1. Distributed Training of Machine Learning Models on EKS


    Distributed training of machine learning models involves using multiple compute resources to train models more quickly and effectively. In the context of Amazon Web Services (AWS), 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.

    To set up a distributed training system for machine learning models on EKS using Pulumi, you would typically need to:

    1. Create an EKS cluster where the training will occur.
    2. Define the compute resources needed for the training jobs, usually in the form of Kubernetes worker nodes.
    3. Configure the necessary IAM roles and policies for EKS and any other AWS services that are involved in the training process.
    4. Set up the machine learning environment and distribute the training jobs to the worker nodes.

    Below is a Pulumi Python program that outlines these steps to create an EKS cluster suitable for distributed machine learning training:

    import pulumi import pulumi_eks as eks # Create an EKS cluster with default settings. # EKS automatically adds worker nodes as part of its default node group. cluster = eks.Cluster("eks-cluster") # Export the cluster's kubeconfig. pulumi.export('kubeconfig', cluster.kubeconfig) # Example usage of the cluster: deploy a simple NGINX pod. app_labels = { "app": "nginx" } nginx_pod = cluster.core_v1.Pod( "nginx-pod", metadata={ "labels": app_labels, }, spec={ "containers": [{ "name": "nginx", "image": "nginx:1.7.9", "ports": [{ "containerPort": 80 }], }], }) pulumi.export('pod_name', nginx_pod.metadata["name"])

    This is a basic program, and for distributed machine learning, you would need additional configurations. Specifically:

    • IAM Roles and Policies: These need to be configured to allow EKS and worker nodes to access necessary AWS services like ECR (Elastic Container Registry) for pulling Docker images of the training software or S3 for storing data and model artifacts.
    • GPU-based Instances: If your machine learning training jobs can be accelerated by GPUs, you'll need to provision GPU-based instances as your worker nodes.
    • Kubernetes Jobs or Deployments: For running your training jobs, you may use Kubernetes resources such as Jobs for training on a single dataset or Deployments for continuous training processes.

    IAM Roles and Policies

    IAM roles and policies for EKS can be created and managed in Pulumi as well. The aws-iam package allows you to define these roles and policies within your Pulumi program. Below is an example code snippet that would be incorporated into the larger Pulumi program to create necessary IAM roles:

    import pulumi_aws as aws # Create an IAM role that the EKS cluster can assume to access AWS services. eks_role = aws.iam.Role("eksRole", assume_role_policy=cluster.service_role_policy) # Attach policies to the role as needed. E.g., Amazon EKS Worker Node Policy, EKS CNI Policy, etc. worker_node_policy = aws.iam.RolePolicyAttachment("workerNodePolicyAttachment", role=eks_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_EKS_WORKER_NODE_POLICY) # ... similar attachments for the required policies

    GPU-based Instances

    To select GPU-based instances for worker nodes, you would have to specify within the node group definitions of your EKS cluster:

    node_group = eks.NodeGroup("node-group", cluster=cluster.core, instance_type="p3.2xlarge", # Example GPU instance type desired_capacity=1, # Desired number of nodes in the node group min_size=1, max_size=2, labels={"ondemand": "true"}, subnet_ids=cluster.core.subnet_ids)

    Kubernetes Jobs or Deployments

    To deploy the machine learning training jobs on the EKS cluster, you can define Kubernetes Jobs or Deployments in Pulumi:

    import pulumi_kubernetes as k8s # Define a Kubernetes Job for a training task. training_job = k8s.batch.v1.Job( "training-job", spec={ "template": { "spec": { "containers": [{ "name": "training-container", "image": "your-training-container-image", # Replace with your container image "args": ["--epochs", "100", "--model-dir", "s3://your-model-bucket/"], }], "restartPolicy": "Never", } }, "backoffLimit": 4, }, opts=pulumi.ResourceOptions(provider=cluster.provider))

    In the job definition, you specify the container image, which will have the training code and dependencies. The args are the command-line arguments that your training program needs.

    This Pulumi program sets your cluster up for distributed training. However, real-world applications may require more fine-tuned configurations, which would involve setting up distributed training using specific machine learning frameworks like TensorFlow or PyTorch within the Kubernetes cluster. These details are usually encapsulated in the training container and its associated Kubernetes job definition.