1. Deploy the ibm-cp4d-watson-machine-learning-instance helm chart on Azure Managed Openshift Service


    To deploy the IBM Cloud Pak for Data (CP4D) Watson Machine Learning instance using a Helm chart on Azure Managed OpenShift Service, we'll go through a high-level overview of steps you would typically follow:

    1. Set up an Azure Managed OpenShift Service instance.
    2. Install the Helm CLI and configure it to work with your OpenShift cluster.
    3. Use the Helm CLI to deploy the IBM CP4D Watson Machine Learning instance using the provided Helm chart.

    In our Pulumi program, we'll focus on the first step, which involves provision an OpenShift Cluster on Azure. Afterwards, I'll provide you with guidance on how to proceed with the next steps, which involve the Helm CLI and are typically executed directly through the command line interface or through scripts, rather than Pulumi.

    First, let's provision an Azure Red Hat OpenShift Cluster with Pulumi using the azure-native.redhatopenshift.OpenShiftCluster resource. This resource allows you to create and manage an OpenShift cluster on Azure.

    import * as pulumi from "@pulumi/pulumi"; import * as azure_native from "@pulumi/azure-native"; // Create an Azure Resource Group const resourceGroup = new azure_native.resources.ResourceGroup("resourceGroup", { location: "eastus", }); // Provision an Azure Red Hat OpenShift Cluster const openshiftCluster = new azure_native.redhatopenshift.OpenShiftCluster("openshiftCluster", { resourceName: "myOpenshiftCluster", resourceGroupName: resourceGroup.name, location: resourceGroup.location, clusterProfile: { domain: "exampledomain", // Replace with a unique domain version: "4.3.24", // Specify the OpenShift version resourceGroupId: resourceGroup.id, }, masterProfile: { vmSize: "Standard_D16s_v3", // Specify the size of the VMs for the master nodes subnetId: "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/virtualNetworks/{vnetName}/subnets/{subnetName}", // Replace with your subnet id }, networkProfile: { podCidr: "", serviceCidr: "", }, workerProfiles: [{ name: "worker", // Name of the worker profile count: 3, // Number of worker nodes vmSize: "Standard_D4s_v3", // Specify the size of the VMs for the worker nodes subnetId: "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/virtualNetworks/{vnetName}/subnets/{subnetName}", // Replace with your subnet id }], servicePrincipalProfile: { clientId: "your-service-principal-client-id", // Replace with your service principal client id clientSecret: "your-service-principal-client-secret", // Replace with your service principal client secret }, tags: { project: "Pulumi OpenShift Deployment", }, }); // Export the cluster's API server URL to access the OpenShift web console export const openshiftClusterApiServerUrl = openshiftCluster.apiserverProfile.url;

    Replace placeholder values such as {subscriptionId}, {resourceGroupName}, {vnetName}, {subnetName}, your-service-principal-client-id, and your-service-principal-client-secret with actual values related to your Azure subscription and desired configuration.

    After setting up the OpenShift cluster, you would typically:

    • Install the OpenShift CLI (oc) and the Helm CLI on your local machine or on a CI/CD agent.
    • Configure oc to authenticate against your OpenShift cluster.
    • Add the IBM Helm chart repository, update the Helm repo to fetch the latest charts, and install the IBM CP4D Watson Machine Learning chart to your cluster.

    While these tasks are beyond the scope of Pulumi automation (as Pulumi focuses on provisioning infrastructure), you can carry out these tasks using scripts or manually via a terminal.

    For more detailed instructions, refer to the official OpenShift and IBM CP4D documentation. You'll find information on how to set up the CLI tools, configure them, and use Helm charts for installation of complex applications like IBM Cloud Pak for Data.