Kubernetes and Helm

It is easy to launch a Dask cluster and a Jupyter notebook server on cloud resources using Kubernetes and Helm.

This is particularly useful when you want to deploy a fresh Python environment on Cloud services like Amazon Web Services, Google Compute Engine, or Microsoft Azure.

If you already have Python environments running in a pre-existing Kubernetes cluster, then you may prefer the Kubernetes native documentation, which is a bit lighter weight.

Launch Kubernetes Cluster

This document assumes that you have a Kubernetes cluster and Helm installed.

If this is not the case, then you might consider setting up a Kubernetes cluster on one of the common cloud providers like Google, Amazon, or Microsoft. We recommend the first part of the documentation in the guide Zero to JupyterHub that focuses on Kubernetes and Helm (you do not need to follow all of these instructions). In particular, you don’t need to install JupyterHub.

Alternatively, you may want to experiment with Kubernetes locally using Minikube.

Which Chart is Right for You?

Dask maintains a Helm chart repository containing various charts for the Dask community https://helm.dask.org/ . You will need to add this to your known channels and update your local charts:

helm repo add dask https://helm.dask.org/
helm repo update

We provides two Helm charts. The right one to choose depends on whether you’re deploying Dask for a single user or for many users.

Helm Chart

Use Case


Single-user deployment with one notebook server and one Dask Cluster.


Multi-user deployment with JupyterHub and Dask Gateway.

See Helm Install Dask for a Single User or Helm Install Dask for Multiple Users for detailed instructions on deploying either of these. As you might suspect, deploying dask/daskhub is a bit more complicated since there are more components. If you’re just deploying for a single user we’d recommend using dask/dask.

Helm Install Dask for a Single User

Once your Kubernetes cluster is ready, you can deploy dask using the Dask Helm chart:

helm install my-dask dask/dask

This deploys a dask-scheduler, several dask-worker processes, and also an optional Jupyter server.

Verify Deployment

This might take a minute to deploy. You can check its status with kubectl:

kubectl get pods
kubectl get services

$ kubectl get pods
NAME                                  READY     STATUS              RESTARTS    AGE
bald-eel-jupyter-924045334-twtxd      0/1       ContainerCreating   0            1m
bald-eel-scheduler-3074430035-cn1dt   1/1       Running             0            1m
bald-eel-worker-3032746726-202jt      1/1       Running             0            1m
bald-eel-worker-3032746726-b8nqq      1/1       Running             0            1m
bald-eel-worker-3032746726-d0chx      0/1       ContainerCreating   0            1m

$ kubectl get services
NAME                 TYPE           CLUSTER-IP      EXTERNAL-IP      PORT(S)                      AGE
bald-eel-jupyter     LoadBalancer   80:30173/TCP                  2m
bald-eel-scheduler   LoadBalancer   8786:31166/TCP,80:31626/TCP   2m
kubernetes           ClusterIP     <none>           443/TCP

You can use the addresses under EXTERNAL-IP to connect to your now-running Jupyter and Dask systems.

Notice the name bald-eel. This is the name that Helm has given to your particular deployment of Dask. You could, for example, have multiple Dask-and-Jupyter clusters running at once, and each would be given a different name. Note that you will need to use this name to refer to your deployment in the future. Additionally, you can list all active helm deployments with:

helm list

NAME            REVISION        UPDATED                         STATUS      CHART           NAMESPACE
bald-eel        1               Wed Dec  6 11:19:54 2017        DEPLOYED    dask-0.1.0      default

Connect to Dask and Jupyter

When we ran kubectl get services, we saw some externally visible IPs:

mrocklin@pangeo-181919:~$ kubectl get services
NAME                 TYPE           CLUSTER-IP      EXTERNAL-IP      PORT(S)                       AGE
bald-eel-jupyter     LoadBalancer   80:30173/TCP                  2m
bald-eel-scheduler   LoadBalancer   8786:31166/TCP,80:31626/TCP   2m
kubernetes           ClusterIP     <none>           443/TCP                       48m

We can navigate to these services from any web browser. Here, one is the Dask diagnostic dashboard, and the other is the Jupyter server. You can log into the Jupyter notebook server with the password, dask.

You can create a notebook and create a Dask client from there. The DASK_SCHEDULER_ADDRESS environment variable has been populated with the address of the Dask scheduler. This is available in Python from the dask.config object.

>>> import dask
>>> dask.config.get('scheduler_address')

Although you don’t need to use this address, the Dask client will find this variable automatically.

from dask.distributed import Client, config
client = Client()

Configure Environment

By default, the Helm deployment launches three workers using one core each and a standard conda environment. We can customize this environment by creating a small yaml file that implements a subset of the values in the dask helm chart values.yaml file.

For example, we can increase the number of workers, and include extra conda and pip packages to install on the both the workers and Jupyter server (these two environments should be matched).

# config.yaml

  replicas: 8
      cpu: 2
      memory: 7.5G
      cpu: 2
      memory: 7.5G
      value: numba xarray -c conda-forge
      value: s3fs dask-ml --upgrade

# We want to keep the same packages on the worker and jupyter environments
  enabled: true
      value: numba xarray matplotlib -c conda-forge
      value: s3fs dask-ml --upgrade

This config file overrides the configuration for the number and size of workers and the conda and pip packages installed on the worker and Jupyter containers. In general, we will want to make sure that these two software environments match.

Update your deployment to use this configuration file. Note that you will not use helm install for this stage: that would create a new deployment on the same Kubernetes cluster. Instead, you will upgrade your existing deployment by using the current name:

helm upgrade bald-eel dask/dask -f config.yaml

This will update those containers that need to be updated. It may take a minute or so.

As a reminder, you can list the names of deployments you have using helm list

Check status and logs

For standard issues, you should be able to see the worker status and logs using the Dask dashboard (in particular, you can see the worker links from the info/ page). However, if your workers aren’t starting, you can check the status of pods and their logs with the following commands:

kubectl get pods
kubectl logs <PODNAME>
mrocklin@pangeo-181919:~$ kubectl get pods
NAME                                  READY     STATUS    RESTARTS   AGE
bald-eel-jupyter-3805078281-n1qk2     1/1       Running   0          18m
bald-eel-scheduler-3074430035-cn1dt   1/1       Running   0          58m
bald-eel-worker-1931881914-1q09p      1/1       Running   0          18m
bald-eel-worker-1931881914-856mm      1/1       Running   0          18m
bald-eel-worker-1931881914-9lgzb      1/1       Running   0          18m
bald-eel-worker-1931881914-bdn2c      1/1       Running   0          16m
bald-eel-worker-1931881914-jq70m      1/1       Running   0          17m
bald-eel-worker-1931881914-qsgj7      1/1       Running   0          18m
bald-eel-worker-1931881914-s2phd      1/1       Running   0          17m
bald-eel-worker-1931881914-srmmg      1/1       Running   0          17m

mrocklin@pangeo-181919:~$ kubectl logs bald-eel-worker-1931881914-856mm
EXTRA_CONDA_PACKAGES environment variable found.  Installing.
Fetching package metadata ...........
Solving package specifications: .
Package plan for installation in environment /opt/conda/envs/dask:
The following NEW packages will be INSTALLED:
    fasteners: 0.14.1-py36_2 conda-forge
    monotonic: 1.3-py36_0    conda-forge
    zarr:      2.1.4-py36_0  conda-forge
Proceed ([y]/n)?
monotonic-1.3- 100% |###############################| Time: 0:00:00  11.16 MB/s
fasteners-0.14 100% |###############################| Time: 0:00:00 576.56 kB/s

Delete a Helm deployment

You can always delete a helm deployment using its name:

helm delete bald-eel --purge

Note that this does not destroy any clusters that you may have allocated on a Cloud service (you will need to delete those explicitly).

Avoid the Jupyter Server

Sometimes you do not need to run a Jupyter server alongside your Dask cluster.

  enabled: false

Helm Install Dask for Multiple Users

The dask/daskhub Helm Chart deploys JupyterHub, Dask Gateway, and configures the two to work well together. In particular, Dask Gateway is registered as a JupyterHub service so that Dask Gateway can re-use JupyterHub’s authentication, and the JupyterHub environment is configured to connect to the Dask Gateway without any arguments.


The dask/daskhub helm chart came out of the Pangeo project, a community platform for big data geoscience.

The dask/daskhub helm chart uses the JupyterHub and Dask-Gateway helm charts. You’ll want to consult the JupyterHub helm documentation and and Dask Gateway helm documentation for further customization. The default values are at https://github.com/dask/helm-chart/blob/main/daskhub/values.yaml.

Verify that you’ve set up a Kubernetes cluster and added Dask’s helm charts:

$ helm repo add dask https://helm.dask.org/
$ helm repo update

JupyterHub and Dask Gateway require a few secret tokens. We’ll generate them on the command line and insert the tokens in a secrets.yaml file that will be passed to Helm.

Run the following command, and copy the output. This is our token-1.

$ openssl rand -hex 32  # generate token-1

Run command again and copy the output again. This is our token-2.

$ openssl rand -hex 32  # generate token-2

Now substitute those two values for <token-1> and <token-2> below. Note that <token-2> is used twice, once for jupyterhub.hub.services.dask-gateway.apiToken, and a second time for dask-gateway.gateway.auth.jupyterhub.apiToken.

# file: secrets.yaml
    secretToken: "<token-1>"
        apiToken: "<token-2>"

        apiToken: "<token-2>"

Now we’re ready to install DaskHub

$ helm upgrade --wait --install --render-subchart-notes \
    dhub dask/daskhub \

The output explains how to find the IPs for your JupyterHub depoyment.

$ kubectl get service proxy-public
NAME           TYPE           CLUSTER-IP      EXTERNAL-IP      PORT(S)                      AGE
proxy-public   LoadBalancer   443:31587/TCP,80:30500/TCP   2m40s

Creating a Dask Cluster

To create a Dask cluster on this deployment, users need to connect to the Dask Gateway

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

Depending on the configuration, users may need to cluster.scale(n) to get workers. See https://gateway.dask.org/ for more on Dask Gateway.

Matching the User Environment

Dask Clients will be running the JupyterHub’s singleuser environment. To ensure that the same environment is used for the scheduler and workers, you can provide it as a Gateway option and configure the singleuser environment to default to the value set by JupyterHub.

# config.yaml

      optionHandler: |
        from dask_gateway_server.options import Options, Integer, Float, String
        def option_handler(options):
            if ":" not in options.image:
                raise ValueError("When specifying an image you must also provide a tag")
            return {
                "image": options.image,
        c.Backend.cluster_options = Options(
            String("image", default="pangeo/base-notebook:2020.07.28", label="Image"),

The user environment will need to include dask-gateway. Any packages installed manually after the singleuser pod started will not be included in the worker environment.