Announcing the DaskHub Helm Chart DaskHub
By Tom Augspurger
Today we’re announcing the release of the
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 https://helm.dask.org/ helm repo update helm upgrade --install dhub dask/daskhub
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
- Register Dask Gateway as a JupyterHub service.
- 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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