1. Fault-tolerant AI Workloads on Amazon EKS Using Kafka


    To deploy fault-tolerant AI workloads on Amazon EKS using Kafka, you'll need to set up several AWS resources to support this architecture. This setup includes creating an EKS cluster, deploying a managed Kafka cluster, and ensuring that your AI applications are fault-tolerant and able to handle interruptions and failures.

    For the EKS cluster, you'll use the eks.Cluster resource which provisions and manages an EKS cluster in AWS. This cluster will host your AI workloads.

    For the Kafka cluster, you will use Amazon Managed Streaming for Kafka (MSK), which provides a fully managed Apache Kafka service. The aws.msk.Cluster resource allows you to create and manage an MSK Cluster.

    Here is a Pulumi program in Python that demonstrates how to create these resources. This program will not cover the configuration of the AI workload itself or the specific Kafka topics and producer/consumer configurations since those details depend on your particular use case.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster. # The eks.Cluster class abstracts away details of managing an EKS cluster. ai_eks_cluster = eks.Cluster('ai-workloads-cluster', # Define the desired number of cluster nodes and instance type. desired_capacity=2, min_size=1, max_size=3, instance_type='m5.large', # Create a simple managed node group for AI workloads. create_oidc_provider=True, ) # Create an Amazon Managed Streaming for Kafka (MSK) cluster. # Here, we'll create a Kafka cluster with 2 borker nodes across 3 availability zones for high availability. ai_kafka_cluster = aws.msk.Cluster('ai-kafka-cluster', cluster_name='ai-kafka-cluster', kafka_version='2.6.1', number_of_broker_nodes=2, broker_node_group_info=aws.msk.ClusterBrokerNodeGroupInfoArgs( instance_type='kafka.m5.large', client_subnets=ai_eks_cluster.vpc.subnets.apply(lambda subnets: [s.id for s in subnets]), security_groups=[], # Replace with Kafka Security groups if you have any ), configuration_info=aws.msk.ClusterConfigurationInfoArgs( arn='arn:aws:kafka:us-west-2:123456789012:configuration/example-configuration-name', revision=1, ), ) # Export the cluster endpoint and other outputs to access the clusters later. pulumi.export('eks_cluster_name', ai_eks_cluster.eks_cluster.name) pulumi.export('eks_cluster_endpoint', ai_eks_cluster.eks_cluster.endpoint) pulumi.export('kafka_cluster_name', ai_kafka_cluster.cluster_name) pulumi.export('kafka_cluster_endpoint', ai_kafka_cluster.bootstrap_brokers)

    In this program:

    • We create an EKS cluster to run AI workloads using pulumi_eks.Cluster.
    • We specify the number of nodes, instance type, and the network configuration.
    • We also create an OIDC provider for the cluster to enable integration with other AWS services.
    • Then, we create an MSK Kafka cluster using aws.msk.Cluster.
    • For the MSK cluster, we define the cluster name, the Kafka version, and the number of broker nodes which are spread across availability zones for fault tolerance.
    • We also define the broker node group information including instance type and networking details.
    • We point to a previously created Kafka configuration that needs to be in place.

    You will need to ensure that you have properly configured your AWS credentials and Pulumi environment before deploying these resources. Adjust the instance_type, desired_capacity, min_size, max_size, and other parameters to fit your workload requirements.

    This program assumes that proper VPC and subnet resources are already in place or created by the eks.Cluster class. Adjust security groups and other network configurations as needed for your specific environment.

    Remember to replace the Kafka configuration ARN with your specific configuration if you have custom Kafka setups. You may also need to adjust the version of Kafka and the instance types based on the latest support and your requirements.

    Additionally, you will need to implement the deployment of your AI applications to the EKS cluster and configure your Kafka clients within those applications, which is beyond the scope of this initial infrastructure setup.