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Installing

To use the Dask Operator you must install the custom resource definitions, service account, roles, and the operator controller deployment.

Quickstart

$ helm install --repo https://helm.dask.org --create-namespace -n dask-operator --generate-name dask-kubernetes-operator

Installing with Helm

The operator has a Helm chart which can be used to manage the installation of the operator. The chart is published in the Dask Helm repo repository, and can be installed via:

$ helm repo add dask https://helm.dask.org
"dask" has been added to your repositories

$ helm repo update
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "dask" chart repository
Update Complete. ⎈Happy Helming!⎈

$ helm install --create-namespace -n dask-operator --generate-name dask/dask-kubernetes-operator
NAME: dask-kubernetes-operator-1666875935
NAMESPACE: dask-operator
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Operator has been installed successfully.

Then you should be able to list your Dask clusters via kubectl.

$ kubectl get daskclusters
No resources found in default namespace.

We can also check the operator pod is running:

$ kubectl get pods -A -l app.kubernetes.io/name=dask-kubernetes-operator
NAMESPACE       NAME                                        READY   STATUS    RESTARTS   AGE
dask-operator   dask-kubernetes-operator-775b8bbbd5-zdrf7   1/1     Running   0          74s

Warning

Please note that Helm does not support updating or deleting CRDs. If updates are made to the CRD templates in future releases (to support future k8s releases, for example) you may have to manually update the CRDs or delete/reinstall the Dask Operator.

Installing with Manifests

If you prefer to install the operator from static manifests with kubectl and set configuration options with tools like kustomize you can generate the default manifests with:

$ helm template --include-crds --repo https://helm.dask.org release dask-kubernetes-operator | kubectl apply -f -

Kubeflow

In order to use the Dask Operator with Kubeflow you need to perform some extra installation steps.

User permissions

Kubeflow doesn’t know anything about our Dask custom resource definitions so we need to update the kubeflow-kubernetes-edit cluster role. This role allows users with cluster edit permissions to create pods, jobs and other resources and we need to add the Dask custom resources to that list. Edit the existing clusterrole and add a new rule to the rules section for kubernetes.dask.org that allows all operations on all custom resources in our API namespace.

$ kubectl patch clusterrole kubeflow-kubernetes-edit --type="json" --patch '[{"op": "add", "path": "/rules/-", "value": {"apiGroups": ["kubernetes.dask.org"],"resources": ["*"],"verbs": ["*"]}}]'
clusterrole.rbac.authorization.k8s.io/kubeflow-kubernetes-edit patched

Dashboard access

If you are using the Jupyter Notebook service in KubeFlow there are a couple of extra steps you need to do to be able to access the Dask dashboard. The dashboard will be running on the scheduler pod and accessible via the scheduler service, so to access that your Jupyter container will need to have the jupyter-server-proxy extension installed. If you are using the Dask Jupter Lab extension this will be installed automatically for you.

By default the proxy will only allow proxying other services running on the same host as the Jupyter server, which means you can’t access the scheduler running in another pod. So you need to set some extra config to tell the proxy which hosts to allow. Given that we can already execute arbitrary code in Jupyter (and therefore interact with other services within the Kubernetes cluster) we can allow all hosts in the proxy settings with c.ServerProxy.host_allowlist = lambda app, host: True.

The dask_kubernetes.operator.KubeCluster and distributed.Client objects both have a dashboard_link attribute that you can view to find the URL of the dashboard, and this is also used in the widgets shown in Jupyter. The default link will not work on KubeFlow so you need to change this to "{NB_PREFIX}/proxy/{host}:{port}/status" to ensure it uses the Jupyter proxy.

To apply these configuration options to the Jupyter pod you can create a PodDefault configuration object that can be selected when launching the notebook. Create a new file with the following contents.

# configure-dask-dashboard.yaml
apiVersion: "kubeflow.org/v1alpha1"
kind: PodDefault
metadata:
name: configure-dask-dashboard
spec:
selector:
   matchLabels:
      configure-dask-dashboard: "true"
desc: "configure dask dashboard"
env:
   - name: DASK_DISTRIBUTED__DASHBOARD__LINK
      value: "{NB_PREFIX}/proxy/{host}:{port}/status"
volumeMounts:
   - name: jupyter-server-proxy-config
   mountPath: /root/.jupyter/jupyter_server_config.py
   subPath: jupyter_server_config.py
volumes:
   - name: jupyter-server-proxy-config
   configMap:
      name: jupyter-server-proxy-config
---
apiVersion: v1
kind: ConfigMap
metadata:
name: jupyter-server-proxy-config
data:
jupyter_server_config.py: |
   c.ServerProxy.host_allowlist = lambda app, host: True

Then apply this to your KubeFlow user’s namespace with kubectl. For example with the default user@example.com user it would be.

$ kubectl apply -n kubeflow-user-example-com -f configure-dask-dashboard.yaml

Then when you launch your Jupyter Notebook server be sure to check the configure dask dashboard configuration option.

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