1. Kubernetes-Powered High-Performance Computing for AI on AWS Upbound


    To set up a Kubernetes-powered High-Performance Computing (HPC) environment for AI on AWS using Pulumi, you need to create an Amazon Elastic Kubernetes Service (EKS) cluster. This will be your Kubernetes control plane and will orchestrate your containerized applications, including those running high-performance computing workloads for AI.

    Here is how the setup will be broken down:

    • First, we will create an EKS Cluster.
    • We will attach node groups to the cluster which will provide the compute capacity for your applications.
    • We might also attach storage through AWS's Elastic Block Storage (EBS) or Elastic File System (EFS), which would be suitable for AI datasets or models that need to be accessed by your applications.
    • Additionally, if your AI applications need to interact with other AWS services, you can set up those integrations as part of your code.

    Below is a Pulumi program written in Python which demonstrates how to create an EKS cluster and two node groups (“gpu” for AI workloads requiring GPU resources, and “cpu” for general compute tasks) with Pulumi.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster. cluster = eks.Cluster("ai-cluster", instance_type="m5.large", # This should be selected based on your specific CPU needs desired_capacity=2, min_size=1, max_size=3, storage_classes="gp2", # Standard storage class deploy_dashboard=False, # Dashboard is not typically recommended for security reasons ) # Create a node group for GPU-powered instances for AI workloads. gpu_node_group = eks.NodeGroup("ai-gpu-nodegroup", cluster=cluster.core, # Attaching the node group to our cluster instance_type="p3.2xlarge", # GPU-based instances for AI computations desired_capacity=1, min_size=1, max_size=2, labels={"workload-type": "gpu"}, ) # Create a node group for CPU-powered instances for general tasks. cpu_node_group = eks.NodeGroup("ai-cpu-nodegroup", cluster=cluster.core, # Attaching the node group to our cluster instance_type="m5.large", desired_capacity=2, min_size=1, max_size=4, labels={"workload-type": "general"}, ) # Export the cluster's kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig)

    This program performs the following actions:

    • Initializes a new EKS cluster named ai-cluster.
    • Disables the deployment of the Kubernetes Dashboard as it's considered a security risk.
    • Initializes a GPU-based node group ai-gpu-nodegroup that your AI applications can be scheduled on. This uses instances like p3.2xlarge which are equipped with GPUs.
    • Initializes a CPU-based node group ai-cpu-nodegroup intended for general tasks that don't require GPUs.

    The desired_capacity, min_size, and max_size parameters dictate the scaling properties of your node groups:

    • desired_capacity is where the Auto Scaling Group should start regarding the number of instances.
    • min_size is the minimum number of instances to which the group can scale down.
    • max_size is the maximum number of instances to which the group can scale up.

    Labels are applied to the node groups to help with scheduling decisions based on the type of workload; workloads requiring GPU can target nodes with the label workload-type: gpu.

    The output is the kubeconfig for the cluster, which you can use with kubectl or other Kubernetes tools to interact with your cluster.

    This program sets the groundwork for HPC in AI on AWS using Kubernetes. Further configurations may include setting up security groups, IAM roles, and policies for fine-grained access control, and integrating with other AWS services like S3 for data storage, and possibly Amazon SageMaker for building, training, and deploying machine learning models.