1. Running Distributed Machine Learning Jobs on EKS


    To run distributed machine learning jobs on Amazon EKS (Elastic Kubernetes Service), you will need to set up an EKS cluster and configure your environment to handle the specific requirements of your machine learning workload. When running ML workloads, you often need to ensure that your cluster has enough compute power and is capable of scaling based on the job demands. You may also want to integrate with services like ECR (Elastic Container Registry) to manage your Docker images and S3 for storing your datasets and models.

    In this guide, I will walk you through creating an EKS cluster with an associated node group that can scale as needed. I'll also show you how to create an ECR repository for storing container images which you might use for your ML jobs. Note that this is a starting point – depending on your ML job's complexity, you might need additional configuration or services.

    Here's a step-by-step Pulumi program in Python that sets up the infrastructure:

    1. EKS Cluster: We will create an EKS cluster using the pulumi_eks package. This cluster acts as the control plane for your Kubernetes workloads.

    2. Node Group: To run your distributed jobs, you'll need compute resources. We'll create a managed node group within the EKS cluster with the ability to scale as needed.

    3. ECR Repository: We'll use the pulumi_aws package to create an ECR repository. This is where you'll store your Docker images for ML workloads.

    4. S3 Bucket: I'll include a simple definition for creating an S3 bucket to illustrate how you might handle data storage. You would store datasets, models, and other artifacts here.

    Now, let's put it all together in a Pulumi program:

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster. cluster = eks.Cluster("ml-cluster") # Create a managed node group within the EKS cluster. node_group = eks.NodeGroup( "ml-node-group", cluster=cluster.core, # Reference to the created cluster desired_capacity=2, # Initial number of nodes min_size=1, # Minimum number of nodes max_size=4, # Maximum number of nodes for auto-scaling instance_type="m5.large", # Instance type for ML tasks, choose based on your workload labels={"workload": "machine-learning"} # Helpful labels for organizing resources ) # Create an ECR repository. ecr_repository = aws.ecr.Repository("ml-container-repo") # Example S3 bucket for ML data. ml_data_bucket = aws.s3.Bucket("ml-data-bucket") # Export the cluster kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig) # Export the ECR repository URL. pulumi.export("ecr_repository_url", ecr_repository.repository_url) # Export the S3 bucket name. pulumi.export("ml_data_bucket_name", ml_data_bucket.bucket)

    This Pulumi program accomplishes the following:

    • It creates a new EKS cluster which will act as the foundation for running Kubernetes-based ML workloads.
    • It adds a scalable node group with an initial desired size that can adjust to your workload demands.
    • It sets up an ECR repository to store and manage your ML Docker images.
    • It establishes an S3 bucket for handling datasets and ML artifacts.

    To run this code, save it to a file (eg. main.py), make sure you've installed Pulumi and configured AWS credentials, and then run the following commands:

    pulumi stack init dev pulumi up

    The pulumi up command will provision the resources defined in the script on AWS and output the necessary access configurations such as the kubeconfig for the EKS cluster.

    From here, you would typically build your machine learning container images, push them to ECR, and then deploy your ML workloads to the EKS cluster. These workloads might be in the form of Kubernetes jobs, deployments, or other resource types that can carry out distributed ML tasks. Remember to replace "m5.large" with the instance type that suits your machine learning workload the best.