Today we’re announcing the release of the daskhub helm chart. This is a Helm chart to easily install JupyterHub and Dask for multiple users on a Kubernetes Cluster. If you’re managing deployment for many people that needs interactive, scalable computing (say for a class of students, a data science team, 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
helm repo update
helm upgrade --install dhub dask/daskhub


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

It’s interesting to read the original announcement of Pangeo’s JupyterHub deployment. A lot has improved, and we hope that this helm chart assists more groups in deploying JupyterHubs capable of scalable computations with Dask.


Internally, the DaskHub helm chart is relatively simple combination of the JupyterHub and Dask Gateway helm charts. The only additional magic is some configuration to

  1. Register Dask Gateway as a JupyterHub service.
  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 to Dask Clusters, including their dashboards, with a simple

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

Check out the documentation for details and let us know if you run into any difficulties.

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