1. Deploy the ibm-cp4d-watson-machine-learning-instance helm chart on Digital Ocean Kubernetes Service


    Deploying an IBM Cloud Pak for Data (CP4D) Watson Machine Learning instance on Digital Ocean Kubernetes Service using Helm requires several steps. We will create a Kubernetes cluster on Digital Ocean, configure our environment for Helm, and then use the Helm package to deploy the IBM CP4D Watson Machine Learning instance.

    Below, we'll outline these steps as a comprehensive Pulumi program written in TypeScript.

    Step 1: Create a Digital Ocean Kubernetes Cluster

    Using Pulumi's digitalocean provider, we'll begin by creating a Kubernetes cluster on Digital Ocean. The following program segment demonstrates how to do this:

    import * as digitalocean from "@pulumi/digitalocean"; // Create a new Digital Ocean Kubernetes cluster const cluster = new digitalocean.KubernetesCluster("do-cluster", { region: digitalocean.Regions.NYC3, // Choose the region that is closest to you or your users version: "latest", // Specify the version of Kubernetes you'd like to use nodePool: { name: "default", size: "s-2vcpu-2gb", // Select the size that matches your workload needs nodeCount: 2, // Define the number of nodes in the pool }, });

    In this code snippet, we are creating a Kubernetes cluster with a default node pool in the New York 3 (NYC3) region, using the latest version of Kubernetes available on Digital Ocean, with a machine type suitable for small workloads (this might need to be adjusted for the production use of IBM CP4D).

    Step 2: Configure Kubernetes and Helm

    Before deploying the Helm chart, we need to configure our local environment to communicate with the newly created Kubernetes cluster on Digital Ocean:

    import * as k8s from "@pulumi/kubernetes"; // Obtain the Kubeconfig file from the Digital Ocean cluster we created const kubeconfig = cluster.kubeConfigs[0].rawConfig; // Create a Kubernetes provider instance using the kubeconfig from Digital Ocean const k8sProvider = new k8s.Provider("do-k8s-provider", { kubeconfig: kubeconfig, });

    This code extracts the kubeconfig from the Kubernetes cluster and sets up the Pulumi Kubernetes provider with it. This provider will be used to communicate with your cluster for all subsequent operations.

    Step 3: Deploy IBM CP4D Watson Machine Learning Instance using Helm

    Once the cluster and provider are configured, we can proceed to deploy the IBM CP4D Watson Machine Learning instance using a Helm chart:

    import * as helm from "@pulumi/kubernetes/helm"; // Define the Helm chart for IBM CP4D Watson Machine Learning instance. const watsonMLChart = new helm.v3.Chart("ibm-cp4d-watson-ml", { chart: "ibm-cp4d-watson-machine-learning-instance", fetchOpts: { repo: "https://raw.githubusercontent.com/IBM/charts/master/repo/stable/", }, // Specify the chart values which are required for a customized installation // You might need to include configuration values specific to IBM CP4D Watson Machine Learning values: { // ... Insert appropriate values for the chart deployment ... }, }, { provider: k8sProvider }); // Ensure to pass the Kubernetes provider we created // Export the public endpoint to access Watson Machine Learning export const watsonMLEndpoint = watsonMLChart.getResourceProperty("v1/Service", "ibm-cp4d-watson-ml", "status").apply(status => status.loadBalancer.ingress[0].ip);

    This code defines the deployment of the IBM CP4D Watson Machine Learning instance via Helm. It references a Helm chart named ibm-cp4d-watson-machine-learning-instance from the IBM charts repository. The values field can be used to customize your Helm chart deployment with the necessary configuration options.

    Full Program

    Here's the entire Pulumi program that performs the steps described above:

    import * as digitalocean from "@pulumi/digitalocean"; import * as k8s from "@pulumi/kubernetes"; import * as helm from "@pulumi/kubernetes/helm"; // Step 1: Create the Digital Ocean Kubernetes