1. AI Model Serving with Istio Service Mesh


    To serve an AI model with an Istio service mesh setup, you'll need to have a Kubernetes cluster with Istio installed. You can then deploy your machine learning model as a service within the Istio service mesh, which will allow you to take advantage of Istio's features such as traffic routing, monitoring, and security.

    The following Pulumi program will provide you with an example of how you can deploy a machine learning model in a service on a Kubernetes cluster with Istio. For this example, we will:

    1. Set up a Kubernetes cluster (using Google Kubernetes Engine as an example).
    2. Install Istio onto the cluster.
    3. Deploy a sample machine learning model as a Kubernetes Service.

    To accomplish this, we will use the pulumi_gcp package for creating the GKE cluster and pulumi_kubernetes to interact with Kubernetes resources, including installing Istio and deploying our model service.

    Here's how you can achieve this:

    import pulumi import pulumi_gcp as gcp import pulumi_kubernetes as kubernetes from pulumi_kubernetes.helm.v3 import Chart, ChartOpts, FetchOpts # Step 1: Provision a GKE cluster cluster = gcp.container.Cluster("ai-model-serving-cluster", initial_node_count=3, node_version="latest", min_master_version="latest", node_config={ "oauth_scopes": [ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], }) # Export the Cluster name pulumi.export('cluster_name', cluster.name) # Obtain the Kubeconfig kubeconfig = pulumi.Output.all(cluster.name, cluster.endpoint, cluster.master_auth).apply(lambda args: """ apiVersion: v1 clusters: - cluster: certificate-authority-data: {2} server: https://{1} name: {0} contexts: - context: cluster: {0} user: {0} name: {0} current-context: {0} kind: Config preferences: {{}} users: - name: {0} user: auth-provider: config: cmd-args: config config-helper --format=json cmd-path: gcloud expiry-key: '{{.credential.token_expiry}}' token-key: '{{.credential.access_token}}' """.format(args[0], args[1], args[2]['cluster_ca_certificate'])) # Step 2: Install Istio on the cluster using the Helm Chart istio_namespace = kubernetes.core.v1.Namespace("istio-system") istio = Chart( "istio", ChartOpts( chart="istio", version="1.5.1", fetch_opts=FetchOpts( repo="https://istio-release.storage.googleapis.com/charts", ), namespace=istio_namespace.metadata["name"], ) ) # Step 3: Deploy your machine learning model as a service app_labels = {"app": "ai-model"} ai_model_deployment = kubernetes.apps.v1.Deployment( "ai-model-deployment", metadata={ "namespace": istio_namespace.metadata["name"], }, spec={ "selector": {"matchLabels": app_labels}, "replicas": 1, "template": { "metadata": {"labels": app_labels}, "spec": { "containers": [{ "name": "model", "image": "YOUR_MODEL_IMAGE_HERE", # Replace with your AI model container image "ports": [{"containerPort": 8080}], # Adjust the port to where your model serves HTTP requests }], }, }, } ) ai_model_service = kubernetes.core.v1.Service( "ai-model-service", metadata={ "namespace": istio_namespace.metadata["name"], "labels