Today we’re announcing the release of thedaskhubhelm chart. This is a Helm chart to easily installJupyterHub and Dask for multiple users on aKubernetes Cluster. If you’re managing deployment for many people that needsinteractive, scalable computing (say for a class of students, a data scienceteam, or a research lab) then dask/daskhub might be right for you.

You can install dask/daskhub on a Kubernetes cluster today with

helm repo add dask https://helm.dask.org/
helm repo update
helm upgrade --install dhub dask/daskhub

History

The dask/daskhub helm chart is an evolution of the Pangeohelm chart, which came out of that community’s attempts to do big datageoscience on the cloud. We’re very grateful to have years of experience usingDask and JupyterHub together. Pangeo was always aware that there wasn’t anythinggeoscience-specific to their Helm chart and so were eager to contribute it toDask to share the maintenance burden. In the process of moving it over to Dask’schart repository we took the opportunity to clean up a few rough edges.

It’s interesting to read the originalannouncement of Pangeo’s JupyterHubdeployment. A lot has improved, and we hope that this helm chart assists moregroups in deploying JupyterHubs capable of scalable computations with Dask.

Details

Internally, the DaskHub helm chart is relatively simple combination of theJupyterHub and DaskGateway helm charts. The only additionalmagic is some configuration to

1. Register Dask Gateway as a JupyterHubservice.
2. Set environment variables to make using Dask Gateway easy for your users.

With the default configuration, your users will be able to create and connect toDask Clusters, including their dashboards, with a simple

>>> from dask_gateway import GatewayCluster
>>> cluster = GatewayCluster()
>>> client = cluster.get_client()

Check out thedocumentation fordetails and let us know if you run into any difficulties.