1. Kubernetes Autoscaling for ML Model Serving Workloads


    To implement autoscaling for ML model serving workloads in Kubernetes, you will need to configure a Horizontal Pod Autoscaler (HPA). The HPA automatically scales the number of pods in a deployment, replica set, or stateful set based on observed CPU or memory utilization or other custom metrics.

    Horizontal Pod Autoscaler monitors the resource usage of a Pod or a set of Pods and automatically adjusts their number based on the current demand. This can be particularly useful for machine learning workloads, which may experience varying levels of demand. ML models may need to scale up rapidly to handle large volumes of requests and then scale down to minimize cost when demand decreases.

    The HPA requires metrics to make scaling decisions. If you're scaling on CPU or memory usage, these metrics are provided by Kubernetes metrics server by default. For scaling based on custom metrics, like the number of requests per second, you need to integrate an additional metrics provider, such as Prometheus.

    The HPA is defined as a Kubernetes API resource. The following Pulumi program demonstrates how to define an HPA resource targeting a Kubernetes Deployment for serving a machine learning model. This HPA will scale your Deployment based on the average CPU utilization; once the average CPU utilization goes above a certain threshold, more pods will be added.

    This example assumes:

    • You have a Kubernetes cluster up and running.
    • The Kubernetes metrics server or another metrics provider is installed and collecting metrics.
    • There is an existing Deployment that serves your ML model.

    Let's create an HPA with Pulumi using Python:

    import pulumi import pulumi_kubernetes as k8s # Define the Horizontal Pod Autoscaler hpa = k8s.autoscaling.v1.HorizontalPodAutoscaler( "ml-model-hpa", # Name of the HPA resource spec=k8s.autoscaling.v1.HorizontalPodAutoscalerSpecArgs( # Specifications for HPA scale_target_ref=k8s.autoscaling.v1.CrossVersionObjectReferenceArgs( api_version="apps/v1", kind="Deployment", name="ml-model-serving-deployment" # Target deployment of your ML model ), min_replicas=1, # Minimum number of pod replicas max_replicas=10, # Maximum number of pod replicas target_cpu_utilization_percentage=80 # Target CPU utilization percentage ) ) # Export the HPA name pulumi.export('hpa_name', hpa.metadata.name)

    In this program, we create a Horizontal Pod Autoscaler named ml-model-hpa, which targets a deployment named ml-model-serving-deployment. The autoscaler will ensure that the number of pods stays between 1 and 10, and that average CPU utilization remains around 80%. If the CPU utilization goes beyond this threshold, the HPA will create more pods to distribute the load, up to the specified maximum.

    The pulumi.export statement will output the name of the HPA once it's deployed, which you can use to reference the HPA in other commands, such as kubectl get hpa.

    This is a basic example of a CPU-based HPA. For more complex use-cases, such as using custom metrics, you would need to define those metrics and possibly install a custom metrics server. To adapt this to your setup, you would need to adjust the scale_target_ref to target the proper deployment resource of your ML model, and set the min_replicas, max_replicas, and target_cpu_utilization_percentage according to your specific workload and performance requirements.

    Remember, Pulumi code is executed from your local machine or a CI/CD pipeline, not from within the Kubernetes cluster. After writing this code, you would run pulumi up to deploy the changes to your Kubernetes cluster, assuming you have the Pulumi CLI installed and configured to communicate with your cluster.

    For further understanding and information about the Kubernetes provider for Pulumi and the HorizontalPodAutoscaler, you can refer to Kubernetes documentation by Pulumi.