1. Distributed Training Workloads on Kubernetes Clusters


    To accomplish distributed training workloads on Kubernetes clusters using Pulumi and Python, we will follow several steps:

    1. Create the Kubernetes cluster: The core of our setup, where we will schedule our distributed training jobs.
    2. Define the distributed training job: Outline the containers, configuration, and resources needed for the distributed training workload.
    3. Deploy the job on the cluster: Apply the defined configurations to the cluster to start the training.

    In this guide, I'll demonstrate how to achieve this on AWS using Amazon EKS (Elastic Kubernetes Service), which is a fully managed Kubernetes service. We will deploy a Kubernetes cluster in AWS and then deploy a sample distributed training job using Kubernetes resources such as Deployment, StatefulSet, Job, or custom resources if working with a framework like Kubeflow.

    Here is the program that sets up a Kubernetes cluster and deploys a mock distributed training job. In a real-world scenario, you would need to replace the placeholder job configuration with your actual training script and dependencies.

    import pulumi import pulumi_aws as aws import pulumi_kubernetes as k8s # Create an EKS cluster with default settings. # We will have different options to customize the EKS cluster if needed, such as the version, # size and type of instances for the worker nodes, and the desired capacity. cluster = aws.eks.Cluster("eks-cluster") # Create a Kubernetes Provider pointing to the created cluster. # The provider is used for deploying resources to the cluster. k8s_provider = k8s.Provider("k8s-provider", kubeconfig=cluster.kubeconfig.apply(lambda c: c)) # A distributed training job typically consists of multiple pods running the same workload in parallel. # Kubernetes Jobs are suitable for this purpose. The number of completions can be used to control the level of parallelism. # Here is an example of a Kubernetes Job that would be used for a distributed training scenario. training_job = k8s.batch.v1.Job( "training-job", spec=k8s.batch.v1.JobSpecArgs( completions=3, # Assuming we want three parallel workers for training. parallelism=3, # The job will run with up to 3 Pods running concurrently. template=k8s.core.v1.PodTemplateSpecArgs( spec=k8s.core.v1.PodSpecArgs( containers=[k8s.core.v1.ContainerArgs( name="training-container", image="YOUR_TRAINING_IMAGE", # Use your custom image with training logic here. command=["python", "train.py"] # Replace this command with the one your container requires. )], restart_policy="Never", ), ), ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Export the cluster's kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig)


    • EKS Cluster: We use the aws.eks.Cluster class to create a new managed Kubernetes cluster. AWS EKS offloads a lot of the heavy lifting when it comes to managing Kubernetes, such as the control plane.

    • Kubernetes Provider: The pulumi_kubernetes.Provider class connects to the created EKS cluster to manage Kubernetes resources. We use the cluster's kubeconfig as the credentials to access the cluster.

    • Kubernetes Job: The pulumi_kubernetes.batch.v1.Job class represents a batch job in Kubernetes. Jobs are ideal for batch or background tasks that run to completion, which is often the case with distributed training tasks. The completions attribute specifies the desired number of successfully finished pods, and parallelism controls the maximum number of pods that can run in parallel during the job execution.

    • Container Image: Within the job specification, you define the container image that holds your training code (YOUR_TRAINING_IMAGE). This should be replaced with the image URL of your training application.

    • Training Script: Replace the command with the command that needs to be run within the container to start the distributed training. In this example, it's a Python script named train.py.

    • Resource Options: Usage of ResourceOptions in pulumi enables specifying additional options; here, we're tying the Kubernetes resources to a specific provider that manages the resources in the designated EKS cluster.

    Please note that this is a simplified example. Depending on the training job's requirements, you might need to configure additional parameters, such as resource requests/limits for CPU and memory, volume mounts for data access, and configuring environment variables.

    Remember to replace YOUR_TRAINING_IMAGE with the actual image URL and adjust the command according to what your container requires to start the training process. Additionally, make sure your AWS credentials are configured properly for Pulumi to interact with AWS services.