Deploy the daskhub helm chart on Opensshift

Question

Pulumi · Accepted Answer

To deploy the DaskHub Helm chart on OpenShift using Pulumi, you will be using the kubernetes.helm.sh/v3.Chart resource from Pulumi's Kubernetes provider. This provider allows you to deploy Helm charts to a Kubernetes cluster, which is compatible with OpenShift.

OpenShift is an enterprise Kubernetes platform, which means Helm charts that work on Kubernetes should also work on OpenShift. However, be aware that there may be additional security constraints and configurations specific to OpenShift that you may need to accommodate.

Before you start, you should have the OpenShift CLI tool, oc, configured to access your OpenShift cluster. Additionally, ensure that Pulumi and the Pulumi Kubernetes provider are installed and set up to manage resources in your cluster.

The kubernetes.helm.sh/v3.Chart resource deploys a chart from a Helm repository. In the program below, I will show you how to deploy the DaskHub chart. You will need to specify the chart name, which is daskhub, and it's usually available in the Dask official Helm chart repository. You can customize the deployment by providing specific values in the values property.

Here's a program that demonstrates how you might set up the DaskHub Helm chart using Pulumi with TypeScript:

import * as k8s from '@pulumi/kubernetes';

// Create a Kubernetes Namespace
const ns = new k8s.core.v1.Namespace("daskhub-namespace", {
    metadata: {
        // You may want to add labels and annotations as necessary.
        name: "daskhub"
    },
});

// Deploy the DaskHub Helm chart into the namespace created above.
const daskhubChart = new k8s.helm.v3.Chart("daskhub", {
    namespace: ns.metadata.name,
    repo: "https://helm.dask.org/", // This is the repository where the Dask Helm charts are hosted.
    chart: "daskhub",
    // You can include custom values for your DaskHub configuration here, as needed.
    // For example, to configure the number of scheduler replicas or worker replicas or resources.
    values: {
        jupyterhub: {
            // This is an example of how you can specify values
            // for the JupyterHub component of DaskHub.
            hub: {
                // Configurations for the JupyterHub hub component.
            },
            singleuser: {
                // Configurations for JupyterHub single user instances.
            }
        },
        dask: {
            // This is where you'd configure Dask component settings.
            scheduler: {
                // Scheduler settings go here.
            },
            worker: {
                // Worker settings go here.
            }
        }
    },
    // Ensure the chart is installed after the namespace is created to avoid any timing issues.
    dependsOn: [ns],
});

// Export the base URL to access the JupyterHub interface.
// You'll need to replace `<external-ip>` with the actual external IP of the service or the route URL
// once it is known upon resource creation or retrieved from existing cluster configurations.
export const jupyterHubUrl = `http://${<external-ip>}/`;

// To verify that the chart has been deployed you can use the `kubectl get pods --namespace daskhub`
// command. This will list all the pods that are deployed as part of the DaskHub Helm chart, including
// the JupyterHub and Dask Scheduler and Worker pods.

This program creates a new namespace for the DaskHub deployment and then uses the Chart resource to deploy the DaskHub Helm chart into that namespace.

Please note that in a real-use case, you would need to fill in the values with appropriate configurations for your deployment needs. The jupyterhub and dask blocks within values are placeholders for you to specify the configuration as per the DaskHub chart's documentation.

The jupyterHubUrl is an export that will let you access the JupyterHub interface in the browser. You'll need to replace the placeholder <external-ip> with the actual IP or domain name that you obtain from your OpenShift cluster, which is typically done via OpenShift routes or a LoadBalancer service.

After writing all the code, you would run it using the Pulumi CLI commands pulumi up to deploy the resources to your OpenShift cluster. You can also use Pulumi's state and diff features to see what changes will be made before you apply them.

When you access the JupyterHub UI, you'll be able to start Dask clusters and access the Dask dashboard from the Jupyter notebooks that get spawned. This integration allows data scientists to work seamlessly with scalable compute resources directly from their notebooks.