1. Managed Kubernetes Clusters for AI Model Serving


    Managed Kubernetes clusters are ideal for deploying, scaling, and managing containerized applications such as AI model serving platforms. Kubernetes provides features like auto-scaling, self-healing, and load balancing that are beneficial for AI workloads which often require high availability and scalability.

    In this example, we will use Pulumi with the AWS provider to create a managed Kubernetes cluster using Amazon Elastic Kubernetes Service (EKS). AWS EKS is a managed service that makes it easier to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters.

    Here's what we are going to do:

    1. Create an EKS cluster: This acts as the control plane for the Kubernetes cluster.
    2. Define at least one node group: These are the worker machines that run your containers. Here, we'll define one node group for general processing and another optimized for compute-heavy tasks like AI model serving.
    3. Configure Kubernetes roles and permissions: These settings control access to the Kubernetes API and are important for a secure and well-managed cluster.
    4. Output the cluster's configuration: This includes the necessary details to connect to your Kubernetes cluster using kubectl.

    Now, let's write a Pulumi program in Python for setting up a managed Kubernetes cluster for AI model serving:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create a VPC configured for EKS cluster deployments. # This provides the networking infrastructure for your cluster. vpc = aws.ec2.Vpc("ai-vpc", cidr_block="", enable_dns_hostnames=True) # Create subnets for the EKS cluster. # Subnets are subsections within a VPC that can contain resources such as EC2 instances. public_subnet = aws.ec2.Subnet("ai-subnet", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a", map_public_ip_on_launch=True) # Define an EKS cluster. # The below defaults can be customized as needed. eks_cluster = eks.Cluster("ai-eks-cluster", role_arn=aws.iam.Role("ai-eks-role", assume_role_policy=aws.iam.get_policy_document( statements=[aws.iam.get_policy_document_statement( actions=["sts:AssumeRole"], principals=[aws.iam.get_policy_document_statement_principal("Service", "eks.amazonaws.com")], )] ).json)["arn"], vpc_config=eks.ClusterVpcConfigArgs( public_subnet_ids=[public_subnet.id] ) ) # Define the standard node group. standard_node_group = eks.NodeGroup("standard-node-group", cluster_name=eks_cluster.name, node_group_name="standard-ng", node_role_arn=aws.iam.Role("standard-ng-role", assume_role_policy=aws.iam.get_policy_document( statements=[aws.iam.get_policy_document_statement( actions=["sts:AssumeRole"], principals=[aws.iam.get_policy_document_statement_principal("Service", "ec2.amazonaws.com")], )] ).json)["arn"], subnet_ids=eks_cluster.core.subnet_ids, desired_capacity=2, min_size=1, max_size=3, instance_type="t3.medium") # Define an AI optimized node group for model-serving workloads. ai_node_group = eks.NodeGroup("ai-node-group", cluster_name=eks_cluster.name, node_group_name="ai-ng", node_role_arn=aws.iam.Role("ai-ng-role", assume_role_policy=aws.iam.get_policy_document( statements=[aws.iam.get_policy_document_statement( actions=["sts:AssumeRole"], principals=[aws.iam.get_policy_document_statement_principal("Service", "ec2.amazonaws.com")], )] ).json)["arn"], subnet_ids=eks_cluster.core.subnet_ids, scaling_config=eks.NodeGroupScalingConfigArgs( desired_size=2, min_size=1, max_size=5 ), instance_types=["p2.xlarge"]) # p2.xlarge instances are optimized for AI and machine learning workloads. # Exporting cluster details pulumi.export('kubeconfig', eks_cluster.kubeconfig) pulumi.export('cluster_name', eks_cluster.name) pulumi.export('cluster_endpoint', eks_cluster.endpoint) pulumi.export('nodegroup_name_standard', standard_node_group.node_group_name) pulumi.export('nodegroup_name_ai', ai_node_group.node_group_name)

    This program does the following:

    • Sets up a new VPC and subnet that our EKS cluster will use for networking.
    • Creates an EKS cluster with a specific IAM role attached to it for permissions (the aws.iam.Role resource).
    • Defines a node group for standard operations with a default instance type (t3.medium).
    • Defines another node group specifically optimized for AI workloads (p2.xlarge instances) which are ideal for compute-heavy tasks such as machine learning.
    • Exports important details about our EKS cluster such as kubeconfig, which we will use to connect to our Kubernetes cluster with kubectl or another compatible tool.

    Please ensure you have the AWS provider configured in your Pulumi setup before running this code. This can usually be done via environment variables or the AWS CLI configuration. Make sure the AWS account has the necessary permissions to create the resources specified in the program.