Docker

Continuous Integration (CI) is not a tool but a practice of continually merging in new behaviour/features into a released product. To facilitate this practice without exposing end users to unstable behaviour and bugs, testing needs to be standardised and automated. It’s no wonder then that CI is often associated with Test Driven Development (TDD), which mandates that you write your tests first, working backwards to the write the minimal code that should pass each test.

It’s taking a long time to run my genetic algorithm optimisation models recently. So much so that I’ve been looking at offloading processes to other computers lying idle on the network. The armr project aims to do this with parallel processing and Rstudio server docker images running on the raspberry pi but this is a work in progress currently, chiefly due to having to build Rstudio server from source.

When I began learning about how to use Docker I stumbled on an excellent project called Rocker. For anyone with an x86 machine these Rocker images allow them to run R and most of its dependencies in a containerised environment. Plumber APIs, anyone? What about your own Shiny server? Finally, data scientists using R can have the same level of control on dependencies and package versions as Python users have become accustomed to through venv.

Docker containers for R on 32 bit ARM architectures, including Raspberry Pi

Gitlab runner in a docker container. Compatible with Raspberry Pi