Deploy a workload - Amazon SageMaker AI
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Deploy a workload

The following examples demonstrate how HyperPod autoscaling with Karpenter automatically provisions nodes in response to workload demands. These examples show basic scaling behavior and multi-availability zone distribution patterns.

Deploy a simple workload

  1. The following Kubernetes deployment includes pods that request for 1 CPU and 256M memory per replica or pod. In this scenario, the pods aren’t spun up yet.

    kubectl apply -f https://raw.githubusercontent.com/aws/karpenter-provider-aws/refs/heads/main/examples/workloads/inflate.yaml

  2. To test the scale up process, run the following command. Karpenter will add new nodes to the cluster.

    kubectl scale deployment inflate --replicas 10

  3. To test the scale down process, run the following command. Karpenter will remove nodes from the cluster.

    kubectl scale deployment inflate --replicas 0
Deploy a workload across multiple AZs

  1. Run the following command to deploy a workload that runs a Kubernetes deployment where the pods in deployment need to spread evenly across different availability zones with a max Skew of 1.

    kubectl apply -f https://raw.githubusercontent.com/aws/karpenter-provider-aws/refs/heads/main/examples/workloads/spread-zone.yaml

  2. Run the following command to adjust number of pods:

    kubectl scale deployment zone-spread --replicas 15

    Karpenter will add new nodes to the cluster with at least one node it a different availability zone.

For more examples, see Karpenter example workloads on GitHub.