1. EKS to Orchestrate Machine Learning Model Serving


    To orchestrate machine learning model serving on AWS, you may want to leverage Amazon Elastic Kubernetes Service (EKS), which is a fully managed Kubernetes service. EKS provides you with a scalable and secure control plane to run your machine learning models packaged in containers. You can easily scale your applications, manage your resources efficiently, and integrate with other AWS services like Auto Scaling, CloudWatch, and IAM.

    Below is a Pulumi program that creates an EKS cluster, configures a node group to host the worker nodes, and applies the necessary roles and policies for the cluster to operate. The program is written in Python and uses pulumi_eks, an abstraction that simplifies EKS deployment.

    Detailed Explanation:

    1. EKS Cluster: We will create an EKS cluster which will form the backbone of our Kubernetes-based ML serving environment. eks.Cluster automatically handles the creation of the EKS control plane and worker nodes.

    2. Node Group: We will configure a node group with EC2 instances that will serve as worker nodes for our Kubernetes cluster. These workers will run the containerized ML models.

    3. IAM Role and Policy: Proper IAM roles and policies will be established to grant the EKS cluster permissions to AWS resources. These are necessary for the cluster to interact with services like EC2 and CloudWatch.

    4. EKS Role: We will create an EKS role using aws_iam.EKSRole which the EKS service will assume.

    5. Output: We will export the EKS cluster name and the kubeconfig needed to interact with the cluster, which you’ll use to deploy and manage your ML services.

    Here's the Pulumi program for the above architecture:

    import pulumi import pulumi_eks as eks import pulumi_aws as aws # Create an IAM role for EKS cluster authorization 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 IAM policies to the role eks_policy_arns = [ 'arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy', 'arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy', 'arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly' ] for policy in eks_policy_arns: aws.iam.RolePolicyAttachment(policy, role=eks_role.name, policy_arn=policy) # Create an EKS cluster eks_cluster = eks.Cluster('eksCluster', role_arn=eks_role.arn, version='1.21', instance_type='m5.large' ) # Export the cluster name pulumi.export('cluster_name', eks_cluster.name) # Export the kubeconfig pulumi.export('kubeconfig', eks_cluster.kubeconfig)

    In this program:

    • We created a new IAM role and attached existing AWS policies for EKS and EC2 permissions.
    • We instantiated an EKS cluster with a specific Kubernetes version and instance type for the worker nodes.
    • We exported the EKS cluster's name and kubeconfig to be used for CLI or programmatic access to the Kubernetes cluster.

    This setup is the starting point for serving machine learning models. In practice, you would package your ML model into a Docker container, push the container to a registry like Amazon ECR, and then define Kubernetes deployments and services to manage and expose your models, scaling as needed. You can integrate with AWS services for monitoring, logging, and security to enhance your model serving architecture.

    Remember to replace instance_type with the appropriate value tailored to your ML workloads requirements, and you might need additional IAM policies or roles based on the specific AWS services you plan to integrate with.