1. Load Balancing for Distributed Tensorflow Training on EKS


    To set up load balancing for a distributed TensorFlow training system on Amazon EKS (Elastic Kubernetes Service), we can utilize several Pulumi components that work together to create a complete environment.

    This will involve creating an EKS cluster, deploying the necessary Kubernetes resources, and configuring the load balancer to distribute inference requests or training jobs across multiple pods running TensorFlow.

    Here's a high-level overview of the steps involved:

    1. Create an EKS Cluster: We'll need a Kubernetes cluster on AWS EKS where the TensorFlow application will run. We will use the pulumi_eks package which provides a high-level interface to deploy an EKS cluster.

    2. Setup Node Groups: EKS manages the Kubernetes worker nodes as part of the NodeGroup feature. A Pulumi EKS NodeGroup represents a set of managed instances that run the Kubernetes worker roles.

    3. Install the VPC CNI Plugin: The Amazon VPC CNI plugin for Kubernetes enables pods to have the same IP address inside the pod as they do on the VPC network.

    4. Configure Load Balancing: Kubernetes provides different types of load balancers. The most common type used in conjunction with EKS is the NLB (Network Load Balancer) or ALB (Application Load Balancer), which can be configured with Pulumi using the service type LoadBalancer in the Service resource.

    For simplicity and conciseness, I will focus on creating the EKS cluster and setting up a sample load balancer using a Kubernetes Service. You'll then need to deploy your TensorFlow training application to the cluster and expose it via the load balancer.

    Let's start with the Python Pulumi program to create an EKS cluster and set up load balancing:

    import pulumi import pulumi_eks as eks # Create an EKS cluster. cluster = eks.Cluster('eks-cluster', desired_capacity=2, min_size=1, max_size=3, instance_type='m5.large', # The instance profile to use for the worker nodes in the cluster. # instance_roles parameter can be used here if you need to assign existing IAM roles. ) # This could be replaced with a Managed Node Group or Fargate profile configuration. # Deploy a Kubernetes Service of type LoadBalancer which will provision an AWS LoadBalancer # to handle traffic to the deployed TensorFlow application. app_labels = {"app": "tensorflow"} app_service = cluster.core_v1.Service("app-svc", spec={ "type": "LoadBalancer", "ports": [{"port": 80, "target_port": 80}], "selector": app_labels }) # Exporting the service's load balancer ingress to access it from the internet. ingress = app_service.status.apply(lambda status: status.load_balancer.ingress[0] if status.load_balancer.ingress else None) pulumi.export('app_load_balancer', ingress)


    • We import the required Pulumi modules.
    • We create an EKS cluster with the eks.Cluster class. The cluster will have desired worker node capacity between 1 and 3, and the nodes will be of type m5.large.
    • A Kubernetes Service is created which specifies a LoadBalancer type. This will automatically create an AWS Load Balancer for the Service. When you deploy your application, you will match the selector with the labels on your TensorFlow deployment.
    • We export the ingress point of the load balancer so that you can connect to the TensorFlow application from outside the VPC.

    Please note that this program assumes you'll be deploying a TensorFlow application with pods that use labels matching app: tensorflow. Also, the cluster provisioning within AWS will handle all underlying configurations like VPC, subnets, and security groups.

    To deploy the TensorFlow training workloads and service them through the load balancer, you will have to create a Deployment which consists of TensorFlow-serving containers, and then expose those with a Kubernetes Service of type LoadBalancer, as shown above. Remember to set the appropriate targetPort to match the port your application is listening on.

    Once you have this cluster up and running, you would then create and configure the actual TensorFlow training environment within the cluster, including setting up your distributed training workloads and deploying the TensorFlow serving containers. Make sure your machine learning workloads are well-suited to run within the node groups you have specified.