1. Scaling ML Inference with AWS AMP for Kubernetes-Based Applications


    To scale machine learning (ML) inference with AWS Prometheus Service (AMP) for Kubernetes-based applications, you would typically set up a managed Prometheus workspace, deploy your ML application to a Kubernetes cluster, and use AMP to collect and monitor your ML metrics for scaling purposes. Prometheus is an open-source monitoring system with a dimensional data model, flexible query language, and great visualization options through tools like Grafana.

    I'll walk you through a Pulumi program written in Python that sets up the following cloud resources:

    1. An AWS AMP workspace to collect and monitor ML inference metrics.
    2. An EKS cluster to host the Kubernetes-based ML applications.
    3. Necessary IAM roles and policies to grant the required permissions for the cluster nodes to interact with other AWS services.
    4. Kubernetes deployment and service definitions for your ML application (a placeholder in this example).
    5. Prometheus monitoring configurations that scrape metrics from your ML application.

    Assuming your ML application is containerized and ready for deployment, the following Pulumi program sets up the underlying infrastructure to support it:

    import pulumi import pulumi_aws as aws import pulumi_kubernetes as k8s from pulumi_aws import iam # Create an AWS AMP workspace for Prometheus monitoring amp_workspace = aws.amp.Workspace("ampWorkspace") # Create an EKS cluster to host the Kubernetes-based ML applications # First we create the necessary IAM roles and attach the EKS policies eks_role = iam.Role("eksRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": "eks.amazonaws.com" }, "Action": "sts:AssumeRole" }] }""") iam.RolePolicyAttachment("eksAmazonEKSClusterPolicyAttachment", policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy", role=eks_role) iam.RolePolicyAttachment("eksAmazonEKSServicePolicyAttachment", policy_arn="arn:aws:iam::aws:policy/AmazonEKSServicePolicy", role=eks_role) eks_cluster = aws.eks.Cluster("eksCluster", role_arn=eks_role.arn, # Additional EKS Cluster configurations, like VPC and Subnet IDs, should be specified here ) # Deploy your ML application to the EKS cluster # The following illustrates placeholder deployment and service definitions # You'll need to replace 'CONTAINER_IMAGE' with your actual ML application container image ml_app_labels = {"app": "ml-application"} ml_app_deployment = k8s.apps.v1.Deployment( "mlAppDeployment", metadata={"namespace": "default"}, spec={ "selector": {"matchLabels": ml_app_labels}, "replicas": 1, "template": { "metadata": {"labels": ml_app_labels}, "spec": {"containers": [{"name": "ml-container", "image": "CONTAINER_IMAGE"}]}, }, }, opts=pulumi.ResourceOptions(provider=eks_cluster.provider), ) ml_app_service = k8s.core.v1.Service( "mlAppService", metadata={"labels": ml_app_labels, "namespace": "default"}, spec={ "type": "LoadBalancer", "selector": ml_app_labels, "ports": [{"port": 80, "targetPort": 8080}], }, opts=pulumi.ResourceOptions(provider=eks_cluster.provider), ) # Output the AMP workspace ID and EKS cluster details pulumi.export("amp_workspace_id", amp_workspace.id) pulumi.export("eks_cluster_id", eks_cluster.id) pulumi.export("eks_cluster_name", eks_cluster.name) pulumi.export("ml_app_service_url", ml_app_service.status.apply(lambda status: status.load_balancer.ingress[0].hostname if status.load_balancer.ingress else None))

    In the above program, we start by creating an AWS AMP Workspace, which is where Prometheus will store the monitoring data. We also create all the necessary IAM roles and policies to allow Amazon EKS service to manage clusters on your behalf.

    Next, we create an EKS cluster that will run your Kubernetes-based ML applications. We attach the IAM role to the cluster allowing it to operate with the necessary permissions.

    After that, we set up a Kubernetes deployment for your ML application with a placeholder for the application's container image. The deployment specifies how your containers should run, which image to use, and the desired number of replicas. A corresponding Kubernetes Service is also created to expose your ML application, using a LoadBalancer to distribute traffic.

    The program ends by exporting the IDs and names of the created resources, together with the URL endpoint of the ML application service which can be used to access the ML application once it's deployed and running.

    To use this Pulumi program, you need to replace 'CONTAINER_IMAGE' with the appropriate container image for your ML application. Additionally, you would typically configure Prometheus to scrape metrics from your application's /metrics endpoint (or whichever endpoint exposes your application's metrics) and set up alerting rules that help you make scaling decisions.

    Remember that deploying actual ML models and inference applications may require additional configurations depending on their specific requirements, such as GPU-accelerated instances for inference, autoscaling policies based on custom metrics, and the use of Amazon SageMaker for model deployment and management.