1. EKS for Distributed Training of Machine Learning Models


    To set up an Amazon Elastic Kubernetes Service (EKS) infrastructure suitable for distributed training of machine learning models, you would typically go through the following steps:

    1. Create an EKS Cluster: This will be the central management entity for your Kubernetes nodes and workloads.
    2. Configure Node Groups or Managed Node Groups: These are groups of worker nodes where your containers (and thus, your machine learning workloads) will run. In a distributed training context, you might have multiple nodes with GPUs attached, for instance.
    3. Define IAM Roles: IAM roles with the necessary permissions must be attached to your EKS cluster and worker nodes.
    4. Set up Networking: Kubernetes networking must be configured for pods to communicate with each other across nodes.
    5. Deploy Distributed Training Workloads: This step involves packaging your machine learning code into containers and creating Kubernetes deployments to run these containers across your nodes.

    Below is a Pulumi program written in Python detailing the creation of such an EKS cluster. I'll explain each component and its relevance to the distributed training of machine learning models.

    import pulumi import pulumi_eks as eks # IAM roles are required for EKS and NodeGroups to interact with other AWS services. # This example assumes that we already have an IAM role for EKS and NodeGroups with the necessary AWS service policies attached. # For the purposes of this example, we are using placeholder ARNs for the roles. eks_role_arn = "arn:aws:iam::123456789012:role/EksClusterRole" nodegroup_role_arn = "arn:aws:iam::123456789012:role/EksNodeGroupRole" # We create an EKS cluster named 'machine-learning-cluster'. # The version of Kubernetes is specified to ensure compatibility with machine learning frameworks like TensorFlow or PyTorch. cluster = eks.Cluster('machine-learning-cluster', role_arn=eks_role_arn, version='1.21') # NodeGroups are sets of worker nodes. We set the instance type to a GPU instance for machine learning workloads. # The desired capacity, min size, and max size are configured based on the needs of your specific workloads. gpu_managed_node_group = eks.ManagedNodeGroup('gpu-managed-node-group', cluster=cluster.core, instance_type='p3.8xlarge', node_role_arn=nodegroup_role_arn, desired_capacity=2, min_size=1, max_size=4, labels={'workload-type': 'ml-gpu'}, tags={'Name': 'machine-learning-node-group'}) # Export the cluster's kubeconfig and the name of the NodeGroup. pulumi.export('kubeconfig', cluster.kubeconfig) pulumi.export('node_group_name', gpu_managed_node_group.node_group_name)


    To deploy this infrastructure using Pulumi:

    1. Configure AWS Credentials: Ensure your AWS credentials are configured for Pulumi, this can be done through the AWS CLI or environment variables.
    2. Initialize a Pulumi Project: Start by creating a new Pulumi project using pulumi new aws-python.
    3. Add the Above Code: Replace the contents of __main__.py with the code above.
    4. Deploy With Pulumi: Run pulumi up to preview and deploy the resources. The output will display the changes to be made and prompt you to proceed with the deployment.


    • EKS Cluster: The eks.Cluster resource initializes the control plane of the Kubernetes cluster. We specify a Kubernetes version and attach an IAM role that grants the EKS service the necessary AWS permissions.

    • Managed Node Group: Managed node groups make it easy to manage EC2 instances that the Kubernetes control plane uses as worker nodes. We've chosen GPU instances (p3.8xlarge) which are well-suited for machine learning tasks. The node group is tagged and labelled; these can be useful for cost allocation and deployment strategies.

    • IAM Role ARNs: The eks_role_arn and nodegroup_role_arn (ARNs for AWS Identity and Access Management roles) are to be replaced with actual IAM role ARNs that you have to create with the appropriate permissions. These roles are critical for allowing EKS and the nodes to communicate with other AWS services.

    • Exported Outputs: The kubeconfig output contains the configuration needed to connect to the Kubernetes cluster using kubectl or any Kubernetes management tool. The node_group_name output can help with further configuration or management tasks.

    You will likely need to configure additional Kubernetes resources like storage classes or network policies, and also deploy your machine learning workloads, which can be done with additional Pulumi resources or Kubernetes manifests.