1. Monitoring AI Model Serving on GKE with Prometheus


    Monitoring AI model serving on Google Kubernetes Engine (GKE) with Prometheus involves setting up a GKE cluster, deploying your AI model as a service within the cluster, and then instrumenting Prometheus to collect and analyze the metrics exposed by your model's service.

    Below are steps to create a basic setup:

    1. Provision a GKE Cluster: We'll start by provisioning a GKE cluster using Pulumi's GCP provider. The cluster will be the environment where your AI model is served.

    2. Deploy Your AI Model: After creating the cluster, you would typically package your AI model into a Docker image and deploy it to the cluster as a Kubernetes deployment. You will need to configure Kubernetes services, deployments, and other necessary resources to serve your model.

    3. Set Up Prometheus: For monitoring, we will deploy Prometheus within the GKE cluster. Prometheus can be set up using the stable/prometheus Helm chart, which simplifies the configuration and deployment of Prometheus.

    4. Configure Exporters: To enable Prometheus to monitor your AI model, you need to expose metrics from your service in a format that Prometheus can scrape. The Prometheus exporter will expose an HTTP endpoint with metrics in the correct format.

    5. Set Up AlertManager: Optionally, you could also set up AlertManager to handle alerts based on the metrics collected by Prometheus.

    Below is a basic Pulumi program in Python that sets up a GKE cluster and assumes the model serving and Prometheus setup are done separately:

    import pulumi import pulumi_kubernetes as k8s import pulumi_gcp as gcp # Create a GKE cluster cluster = gcp.container.Cluster("ai-model-cluster", initial_node_count=3, node_config={ "machine_type": "n1-standard-1", "oauth_scopes": [ "https://www.googleapis.com/auth/cloud-platform", ], }) # Export the Cluster name pulumi.export('cluster_name', cluster.name) # Obtain the Kubeconfig after the cluster is created kubeconfig = pulumi.Output.all(cluster.name, cluster.endpoint, cluster.master_auth).apply( lambda args: """apiVersion: v1 clusters: - cluster: certificate-authority-data: {1} server: https://{0} name: {2} contexts: - context: cluster: {2} user: {2} name: {2} current-context: {2} kind: Config preferences: {{}} users: - name: {2} user: client-certificate-data: {1} client-key-data: {1} token: {1} """.format(args[1], args[2]['clusterCaCertificate'], args[0])) # Export the Kubeconfig to be used by kubectl pulumi.export('kubeconfig', kubeconfig)

    This program creates a GKE cluster with a default node pool of 3 nodes using n1-standard-1 machine type. After the cluster is provisioned, it exports the necessary information to connect to the cluster using kubectl.

    Please note that this is just a foundation. You need to add additional Pulumi code or configurations to deploy your AI model and set up Prometheus monitoring within the GKE cluster. After deploying your model, you will need to annotate your Kubernetes service with the appropriate Prometheus annotations for discovery, as Prometheus is designed to discover services automatically based on annotations.

    In practice, the model serving and Prometheus setup would require more detailed configurations, including creating a Docker image for your AI model, writing Kubernetes deployment and service YAML definitions, and setting up Prometheus monitoring with alerting rules. Each of these tasks can be automated and managed using Pulumi.

    For more information on using Pulumi with GCP and Kubernetes, check out the Pulumi documentation which provides comprehensive guides and API references.