Full Details: https://ais.berkeley.edu/events/zero-jupyterhub-hands-workshop/2017-10-09
Open to: UC Berkeley Faculty, Graduate Students, and Staff. Learners of all backgrounds are welcome.
Location: Academic Innovation Studio (Dwinelle 117, Level D)
What's JupyterHub? JupyterHub allows you to flexibly connect a custom computational environment with multiple users via the cloud. It is a rapidly-growing technology that has proven useful in pedagogy (currently being used for a >1,000 person class) as well as in research.
Workshop Description: This three-hour hands-on workshop will cover the basics of how to set up your own JupyterHub in the cloud using Kubernetes (a popular technology for robustly managing cloud resources), Cloud computing, a bit of dev-ops background, as well as some more advanced topics such as customizing your JupyterHub deployment for your needs. We will begin by deploying a bare JupyterHub in Google Cloud that exposes a public IP address you can use to access the hub resources. We'll then cover some best-practices in debugging and managing your Kubernetes deployment, as well as how to extend your JupyterHub in order to tailor the user environment however you like.
This workshop will be useful for educators or researchers who are interested in deploying a JupyterHub for their own purposes (either in teaching or in research). This workshop doesn't require any specific technical skill - we step you through everything that is needed from start to finish. However, familiarity with the shell, editing text files, and a high-level knowledge of what we mean by "the cloud" will be helpful. By the end of the workshop, each participant should have their own public JupyterHub running a custom Docker image.
Register Now (UC Berkeley ID required)
Registration closes on October 8, 2017 - 5:00pm.
Speaker(s)
Chris Holdgraf
Chris Holdgraf was formerly a BIDS Data Science Fellow and Community Architect for UC Berkeley's Data Science Education Program. His background is in cognitive and computational neuroscience, where he used predictive models to understand the auditory system in the human brain. He's interested in the boundary between technology, open-source software, and scientific workflows, as well as creating new pathways for this kind of work in science and the academy. He's a core member of Project Jupyter, specifically working with JupyterHub and Binder, two open-source projects that make it easier for researchers and educators to do their work in the cloud. He works on these core tools, along with research and educational projects that use these tools at Berkeley and in the broader open science community.
