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Jupyter Notebooks have become a mainstream tool for interactive computing inevery field of science. Jupyter Notebooks are suitable as companionapplications for Science Gateways, providing more flexibility andpost-processing capability to the users. Moreover they are often used intraining events and workshops to provide immediate access to a pre-configuredinteractive computing environment. The Jupyter team released the JupyterHub webapplication to provide a platform where multiple users can login and access aJupyter Notebook environment. When the number of users and memory requirementsare low, it is easy to setup JupyterHub on a single server. However, setupbecomes more complicated when we need to serve Jupyter Notebooks at scale totens or hundreds of users. In this paper we will present three strategies fordeploying JupyterHub at scale on XSEDE resources. All options share thedeployment of JupyterHub on a Virtual Machine on XSEDE Jetstream. In the firstscenario, JupyterHub connects to a supercomputer and launches a single node jobon behalf of each user and proxies back the Notebook from the computing nodeback to the user's browser. In the second scenario, implemented in the contextof a XSEDE consultation for the IRIS consortium for Seismology, we deployDocker in Swarm mode to coordinate many XSEDE Jetstream virtual machines toprovide Notebooks with persistent storage and quota. In the last scenario weinstall the Kubernetes containers orchestration framework on Jetstream toprovide a fault-tolerant JupyterHub deployment with a distributed filesystemand capability to scale to thousands of users. In the conclusion section weprovide a link to step-by-step tutorials complete with all the necessarycommands and configuration files to replicate these deployments.

Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops