1. Deploy the ibm-cp4d-watson-machine-learning-instance helm chart on Linode Kubernetes Engine


    To deploy the IBM Cloud Pak for Data (cp4d) Watson Machine Learning instance using a Helm chart to a Kubernetes cluster hosted in Linode Kubernetes Engine (LKE), you'll need to follow some steps in Pulumi. First, however, it's important to note that as of my knowledge cutoff in early 2023, Linode was not officially part of the set of cloud providers supported by Pulumi, meaning there isn't a dedicated Pulumi provider for Linode.

    Despite this, you can still use Pulumi to deploy to a Linode Kubernetes cluster. This is because LKE provides Kubernetes clusters that conform to the standard Kubernetes API, which means you can interact with them using Pulumi's Kubernetes provider.

    Here's how you can achieve the deployment:

    1. Install Pulumi CLI: Ensure Pulumi CLI is installed on your system. This facilitates infrastructure deployment via code.

    2. Configure Pulumi to use the Kubernetes provider: This is required to interact with your K8s cluster.

    3. Set up access to your Linode Kubernetes Cluster: Obtain your kubeconfig from Linode to allow Pulumi to interact with your cluster.

    4. Write your Pulumi code: You'll need to write a program in TypeScript to use Pulumi's Kubernetes provider to deploy the IBM cp4d Watson Machine Learning instance using a Helm chart.

    5. Setup Helm Chart details: You will specify the details such as chart name, version, and values according to the requirements of the IBM cp4d service.

    Below is an example of the detailed explanation and Pulumi program in TypeScript that performs this deployment:

    Detailed Explanation


    Before you begin, you need to have the following in place:

    • An active Linode account with an LKE cluster running.
    • kubectl and helm installed locally to interact with Kubernetes clusters.
    • The kubeconfig file for your Linode cluster, which allows you to connect to your Kubernetes cluster.

    Pulumi Program to Deploy IBM cp4d Watson Machine Learning Instance on LKE:

    The following program uses Pulumi's Kubernetes package to deploy the ibm-cp4d-watson-machine-learning-instance Helm chart to your LKE cluster.

    import * as k8s from "@pulumi/kubernetes"; // Your kubeconfig file's content should be set in a way Pulumi can access, // this could be set in an environment variable, Pulumi's config, or read from a file directly. const kubeconfig = process.env.KUBECONFIG_CONTENT; // Create a Kubernetes provider instance using the kubeconfig obtained from Linode. const provider = new k8s.Provider("lke-k8s", {kubeconfig}); // Define the Helm chart details const watsonMLChart = new k8s.helm.v3.Chart("watson-ml-chart", { chart: "ibm-cp4d-watson-machine-learning-instance", // Specify the Helm repository containing the chart fetchOpts: { repo: "https://raw.githubusercontent.com/IBM/charts/master/repo/entitled/", }, // Pass any required values to the Helm chart values: { // ... set the values required for 'ibm-cp4d-watson-machine-learning-instance' }, // Specify the namespace where the chart will be installed or default namespace will be used namespace: "watson-ml", }, { provider }); // Export the endpoints once the chart is deployed export const watsonMLEndpoint = watsonMLChart.getResourceProperty("v1/Service", "watson-ml-service", "status");

    In this program:

    • We import the necessary Pulumi Kubernetes package.
    • We create a Kubernetes provider that Pulumi uses to communicate with the Linode Kubernetes cluster, configured with kubeconfig obtained from Linode.
    • We define a Helm chart resource, specifying details such as the chart name (ibm-cp4d-watson-machine-learning-instance), the repository where the chart can be found, any values required for the Helm chart configuration, and the Kubernetes namespace where the Helm chart should be deployed.
    • We export the endpoint property of the service created by Helm, which you can use to access the Watson Machine Learning instance.

    Next Steps:

    After writing this program:

    1. Run pulumi up to deploy your changes.
    2. Inspect the output, ensure no errors are reported, and approve the deployment if all are okay.
    3. Wait for the deployment to complete.
    4. Use the exported endpoint to access the Watson Machine Learning instance.

    Please replace the placeholder for the kubeconfig with the actual content, and likewise, update the chart values with the relevant parameters necessary for the cp4d Watson Machine Learning instance. Make sure to check if the Helm chart name and repository URL are current and accurate. If a different chart version is needed, specify it with the version: "<desired-version>" property inside the Chart resource options.