1. High-Performance Multi-Node Training with EKS and VPC CNI


    In this Pulumi Python program, we will be creating an Amazon EKS (Elastic Kubernetes Service) cluster to perform high-performance, multi-node training with a focus on the networking capabilities provided by the AWS VPC CNI (Container Network Interface) plugin. The VPC CNI plugin enables Kubernetes pods to have the same IP addressing and networking as other EC2 instances, allowing for more efficient network routing and high-performance data transfer between nodes.

    We will use the following key Pulumi resources in our program:

    • eks.Cluster: This creates an EKS Kubernetes cluster in AWS. It comes with all the necessary elements like the control plane, security groups, and IAM roles.
    • eks.VpcCni: This configures the VPC CNI plugin for the EKS cluster to optimize pod networking for performance.
    • aws.ecr.Repository: We'll create an Elastic Container Registry (ECR) repository to store Docker images that the Kubernetes pods will use for their training tasks.

    Here's an outline of the steps our Pulumi program will perform:

    1. Create an ECR repository to hold the container images for our training jobs.
    2. Provision an EKS cluster configured with the desired node size and count suitable for high-performance computing tasks.
    3. Configure the EKS cluster with VPC CNI plugin options optimized for network performance, including ENI (Elastic Network Interface) settings.

    Let's proceed with the program:

    import pulumi import pulumi_eks as eks import pulumi_aws as aws # Create an ECR repository to store our training job Docker images ecr_repo = aws.ecr.Repository("multiNodeTrainingRepo") # Create an IAM role for the EKS cluster with the necessary permissions eks_role = aws.iam.Role("eksRole", assume_role_policy=aws.iam.get_policy_document( statements=[aws.iam.GetPolicyDocumentStatementArgs( principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs( type="Service", identifiers=["eks.amazonaws.com"] )], actions=["sts:AssumeRole"] )] ).json) # Attach the necessary policies to the IAM role for EKS policy_attachments = [ aws.iam.RolePolicyAttachment("eksPolicy", policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy", role=eks_role), aws.iam.RolePolicyAttachment("eksVpcPolicy", policy_arn="arn:aws:iam::aws:policy/AmazonEKSVPCResourceController", role=eks_role) ] # Creating the EKS Cluster cluster = eks.Cluster("multiNodeTrainingCluster", role_arn=eks_role.arn, vpc_id="vpc-0abcd1234ef56789", # Assuming a pre-existing VPC ID subnet_ids=["subnet-0abc123d4ef568aa", "subnet-0abc123d4ef568bb"], # Assuming pre-existing Subnet IDs instance_type="m5.large", # Choose an appropriate instance type for training desired_capacity=2, min_size=1, max_size=4, deploy_dashboard=False, # Configure VPC CNI Options for optimizing IP availability and networking vpc_cni_options=eks.VpcCniOptionsArgs( eni_mtu=9001, warm_ip_target=10 # Adjust warm IP target as necessary for your use case ) ) # Export relevant data pulumi.export("cluster_name", cluster.name) pulumi.export("kubeconfig", cluster.kubeconfig) # Required to interact with the Kubernetes cluster pulumi.export("ecr_repo_url", ecr_repo.repository_url) # The URL to push Docker images to

    In this program:

    • We created an ECR repository for storing the Docker images that will be used in the Kubernetes pods for training.
    • An EKS cluster is provisioned with a defined IAM role and attached policies that grant the cluster permissions to operate within AWS.
    • The EKS cluster is configured with the VPC CNI plugin to optimize network performance, which is crucial for high-performance multi-node training.
    • We chose m5.large instance types for the worker nodes and set the desired, minimum, and maximum size of nodes in the node group. This can be adjusted according to the computational requirements of your training jobs.
    • We exported the cluster name, kubeconfig, and ECR repository URL which will be used to administer the Kubernetes cluster and to push/pull Docker images.

    Please ensure that your AWS credentials are configured and that the Pulumi CLI is installed and set up correctly. To launch this infrastructure, save the code to a file with a .py extension and run pulumi up via the command line in the same directory where your file resides. The Pulumi CLI will take care of interpreting and executing your infrastructure code.