1. Kubernetes Managed Clusters for AI Workloads on EKS


    Creating a Kubernetes Managed Cluster specifically for AI workloads involves several key components. We'll focus on provisioning an Amazon EKS (Elastic Kubernetes Service) cluster, which is a managed Kubernetes service provided by AWS. Running AI workloads efficiently often requires the capability to scale resources and use specialized instances that can handle the heavy computation, such as those equipped with GPUs.

    To get started, we will set up the following resources:

    1. An EKS Cluster: The backbone of our Kubernetes managed environment. The resources attached to this cluster will determine the kind of workloads it can handle.
    2. An IAM Role for EKS: AWS services require that actions are performed by roles with the appropriate permissions. For EKS, we define a role that allows the service to manage resources on our behalf.
    3. Node Groups: These are the worker machines that your Kubernetes pods will run on. We will define Auto Scaling Groups that can scale up or down based on demand. For AI workloads, we might configure these groups to use instances with GPUs.
    4. VPC Configuration: Kubernetes clusters require a network, usually in the form of a VPC, to provide the necessary networking for resources and traffic routing.

    The example below describes each of these steps in Python using Pulumi's AWS library alongside its EKS library, which provides high-level components for working with EKS.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Step 1: Create an IAM role that EKS can assume to create AWS resources. eks_role = aws.iam.Role("eksRole", assume_role_policy={ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "eks.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }) # Attach the Amazon EKS service policy to the role. eks_policy_attachment = aws.iam.RolePolicyAttachment("eksPolicyAttachment", policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy", role=eks_role.name ) # Step 2: Define a VPC and subnet for your cluster to enable communication with the worker nodes. vpc = aws.ec2.Vpc("vpc", cidr_block="") subnets = aws.ec2.Subnet("subnets", vpc_id=vpc.id, cidr_block="", availability_zone=aws.get_availability_zones().names[0] ) # Step 3: Create an EKS cluster in the VPC. eks_cluster = eks.Cluster("eksCluster", role_arn=eks_role.arn, vpc_id=vpc.id, subnet_ids=[subnets.id] ) # Step 4: Define a node group for your cluster. For AI workloads, choose an appropriate instance type. node_group = eks.NodeGroup("nodeGroup", cluster_name=eks_cluster.name, node_role_arn=eks_role.arn, subnet_ids=[subnets.id], instance_type="p3.2xlarge", # Example of an instance type with GPU capability. desired_capacity=1, min_size=1, max_size=2, # You can set this higher to allow your cluster to scale based on demand. labels={"workload-type": "ai"}, ) # Export the cluster's kubeconfig and the NodeGroup's ID pulumi.export('kubeconfig', eks_cluster.kubeconfig) pulumi.export('nodeGroup', node_group.id)

    This is a basic configuration and should be further customized for production workloads, particularly for AI which can require more specific configurations around GPU usage, memory and CPU needs, etc. Additionally, when dealing with AI workloads, you may want to consider attaching persistent storage to your pods and involving Amazon SageMaker or other AI/ML services.

    The current Pulumi program creates and configures an EKS cluster with a single node group optimized for AI tasks by utilizing an instance type with GPU support. The number of nodes and their types can be adjusted based on specific needs. The kubeconfig output will be used to interact with your Kubernetes cluster using kubectl, and other Kubernetes ecosystem tools.