"Doing Reproducible Research with Docker"
A key obstacle to reproducible research that I frequently encounter when working with students and collaborators is keeping the toolkit simple, with managing dependencies being an especially time-consuming challenge. Virtual machines are one solution to these problems, but remain less than ideal because of relatively long start-up and shut-down times, their large size and performance demands, limited portability, and the need for the user to be familiar with a different desktop environment, amongst other concerns. In this talk I introduce Docker, a free and open source Linux container tool recently popular amongst commercial DevOps workers that provides lightweight virtual environments on Windows/OSX/Linux systems and has several advantages over regular virtual machines. I will describe the key elements of doing reproducible research with Docker and demonstrate dockerfiles, containers, images and registries (bring your laptop and follow along! If your using Windows/OSX then be sure to install http://boot2docker.io/ in advance). I will show how these help with dependencies and keeping things simple, especially when working with R or Python.
Please join us!