1. Scalable ML Model Training with EKS Managed Node Groups


    Scalable machine learning (ML) model training on Kubernetes can be accomplished using Amazon Elastic Kubernetes Service (EKS), which offers managed node groups to easily scale worker nodes. Managed node groups automate tasks such as node provisioning, application of patches, and version upgrades, which simplifies the cluster management.

    To configure this infrastructure, we need to define an EKS cluster and at least one managed node group. The managed node group will be tailored for machine learning workloads by selecting appropriate instance types and sizes which provide the necessary compute (CPU/GPU) and memory resources. Additionally, we will apply Kubernetes labels and taints to ensure that machine learning workloads are scheduled on the appropriate nodes. We'll also configure scaling settings so the node group can adjust in size based on workload demands.

    Here is a Pulumi program written in Python that sets up an EKS cluster with a managed node group optimized for ML model training:

    import pulumi import pulumi_eks as eks # Define the EKS cluster cluster = eks.Cluster('ml-training-cluster', # By not specifying certain optional parameters like vpc_id or subnet_ids, # we're implicitly telling Pulumi to provision default VPC and subnets for this EKS cluster. ) # IAM role for the EKS cluster nodes node_role = eks.create_node_group_role('nodegroup-iam-role') # Define the managed node group for machine learning workloads ml_node_group = eks.ManagedNodeGroup('ml-node-group', cluster=cluster.core, # Associate the node group with the EKS cluster instance_types=['p2.xlarge'], # Select an instance type suitable for ML workloads, such as a GPU instance node_role=node_role, # IAM role for nodes in this node group scaling_config=eks.ManagedNodeGroupScalingConfigArgs( min_size=1, # Minimum size of the node group max_size=5, # Maximum size for the node group to scale up to desired_size=2 # Desired number of nodes - change this based on expected workloads ), labels={'workload-type': 'ml'}, # Kubernetes labels to differentiate nodes purposed for ML workloads taints=[ # Add taints to prevent non-ML workloads from scheduling on GPU nodes eks.ManagedNodeGroupTaintArgs( key='workload-type', value='ml', effect='NoSchedule' ), ], disk_size=100, # Disk size for each node, in GiB ) # Export the cluster's kubeconfig and the managed node group's name as stack outputs pulumi.export('kubeconfig', cluster.kubeconfig) pulumi.export('managed_node_group_name', ml_node_group.node_group_name)


    • We start by importing the Pulumi SDK for EKS to interact with AWS EKS resources.
    • We create an eks.Cluster class instance named ml-training-cluster. When we omit parameters like VPC and subnet IDs, Pulumi uses reasonable defaults to provision the cluster.
    • We utilize the eks.create_node_group_role utility function to create an IAM role with the necessary permissions for the EKS node group.
    • Then, we define a ManagedNodeGroup class instance named ml-node-group. This node group is configured with instance types suited for ML workloads (like p2.xlarge for GPU capabilities) and has initial scaling configuration set to have a minimum of 1 node, a maximum of 5, and a desired count of 2. Adjust the min_size, max_size, and desired_size based on your needs.
    • We also apply Kubernetes labels and taints to make sure that only ML workloads are scheduled on these nodes.
    • Finally, we export the cluster's kubeconfig for accessing the cluster and the node group's name as stack outputs, so they can be easily retrieved when needed.

    Using this code, you will create a scalable environment for ML workloads on AWS. As your resource demand grows, the node group can scale within the limits you have specified, providing a platform to efficiently run ML model training processes.