1. Kubernetes-Based Deep Learning Training with EKS


    When setting up a Kubernetes-based deep learning training environment on AWS EKS (Elastic Kubernetes Service), you will need to configure several components. Here's what you'll generally need to do:

    1. Create an EKS Cluster: A managed Kubernetes cluster on AWS where your deep learning workloads will run.

    2. Create an IAM Role for EKS: This role will allow EKS to make calls to other AWS services on behalf of your cluster.

    3. Set up Node Groups with GPU Support: Since deep learning workloads typically benefit from GPU acceleration, you'll want to set up your EKS cluster with nodes that have GPUs.

    4. Configure the Kubernetes VPC CNI Plugin: This plugin allows your Kubernetes pods to have the same IP addressing model as other AWS resources, such as EC2 instances.

    5. Deploy Your Deep Learning Workloads: Once the cluster and nodes are set up, you can deploy your deep learning applications or jobs to the cluster.

    Below is a Pulumi Python program that sets up a basic EKS cluster, an IAM role for EKS, a node group with GPU support, and installs the VPC CNI plugin. You can extend this program to deploy your specific deep learning workloads and configure other parameters as per your requirements.

    import pulumi import pulumi_aws as aws import pulumi_eks as eks # Create an EKS cluster. # Documentation: https://www.pulumi.com/registry/packages/aws/api-docs/eks/cluster/ cluster = eks.Cluster("eks-cluster", # Define the desired Kubernetes version. version="1.18", # Create a node group with GPU-enabled instances for deep learning tasks. node_group_options=eks.ClusterNodeGroupOptionsArgs( instance_type="p2.xlarge", # This is an example GPU instance type. desired_capacity=2, # Specify the number of nodes you want in your node group. min_size=1, max_size=3, ), # Add tags for identifying your cluster resources tags={ "project": "DeepLearning", "purpose": "Training", } ) # Export the cluster kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig) # Here we're using `pulumi_eks`, which is a Pulumi component that provides # a Pulumi-native way to provision EKS clusters with embedded best practices.

    This program creates the necessary infrastructure for running Kubernetes-based deep learning workloads. You can tweak the desired_capacity, min_size, and max_size properties of the node group to suit the scale of your workloads.

    To run this Pulumi program, save it to a file (e.g., eks_deep_learning.py), ensure you have the Pulumi CLI installed and configured with AWS credentials, then execute the following commands in your terminal:

    # Install the required Pulumi packages pulumi plugin install resource aws 6.13.3 pulumi plugin install resource eks 1.0.3 pulumi up

    This will start the process of provisioning the resources as described. Once this is successful, you will get a kubeconfig file as an output that you can use to interact with your Kubernetes cluster using kubectl or other Kubernetes tools. You can then proceed with the deployment of your deep learning applications.