1. Deploy the ibm-cp4d-watson-machine-learning-instance helm chart on AWS EKS

    TypeScript

    To deploy an IBM Cloud Pak for Data (CP4D) Watson Machine Learning instance using a Helm chart on an Amazon EKS cluster, you will need to set up an EKS cluster, configure your Kubernetes environment, and use the Pulumi Kubernetes Provider to deploy the Helm chart.

    Below is a detailed explanation followed by a TypeScript program that demonstrates how to accomplish this. The program can be used as a starting point and can be customized further for specific use cases.

    1. Setting up an EKS Cluster: The eks.Cluster resource from Pulumi will create a new EKS cluster. This cluster will be the underlying Kubernetes environment where the Watson Machine Learning service will run.

    2. Configuring Kubernetes Provider: After the cluster is up and running, we will use the kubernetes.Provider resource to configure the Kubernetes provider with the newly created EKS cluster's kubeconfig. This allows Pulumi to deploy Kubernetes resources to the correct cluster.

    3. Deploying Helm Chart: The kubernetes.helm.v3.Chart resource is used to deploy a Helm chart to a Kubernetes cluster. The chart can be from any Helm chart repository or a local path, and you need to specify the chart's name, version, and any values you want to override.

    Let's proceed with the TypeScript program:

    import * as pulumi from '@pulumi/pulumi'; import * as aws from '@pulumi/aws'; import * as eks from '@pulumi/eks'; import * as k8s from '@pulumi/kubernetes'; // Step 1: Create an EKS Cluster. const cluster = new eks.Cluster('my-eks-cluster', { // Define the necessary configurations for the EKS cluster here. // For example, you might want a specific version or to set up the cluster in a particular VPC or region. // You can also specify the instance type for the worker nodes. }); // Step 2: Configure the Kubernetes provider to use the EKS cluster we created. const provider = new k8s.Provider('eks-k8s', { kubeconfig: cluster.kubeconfig.apply(JSON.stringify), }); // Step 3: Deploy the IBM CP4D Watson Machine Learning Helm chart to the EKS cluster. const watsonMlChart = new k8s.helm.v3.Chart('watson-ml-chart', { chart: 'ibm-cp4d-watson-machine-learning-instance', version: 'x.y.z', // Specify the chart version you wish to deploy fetchOpts: { // If the chart is from a Helm repository, specify the repository URL here. // If it's a local chart, you can set the `path` option instead. repo: 'https://charts.your-helm-repo.com/', }, // Set any custom values for the Helm chart deployment. // For example, you might need to set specific configurations related to IBM Cloud Pak for Data or Watson Machine Learning. values: { // Your custom values go here. }, }, { provider: provider }); // Expose the EKS cluster's kubeconfig as a stack output. export const kubeconfig = cluster.kubeconfig; // Use this output to access your EKS cluster after deployment.

    Replace x.y.z with the actual version number of the Helm chart that you intend to use. Also, the values field under watsonMlChart should be populated with any custom values that the ibm-cp4d-watson-machine-learning-instance chart requires, which would typically involve specific installation parameters related to IBM Cloud Pak for Data configuration.

    Make sure to meet any prerequisites such as having the necessary rights in AWS and any required Helm repository configured before running the Pulumi program. For more documentation on the specific Pulumi resources and their properties, you can refer to the following links:

    This program will initialize a new Pulumi project for AWS EKS and deploy the specified Helm chart onto the cluster. Once the Pulumi program runs and the stack is successfully updated, the Watson Machine Learning instance should be deployed in your EKS cluster. To interact with the Kubernetes cluster, you can use the exported kubeconfig to configure kubectl or other Kubernetes management tools.


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