1. Orchestrating Machine Learning Pipelines with AWS EKS


    Orchestrating machine learning pipelines on AWS EKS involves setting up the necessary infrastructure to run and manage ML workflows. Amazon Elastic Kubernetes Service (EKS) is a managed Kubernetes service that simplifies the process of running containerized applications on AWS.

    Here's a high-level rundown of what you might want in your infrastructure to orchestrate ML pipelines:

    1. EKS Cluster: The central component where your Kubernetes pods will run. This managed Kubernetes service will handle the orchestration of your containers.
    2. ECR (Elastic Container Registry) Repository: You will need a place to store your Docker images that contain your ML code. ECR is a Docker container registry that allows you to store, manage, and deploy your container images.
    3. IAM Role: An AWS Identity and Access Management (IAM) role that allows EKS to make calls to other AWS services on your behalf.
    4. Other possible requirements: Depending on your needs, you might also require additional components like storage solutions, logging, monitoring, or specific node configurations.

    Now let's write a Pulumi program in Python that establishes an EKS cluster and an ECR repository, assuming you already have necessary IAM roles configured:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an ECR repository to hold your ML Docker images. ecr_repo = aws.ecr.Repository("ml_ecr_repo", image_tag_mutability="MUTABLE" ) # IAM Role that EKS will assume to create AWS resources for Kubernetes. eks_role = aws.iam.Role("eks_role", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "eks.amazonaws.com" }, "Action": "sts:AssumeRole" } ] })) # Attaching necessary policies to the EKS IAM Role. policy_attachment = aws.iam.RolePolicyAttachment("eks_policy_attachment", role=eks_role.name, policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy" ) # EKS Cluster that will run your ML workloads. cluster = eks.Cluster("ml_eks_cluster", role_arn=eks_role.arn, tags={ "Name": "ml_eks_cluster" } ) # Export the cluster's kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig)

    This program is designed to do the following:

    • It sets up an AWS ECR repository where you can push ML container images.
    • It creates an IAM role that AWS EKS will assume for creating resources like EC2 instances for your Kubernetes pods.
    • It attaches the AmazonEKSClusterPolicy policy to the IAM role, which grants your EKS cluster permissions to make calls to other AWS services.
    • It initializes an EKS cluster, which will automatically be associated with the IAM role and have the specified tags.
    • It exports the kubeconfig of your EKS cluster so that you could interact with your cluster using kubectl or other Kubernetes tools.

    Once your EKS cluster is up and running, you will use the kubeconfig output to manage your Kubernetes resources. You can start orchestrating your ML pipelines by deploying Kubernetes pods that contain your ML workloads. Your ML Docker images will reside in the ECR repository we created.

    This Pulumi program assumes you're familiar with the Docker commands to build and push your ML container image to the ECR repository. Once your images are in ECR, you can create Kubernetes deployment configurations that reference those images to run your ML workloads on the EKS cluster.

    Remember to replace the "AmazonEKSClusterPolicy" in the code with any additional or custom policies you require for your workload. The use of pulumi.Input classes and pulumi.Output methods provided by the Pulumi SDK allows for the use of strongly-typed inputs and dynamic output references.