Developed a workflow built on Docker (and other tools) to create a reproducible, standard, consistent environment to run a variety of datascience projects and cater to development and production modes. The images enable deploying dashboards like shiny or streamlit.io quickly and with ease and addressing problems with a standadrd toolset.
As my Emacs configuration will indicate, I have installed the package docker.el with it the dockerfile and docker-compose minor mode packages. The main docker package enables me to list, view, launch and generally manage containers from within Emacs instead of using vebose Shell commands and possibly constructing aliases for common commands. The latter 2 packages are more useful for developing and editing docker files (including within Org source blocks) with syntax highlighting.
This blog post takes you 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.
Goal: Gain an overview of CI and actually use it to get automated builds of the docker images that built for my datascience toolbox.
Essentially I want to be able to a status check the docker containers that I am maintaining.
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.