Jupyterhub

JupyterHub brings the power of notebooks to groups of users. It gives users access to computational environments and resources without burdening the users with installation and maintenance tasks. Users - including students, researchers, and data scientists - can get their work done in their own workspaces on shared resources which can be managed efficiently by system administrators.

JupyterHub allows users to interact with a computing environment through a webpage. As most devices have access to a web browser, JupyterHub makes it is easy to provide and standardize the computing environment of a group of people (e.g., for a class of students or an analytics team).

Hands on

Open your Google console, and let’s deploy a Jupyterhub!

• Zero to Jupyterhub documentation
• Google Kubernetes Engine Instruction

Dask

• #1 Distributed processing tool in Python
• Distributed Dataframe and Arrays
• We’ll see more of it in another class

Pangeo

• Community of (geo)scientists and developers
• Tackling big data problems: analysing simulations or sensor data
• Providing recipes to build a Pangeo platform:
  • Jupyterhub
  • Dask
  • Xarray
  • on Kubernetes or HPC

Hands on

We will focus on getting a data processing platform on the Cloud:

• Dask cluster running in Kubernetes,
• With Pangeo libraries available,
• Which we will then use in the final evaluation.

Documentation notebook

This is the first part of you evaluation!