docker A brief summary of what Docker is all about.