Installing ========== To use the Dask Operator you must install the custom resource definitions, service account, roles, and the operator controller deployment. Quickstart ---------- .. code-block:: console $ helm install --repo https://helm.dask.org --create-namespace -n dask-operator --generate-name dask-kubernetes-operator .. figure:: images/operator-install.gif :align: left 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: .. code-block:: console $ 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``. .. code-block:: console $ kubectl get daskclusters No resources found in default namespace. We can also check the operator pod is running: .. code-block:: console $ 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. Single namespace ^^^^^^^^^^^^^^^^ By default the controller is installed with a ``ClusterRole`` and watches all namespaces. You can also just install it into a single namespace by setting the following options. .. code-block:: console $ helm install -n my-namespace --generate-name dask/dask-kubernetes-operator --set rbac.cluster=false --set kopfArgs="{--namespace=my-namespace}" NAME: dask-kubernetes-operator-1749875935 NAMESPACE: my-namespace STATUS: deployed REVISION: 1 TEST SUITE: None NOTES: Operator has been installed successfully. 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. .. code-block:: console $ 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 :class:`dask_kubernetes.operator.KubeCluster` and :class:`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. .. code-block:: yaml # 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. .. code-block:: console $ 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. .. figure:: images/kubeflow-notebooks-configuration-selector.png :alt: The KubeFlow Notebook Configuration selector showing the "configure dask dashboard" option checked :align: center