1. Deploy the kserve-inference helm chart on Azure Managed Openshift Service

    TypeScript

    To deploy the kserve-inference Helm chart on Azure Managed OpenShift Service using Pulumi, you'll need to complete several tasks:

    1. Provision an Azure Managed OpenShift Cluster: Set up the managed OpenShift Cluster where your application will run.
    2. Install the Helm Chart on the Cluster: Once the cluster is set up, you'll need to install the Helm chart for kserve-inference.

    Below, you'll find a Pulumi program written in TypeScript that carries out these tasks. This assumes you have some prerequisite knowledge and have set up Pulumi along with the necessary Azure credentials configured on your system. If you haven't, I recommend visiting the Pulumi Getting Started page and following instructions to set up the Pulumi CLI and Azure provider.

    Here's the detailed program to deploy kserve-inference using Pulumi:

    import * as pulumi from '@pulumi/pulumi'; import * as azure_native from '@pulumi/azure-native'; import * as k8s from '@pulumi/kubernetes'; // Step 1: Provision an Azure Managed OpenShift Cluster // Create a resource group to contain the OpenShift cluster const resourceGroup = new azure_native.resources.ResourceGroup('openshiftResourceGroup'); // Provision an OpenShift Managed Cluster // Replace `<Your OpenShift Version>` with a valid version for the Managed OpenShift service in Azure const openshiftCluster = new azure_native.containerservice.OpenShiftManagedCluster('openshiftCluster', { resourceGroupName: resourceGroup.name, location: resourceGroup.location, // Define more detailed configuration such as the network profiles, authentication profiles, etc. openShiftVersion: '<Your OpenShift Version>', // Define agent pool profiles according to your requirements agentPoolProfiles: [{ name: 'agentpool', count: 3, vmSize: 'Standard_D4s_v3', osType: 'Linux', role: 'compute', }], // Define the master profile for the cluster masterPoolProfile: { count: 3, vmSize: 'Standard_D4s_v3', }, // More configuration can be added as needed }); // Step 2: Install the Helm Chart on the Cluster // Set up k8s provider to deploy the Helm chart to the OpenShift Cluster const k8sProvider = new k8s.Provider('k8sProvider', { kubeconfig: openshiftCluster.config.kubeconfig, }); // Deploy the kserve-inference Helm chart using the k8s provider const kserveChart = new k8s.helm.v3.Chart('kserve-inference', { chart: 'kserve-inference', // Use the appropriate repository or chart version as required fetchOpts: { repo: 'http://kserve.github.io/charts', }, // Specify any values you want to override in the Helm chart values: { // Provide necessary overrides here }, }, { provider: k8sProvider }); // Export the cluster's kubeconfig export const kubeconfig = openshiftCluster.config.kubeconfig;

    In this program:

    • We first create a resource group to hold the OpenShift cluster.
    • We provision a Managed OpenShift Cluster. You must replace <Your OpenShift Version> with a valid OpenShift version.
    • We create a Kubernetes provider that knows how to communicate with the Managed OpenShift cluster we've just created.
    • We then deploy the kserve-inference Helm chart to the cluster using the Kubernetes provider.

    Keep in mind that you'll need to specify any necessary Helm chart values to configure kserve-inference appropriately for your use case.

    Also, be sure to use the correct versions for your cluster and your Helm chart, and consider that you might need to specify other parameters depending on your specific requirements.

    To run this program:

    1. Save the code to a file, e.g., index.ts.
    2. Run pulumi up to preview and deploy the changes.

    Upon completion, the kserve-inference application will be deployed in the OpenShift cluster. You can access the kubeconfig to interact with your cluster using kubectl or the OpenShift CLI.


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