1. Deploy the mageai helm chart on Oracle Kubernetes Engine (OKE)


    To deploy the MageAI Helm chart on Oracle Kubernetes Engine (OKE), we'll follow these steps:

    1. Set Up the Oracle Kubernetes Engine (OKE) Cluster: We will assume you have an OKE cluster running and have kubeconfig set up appropriately to interact with your cluster via kubectl.

    2. Create a Kubernetes Namespace (Optional): Helm charts can be deployed into a specific namespace to help organize resources in a clustered environment. By default, if no namespace is specified, Helm installs the chart in the default namespace.

    3. Deploy the Helm Chart: We will use Pulumi's kubernetes.helm.v3.Chart resource, which allows us to deploy Helm charts in a Kubernetes cluster. This will deploy MageAI using the specified Helm chart.

    Below is a detailed Pulumi program written in TypeScript that accomplishes the deployment of the MageAI Helm chart on OKE:

    import * as pulumi from '@pulumi/pulumi'; import * as k8s from '@pulumi/kubernetes'; // Step 1: Reference your Oracle Kubernetes Engine cluster // For this step, ensure you have the correct context set in your kubeconfig to point to your Oracle Kubernetes Engine. const projectName = pulumi.getProject(); // Step 2 (Optional): Create a Kubernetes Namespace for the Helm chart const namespace = new k8s.core.v1.Namespace(`mageai-ns`, { metadata: { // You can name your namespace as per your organizational standards name: `${projectName}-mageai`, }, }, { provider: clusterProvider }); // Ensure to pass the OKE cluster provider if required // Step 3: Deploy the MageAI Helm chart into the Kubernetes cluster const mageaiChart = new k8s.helm.v3.Chart('mageai', { chart: 'mageai', version: '1.0.0', // Replace with the exact chart version you want to deploy fetchOpts: { // Specify Helm repository here if the chart is not in the default Helm repo repo: 'https://example.com/helm-charts', // Replace with the actual Helm repo URL }, namespace: namespace.metadata.name, // You can specify additional Helm values here as needed values: { service: { type: 'LoadBalancer', // Type of service to expose MageAI (could also be NodePort or ClusterIP) }, // Add any other custom values here }, }, { dependsOn: [namespace] }); // Ensure the chart is deployed after the namespace is created // Exports the public IP or hostname that is allocated to the MageAI service export const mageaiEndpoint = mageaiChart.getResourceProperty( 'v1/Service', 'mageai-service', // The name of the mageai service created by the Helm chart 'status', ).apply(status => status.loadBalancer.ingress[0].ip || status.loadBalancer.ingress[0].hostname);

    This program performs the following actions:

    • It imports the required Pulumi packages.
    • It sets up a reference to the Oracle Kubernetes Engine cluster by expecting the correct context to be set in your kubeconfig file.
    • Optionally, it creates a Kubernetes namespace that will be used to deploy and manage resources for MageAI.
    • It deploys the MageAI Helm chart into the Kubernetes cluster within the specified namespace.
    • It exports the endpoint of the MageAI service so you can access it after deployment.

    The mageaiChart variable represents the Helm release for MageAI. The fetchOpts property within the Chart resource is where you would specify the repository containing the MageAI Helm chart if it's not within the default Helm chart repository. You'll need to replace 'https://example.com/helm-charts' with the actual URL of the Helm chart repository that hosts MageAI.

    The values property is a dictionary where you can define the configuration for the Helm chart values. For example, if you want to expose MageAI via a LoadBalancer service, you set the service.type to 'LoadBalancer'.

    After you have written the program, install the necessary Pulumi packages by running npm install with the required package names. Then, to run the program, simply execute pulumi up, which will initiate the deployment process. Pulumi will show you a preview of the resources to be deployed and will prompt you to proceed with the actual deployment.

    Following the execution of this Pulumi program, MageAI should be running on your OKE cluster, and you can access it via the exported mageaiEndpoint.