This is a fork from Dani's work (please see below for citing) to remove R as we don't need this for teaching but do have a few more Python packages that we do use. We've also added some JupyterLab extensions to make interacting with the Lab server a bit easier.

We previously experimented with four approaches to installation: VirtualBox; Vagrant; Docker; and Anaconda Python directly. Each of these has pros and cons, but after careful consideration we have come to the conclusion that Docker is the most robust way to ensure a consistent experience in which all students end up with the same versions of each library, difficult-to-diagnose hardware/OS issues are minimised, and running/recovery is the most straightfoward.

A more detailed set of instructions can also be found in Dani's Repo. Read this if you have trouble!

This draws heavily on Dani Arribas-Bel's work for Liverpool. If you use this, you should cite him.

@software{hadoop,
  author = {{Dani Arribas-Bel}},
  title = {\texttt{gds_env}: A containerised platform for Geographic Data Science},
  url = {https://github.com/darribas/gds_env},
  version = {3.0},
  date = {2019-08-06},
}

• Adapt Dani's Makefile for use with SDS and enabling install of matching env to Docker