Kubernetes-Based Auto-Scaling for ML Models

Question

Pulumi · Accepted Answer

Auto-scaling in Kubernetes allows you to adjust the number of running Pods (containers) in a deployment based on the current load or other metrics. This capability is crucial for managing machine learning (ML) model workloads, which can have unpredictable and variable resource requirements based on incoming requests and data processing needs.

To implement auto-scaling for ML models in Kubernetes, you would typically use the Horizontal Pod Autoscaler (HPA). The HPA automatically scales the number of Pods in a replication controller, deployment, or replica set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics).

Here's how you can set up a Kubernetes-based auto-scaling for ML models using Pulumi:

1. Define a `Deployment` for your ML models. This will set the desired state for your application, including the container image to use and the initial number of replicas.
2. Define a `Service` that exposes your ML models, making them accessible over the network.
3. Define a `HorizontalPodAutoscaler` that targets the `Deployment`. The HPA will monitor the load and automatically scale the number of replicas up or down based on the defined metrics.

The following Python program demonstrates how to create these resources using Pulumi. The example assumes that you have a container image for your ML model that you want to deploy and scale.

```python
import pulumi
from pulumi_kubernetes.apps.v1 import Deployment
from pulumi_kubernetes.core.v1 import Service
from pulumi_kubernetes.autoscaling.v2beta2 import HorizontalPodAutoscaler
from pulumi_kubernetes.meta.v1 import ObjectMeta

# Define the ML model deployment.
ml_model_deployment = Deployment(
    "ml-model-deployment",
    spec={
        "selector": {"matchLabels": {"app": "ml-model"}},
        "replicas": 1,  # Start with one replica.
        "template": {
            "metadata": {"labels": {"app": "ml-model"}},
            "spec": {
                "containers": [
                    {
                        "name": "ml-model",
                        "image": "your-ml-model-image:latest",  # Replace with your image.
                        # Define resource requests and limits for the container, should be configured based on model requirements.
                        "resources": {
                            "requests": {"cpu": "500m", "memory": "1Gi"},
                            "limits": {"cpu": "1", "memory": "2Gi"},
                        },
                    }
                ]
            },
        },
    })

# Define a service to expose the ML model over the network.
ml_model_service = Service(
    "ml-model-service",
    metadata=ObjectMeta(name="ml-model-service", labels={"app": "ml-model"}),
    spec={
        "selector": {"app": "ml-model"},
        "ports": [{"port": 80, "targetPort": 8080}],  # Adjust the ports as necessary.
        "type": "LoadBalancer",
    })

# Define an auto-scaler for the ML model deployment.
ml_model_hpa = HorizontalPodAutoscaler(
    "ml-model-hpa",
    spec={
        "scaleTargetRef": {
            "apiVersion": "apps/v1",
            "kind": "Deployment",
            "name": ml_model_deployment.metadata["name"],
        },
        "minReplicas": 1,
        "maxReplicas": 10,  # Define the maximum number of replicas.
        "metrics": [
            {
                "type": "Resource",
                "resource": {
                    "name": "cpu",
                    "target": {
                        "type": "Utilization",
                        "averageUtilization": 50,  # Target CPU utilization percentage to scale up or down.
                    },
                },
            }
        ],
    })

# Export the service endpoint for access.
pulumi.export("ml_model_service_url", pulumi.Output.concat("http://", ml_model_service.status.apply(
    lambda status: status["load_balancer"]["ingress"][0]["ip"] if status["load_balancer"]["ingress"] else "pending...")))
```

In this program:

- The `Deployment` resource establishes the desired state for the application, where `replicas` is the number of Pod instances.

- The container within the deployment should have its resource `requests` and `limits` set according to the expected load and resource requirements of the ML model. It is crucial to configure these values correctly to ensure proper autoscaling behavior.

- The `Service` resource creates a stable endpoint for accessing the ML models over the network. Here, it uses a LoadBalancer type to distribute traffic across the Pods.

- The `HorizontalPodAutoscaler` resource targets the ML model deployment and adjusts the number of replicas based on CPU utilization. The `minReplicas` and `maxReplicas` fields control the minimum and maximum number of Pod replicas.

- The `metrics` field is configured to use CPU utilization for scaling decisions (`averageUtilization` is set to 50%, meaning the HPA will increase the number of replicas if the average CPU utilization across all Pods goes over 50%).

You will need to replace the image in the deployment spec with the container image for your ML model. It's also important to customize the ports in both the Deployment and Service resources to match the ports used by your ML model application.