Dedicated Jupyter Servers

The default Jupyter environment available to all Chameleon users is a bit limited: you are working within a shared environment and as such there are some practical limitations around the amount of CPU cores and memory you can utilize. More intensive analytical workflows may function better from within a dedicated Jupyter server for use by you and/or other members of your project.

Using the Appliance Catalog

The Chameleon Appliance Catalog provides a JupyterHub appliance that is functionally equivalent to the shared Jupyter environment. The appliance will allow you to reserve a Chameleon bare metal node and provision it with the JupyterHub application, along with a Floating IP Address that allows access over the public Internet. Any Jupyter Notebook servers managed by this multi-user environment will have access to the underlying resources on whatever node you have reserved, removing the limits around CPU and memory usage.

Using Trovi

Trovi also has a JupyterHub artifact you can instantiate on Chameleon. There is no material difference between this method and the Appliance Catalog method and it can serve as a nice introduction to the Sharing Portal if you are not already familiar with it. With this method, you actually provision your own JupyterHub server via the shared default Chameleon JupyterHub; it’s JupyterHub all the way down!