Deploying JupyterHub for Education

As a PhD student at UC Berkeley, my duties involve some amount of teaching; so, this semester (Spring 2015), as well as last spring, I have been a teaching assistant for a class taught by my advisor, Tom Griffiths. The class, called Computational Models of Cognition (COGSCI 131), aims to introduce students to computational models of human behavior. The problem sets are a mixture of simple programming assignments—usually requiring students to implement pieces of different models—and written answers, in which students report and interpret the results of their code.

In the past, the problem sets were written in MATLAB. This year, however, we decided to make the switch to Python. In particular, we decided that the IPython/Jupyter notebook would be an ideal format for the assignments. The notebook is a cross-platform, browser-based application that seamlessly interleaves code, text, and images. With the notebook, it is possible for us to write instructions in the notebook, include a coding exercise after the instructions, and then ask for their interpretation of the results immediately after that. For an example of what the notebook looks like, you can check out try.jupyter.org for a demo.

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How did we serve more than 20,000 IPython notebooks for Nature readers?

The IPython/Jupyter notebook is a wonderful environment for computations, prose, plots, and interactive widgets that you can share with collaborators. People use the notebook all over the place across many varied languages. It gets used by data scientists, researchers, analysts, developers, and people in between.

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Using Docker Machine to run your Docker Containers in Rackspace

Background

Docker announced Docker Machine in December 2014. This clever new software eliminates the need to create virtual machines and install Docker before starting Docker containers on them. It handles the provisioning and install process for you behind the scenes. You can learn more about Docker Machines at its GitHub project page.

Let’s take a quick look at how we can get some of this awesomeness!

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Move fast and don’t break things! Testing with Jenkins, Ansible and Docker

One of our highest priorities at Mist.io is to never break production. Our users depend on it to manage and monitor their servers and we depend on them. At the same time, we need to move fast with development and deliver updates as soon as possible. We want to be able to easily deploy several times per day.

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