# Deploy a workload
<a name="sagemaker-hyperpod-eks-autoscaling-workload"></a>

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
   ```

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

   ```
   kubectl scale deployment inflate --replicas 10
   ```

1. 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
   ```

1. 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](https://github.com/aws/karpenter-provider-aws/tree/main/examples/workloads) on GitHub.