1. Deploy the flyte-sandbox helm chart on Azure Managed Openshift Service


    To deploy the Flyte sandbox Helm chart on an Azure Managed OpenShift Service, you will need to accomplish two major steps using Pulumi:

    1. Provision an Azure Red Hat OpenShift (ARO) cluster.
    2. Deploy the Flyte sandbox Helm chart on the ARO cluster using the Pulumi Kubernetes provider.

    For the first step, we will use the azure-native.containerservice.OpenShiftManagedCluster resource to create an ARO cluster. It is important to note that you will need to have a service principal with appropriate permissions for creating resources in your Azure subscription.

    For the second step, we will install the Flyte sandbox on the created cluster using the Pulumi Kubernetes provider with the kubernetes.helm.v3.Chart resource, which represents a Helm chart in a Pulumi program.

    Below is a TypeScript program demonstrating these steps. It assumes that you have authenticated with Azure and set up the necessary credentials for Pulumi to communicate with your Azure subscription and the Kubernetes cluster.

    import * as azure_native from "@pulumi/azure-native"; import * as k8s from "@pulumi/kubernetes"; import * as pulumi from "@pulumi/pulumi"; // Create the Azure Red Hat OpenShift cluster const cluster = new azure_native.containerservice.OpenShiftManagedCluster("myOpenshiftCluster", { location: "eastus", // Replace with your Azure region resourceName: "myOpenshiftCluster", resourceGroupName: "myResourceGroup", // Replace with your resource group openShiftVersion: "4.3", // Specify the OpenShift version // Define the agent pools for the cluster agentPoolProfiles: [{ name: "compute", // Name of the agent pool count: 1, // Number of nodes in the agent pool vmSize: "Standard_D4s_v3", // VM size for the nodes osType: "Linux", // Operating system type role: "compute", subnetCidr: "", // Subnet CIDR }], // Define the master pool profile masterPoolProfile: { name: "master", count: 3, // Number of master nodes vmSize: "Standard_D4s_v3", // VM size for the master nodes subnetCidr: "", // Subnet CIDR }, }); // Once the Openshift cluster is provisioned, create a Kubernetes provider instance using its kubeconfig const openshiftK8sProvider = new k8s.Provider("openshiftK8sProvider", { kubeconfig: cluster.kubeConfigRaw, }); // Create a new Helm Chart for Flyte sandbox deployment const flyteChart = new k8s.helm.v3.Chart("flyte-sandbox", { chart: "flyte", version: "v0.1.0", // Specify the chart version you wish to deploy namespace: "flyte", // Namespace where the Flyte sandbox should be installed fetchOpts: { repo: "https://flyteorg.github.io/flyte", // The Helm repository for the chart }, values: { // You can specify the values for the chart here }, }, { provider: openshiftK8sProvider }); // Export the frontend address by querying the load balancer created by the chart export const frontendAddress = flyteChart.getResource("v1/Service", "flyte/flyte-sandbox") .apply(svc => svc.status.loadBalancer.ingress[0].ip);


    • The program initializes a new ARO cluster with specified node counts, VM sizes, and subnet CIDR block for the agent and master profiles.
    • A Pulumi Kubernetes provider is defined using the kubeconfig from the deployed OpenShift cluster to allow Pulumi to interact with the cluster.
    • The Flyte sandbox Helm chart is deployed using this provider. We specify the chart name (flyte), version (v0.1.0), namespace (flyte), and the Flyte-specific Helm repository (https://flyteorg.github.io/flyte) where the chart can be found.
    • Finally, a Pulumi export statement is used to output the IP address of the Flyte frontend service. This IP address can be used to access the Flyte dashboard after deployment.

    Make sure to update the location, resourceGroupName, version, values, and other fields as necessary to match your intended configuration.

    After writing your code, you should install the npm dependencies running npm install @pulumi/azure-native @pulumi/kubernetes @pulumi/pulumi and then execute it by running pulumi up. This program assumes that you are already authenticated with Azure CLI and have the proper permissions within your Azure subscription.

    As this is a simple deployment, certain complex scenarios such as handling multiple environments, secrets management, or customized deployments may require additional code not covered in this example.