1. Multi-AZ EKS NodeGroups for Fault-Tolerant AI Applications


    Creating a highly available multi-Availability Zone (AZ) EKS cluster with NodeGroups is crucial for running fault-tolerant AI applications. Amazon EKS (Elastic Kubernetes Service) is a managed Kubernetes service that makes it easy to run Kubernetes on AWS without needing to install and operate your own Kubernetes control plane.

    In order to ensure high availability and fault tolerance, we'll create EKS NodeGroups across multiple AZs in a specified region. Each NodeGroup will be launched in different AZs to ensure that, if one AZ goes down, your AI application can continue running in another AZ.

    Below is a Pulumi program in Python that outlines the steps to create an EKS cluster with multi-AZ NodeGroups suitable for running AI applications. This program requires the pulumi_eks module, which is a high-level Pulumi package for deploying and managing AWS EKS clusters.

    Pulumi's infrastructure-as-code framework allows you to define your infrastructure and its configuration directly in code. This program is written in Python and uses Pulumi's SDK to communicate with AWS services.

    In this program, we'll take these steps:

    1. Import necessary libraries from Pulumi
    2. Create an EKS cluster
    3. Define NodeGroups for the EKS cluster across multiple AZs
    4. Configure the NodeGroups with the necessary compute and storage resources for your AI applications
    import pulumi import pulumi_eks as eks # Specify the desired AWS region for our EKS cluster. aws_region = 'us-west-2' # Create an EKS cluster. cluster = eks.Cluster('my-eks-cluster', provider_credential_opts=eks.KubeconfigOptionsArgs( profile_name='aws-profile'), # Specify your AWS CLI profile here version='1.21', # Specify your desired Kubernetes version instance_role=role) # A pre-created IAM role for EKS Nodes # Define node groups in multiple AZs for fault tolerance. # You can repeat this block for creating multiple node groups in different AZs. nodegroup1 = eks.ManagedNodeGroup('nodegroup-1', cluster=cluster.core, # Reference to our EKS cluster node_role=role, # Node IAM role from above subnet_ids=subnet_ids, # List of subnet IDs across different AZs instance_types=['t3.medium'], # Instance type suitable for AI applications scaling_config=eks.ManagedNodeGroupScalingConfigArgs( min_size=1, max_size=5, desired_size=3)) nodegroup2 = eks.ManagedNodeGroup('nodegroup-2', cluster=cluster.core, node_role=role, subnet_ids=subnet_ids, # Ensure these subnets are in different AZs instance_types=['t3.medium'], scaling_config=eks.ManagedNodeGroupScalingConfigArgs( min_size=1, max_size=5, desired_size=3)) # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig) # Remember to populate variables like 'role', 'subnet_ids' accordingly. # 'role' is an IAM role ARN assigned for EKS nodes, and 'subnet_ids' is a list of subnet IDs across different AZs.

    This creates two node groups, each of which can autoscale between 1 and 5 instances, with a desired state of 3 instances to start with.

    These node groups are configured with t3.medium instance types, which are ideal for general applications. If you're running computation-intensive AI applications, you may want to choose instance types that are more powerful, like those belonging to the p3 or g4 instance families, which have GPUs suitable for machine learning.

    You should replace the subnet_ids list with subnet IDs from your VPC, ensuring that the subnets are spread across different availability zones for high availability. The role should be an IAM role ARN with the necessary permissions for EKS nodes.

    As for the Kubernetes version, ensure that you use a version supported by AWS EKS. You'd need to update the 'version' field with the version you need.

    Once deployed, you'll also have a kubeconfig output which you can use to interact with your EKS cluster via kubectl.

    The structure of this program reflects a typical Pulumi deployment script. You define your resources and their interrelationships, and Pulumi understands how to create, update, and delete these resources in the correct order with the appropriate properties.

    This setup ensures that your AI application has high availability across different AZs and can withstand potential failures, minimizing downtime and maintaining consistent performance.