1. Kubernetes-based Model Training with GPU Support on EKS


    When setting up a Kubernetes-based model training environment with GPU support on Amazon EKS (Elastic Kubernetes Service), you'll need to create an EKS cluster and configure it to support GPU workloads. This typically involves:

    1. Creating an EKS cluster with nodes that have GPU capabilities.
    2. Installing the necessary Kubernetes device plugins for GPUs.
    3. Scheduling your machine learning jobs on the GPU-enabled nodes.

    Below is a Pulumi program written in Python that sets up a Kubernetes cluster on EKS with GPU support. I'll explain each part of the code to help you understand how it works.

    Importing Required Modules

    We'll start by importing the necessary Pulumi modules for AWS and EKS. The pulumi_eks module provides higher-level abstractions specifically for managing EKS clusters.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks

    Creating an IAM Role for EKS

    Amazon EKS requires an IAM role to create and manage resources on your behalf. This role will be used by the EKS cluster's control plane.

    # Create an IAM role for EKS eks_role = aws.iam.Role('eksIamRole', assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "eks.amazonaws.com"}, }] } ) # Attach the necessary IAM policies to the role aws.iam.RolePolicyAttachment('eksPolicy', policy_arn='arn:aws:iam::aws:policy/AmazonEKSClusterPolicy', role=eks_role.name, ) aws.iam.RolePolicyAttachment('eksVpcPolicy', policy_arn='arn:aws:iam::aws:policy/AmazonEKSVPCResourceController', role=eks_role.name, )

    Setting Up an EKS Cluster

    Next, we will create a new EKS cluster with a node group that has GPU support. For this example, we'll use instance types that are optimized for GPU workloads, like the p2.xlarge, which includes a single K80 GPU.

    # Create an EKS cluster with a node group that includes GPU support my_eks_cluster = eks.Cluster('myEksCluster', role_arn=eks_role.arn, instance_type="p2.xlarge", # Select a GPU instance type version='1.18' )

    In real-world scenarios, you may want to specify additional configuration for the VPC, subnets, and EKS addons. Be sure to replace the instance type with the proper GPU-enabled instance that fits your workload needs.

    Enabling GPU Support on the EKS Cluster

    To leverage GPUs on Kubernetes, you need to deploy the NVIDIA device plugin. This plugin is responsible for advertising the presence of the GPU to the Kubernetes scheduler and for mounting the device drivers into the pods. While the plugin is not managed directly by Pulumi, you would typically use a Kubernetes resource to apply the appropriate YAML file or Helm chart.

    # Kubernetes YAML manifest for the NVIDIA device plugin nvidia_device_plugin_yaml = """ apiVersion: v1 kind: ServiceAccount metadata: name: nvidia-device-plugin namespace: kube-system --- apiVersion: apps/v1 kind: DaemonSet metadata: name: nvidia-device-plugin-daemonset namespace: kube-system spec: selector: matchLabels: name: nvidia-device-plugin-ds template: metadata: labels: name: nvidia-device-plugin-ds spec: tolerations: - key: nvidia.com/gpu operator: Exists effect: NoSchedule serviceAccountName: nvidia-device-plugin containers: - name: nvidia-device-plugin-ctr image: "nvidia/k8s-device-plugin:1.0.0-beta" securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] volumeMounts: - name: device-plugin mountPath: /var/lib/kubelet/device-plugins volumes: - name: device-plugin hostPath: path: /var/lib/kubelet/device-plugins """ # Apply the NVIDIA device plugin manifest to the EKS cluster nvidia_device_plugin = eks.Cluster('nvidiaDevicePlugin', adummy_resource=my_eks_cluster.eks_cluster.name, yaml=nvidia_device_plugin_yaml )

    Deploying a GPU-Enabled Application

    Once your GPU-enabled EKS cluster is set up, you can deploy machine learning or other GPU-dependent workloads. This part isn't shown here, as it's application-specific, but you would generally use a Kubernetes manifest to define your job and ensure it requests the GPU resource.


    Finally, we'll export the EKS cluster name and the Kubeconfig needed to interact with the cluster, which will be useful for deploying and managing workloads.

    # Exporting the cluster name and kubeconfig pulumi.export('cluster_name', my_eks_cluster.name) pulumi.export('kubeconfig', my_eks_cluster.kubeconfig)

    This program sets up an EKS cluster with basic GPU support. To run your specific model training workloads, you may need to make further adjustments, like setting up storage classes, tuning resource allocations, and perhaps using a Helm chart for more complex deployments.

    Remember to review Amazon's pricing and limitations for GPU instances, and ensure your AWS account has the necessary quotas to create the GPU-enabled EC2 instances required for your workload.