1. AI Model Metadata Storage on Kubernetes with MySQL


    To set up AI model metadata storage on Kubernetes with a MySQL database, we will take the following steps:

    1. Deploy Kubernetes Cluster: We'll need a Kubernetes cluster. This can be done using cloud-specific Pulumi libraries like pulumi_eks for AWS, pulumi_azure_native for Azure, or pulumi_gcp for Google Cloud.

    2. Deploy MySQL on Kubernetes: To manage stateful applications like MySQL on Kubernetes, we can use StatefulSets. Pulumi doesn't interact with Kubernetes manifests directly, instead, it uses Custom Resources defined in respective Pulumi programming model packages like pulumi_kubernetes.

    3. Use a Helm Chart for MySQL: Helm is a package manager for Kubernetes, and there is a stable MySQL chart available which we can use to deploy MySQL onto the Kubernetes cluster.

    4. Create a Kubernetes Service: A Kubernetes Service is needed to expose the MySQL deployment so that it can be accessed within the cluster.

    The following program outlines these steps. Make sure you have Pulumi installed, and you've set up the chosen cloud provider and Kubernetes on your Pulumi CLI before starting.

    Now, let's write the Pulumi program in Python:

    import pulumi import pulumi_kubernetes as k8s from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Initialize a Pulumi Kubernetes provider by pointing it to the kubeconfig file # that Pulumi can use to communicate with a Kubernetes cluster. kubeconfig = pulumi.Config("kubernetes").require("kubeconfig") k8s_provider = k8s.Provider("k8s-provider", kubeconfig=kubeconfig) # Create a new Helm Chart for MySQL, which will be deployed on the Kubernetes cluster. # The set of configurations for mysql can be adjusted according to your needs. # Here, for simplicity, we deploy the chart with default values. mysql_chart = Chart( "mysql", ChartOpts( chart="mysql", version="1.6.4", fetch_opts=k8s.helm.v3.FetchOpts( repo="https://charts.bitnami.com/bitnami" ), ), providers={"kubernetes": k8s_provider}, ) # The following will expose the MySQL service within the Kubernetes cluster on a set port. # You can configure the service as per the requirements like setting the type as LoadBalancer # for external exposure or using ClusterIP for internal use. mysql_service = mysql_chart.get_resource("v1/Service", "mysql") # Export the cluster service IP and Port to enable accessing MySQL pulumi.export("mysql_cluster_ip", mysql_service.status.apply(lambda status: status.load_balancer.ingress[0].ip)) pulumi.export("mysql_cluster_port", mysql_service.spec.apply(lambda spec: spec.ports[0].port)) # Note: The service IP and port may only be available if the service type is set to LoadBalancer. # For other types like ClusterIP or NodePort, you'd access them using internal cluster methods.

    In this program, we've initialized a Provider which represents the Kubernetes cluster using pulumi_kubernetes. This provider requires access to a kubeconfig file that contains the necessary credentials to authenticate with the Kubernetes cluster.

    We then create a MySQL Helm Chart using pulumi_kubernetes.helm.v3.Chart. The ChartOpts specifies the MySQL chart version and the repository URL.

    Lastly, we export the IP and port of the MySQL service in the Kubernetes cluster. Depending on the service type, you can access MySQL either internally (within the cluster) or through an external IP if the service type is set as LoadBalancer.

    Please note that in production you would also need to carefully manage secrets, storage, and backups when deploying a database like MySQL, which are not covered in this simple example. Pulumi has capabilities such as secrets encryption to ensure sensitive data such as passwords are securely managed.