Autoscaling AI Model Serving with ACID Zalando Operator on Kubernetes

Question

Pulumi · Accepted Answer

Autoscaling AI Model Serving enables dynamic adjustment of resources allocated to an AI model serving application based on the workload. This means that as the number of requests to your AI model increases, the infrastructure can scale up to maintain performance, and scale down when the demand decreases to save costs.

In this context, we'll be leveraging Kubernetes—an open-source platform for automating deployment, scaling, and operations of application containers across clusters of hosts—to manage our AI model serving deployment.

The ACID Zalando Operator is a Kubernetes operator that automates and simplifies the deployment of scalable and secure PostgreSQL databases on Kubernetes. PostgreSQL is a powerful, open-source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. However, for AI model serving, this operator is not directly related; it is more relevant to data persistence use cases, such as storing the AI models or logging the requests for predictions.

For the Kubernetes setup, Pulumi provides support through various packages such as `pulumi_kubernetes`, or cloud-specific packages like `pulumi_eks` for AWS, `pulumi_azure_native` for Azure, and `pulumi_gcp` for Google Cloud Platform. Each of these packages allows you to declare Kubernetes resources in a declarative manner using Python.

Let's focus on setting up a Kubernetes cluster and deploying a hypothetical AI model serving application with autoscaling capabilities. Below you will find a Pulumi program, written in Python, which:

1. Creates a Kubernetes cluster.
2. Deploys a sample AI model serving application with Horizontal Pod Autoscaler to automatically scale the number of pods in a Deployment or ReplicaSet.

```python
import pulumi
import pulumi_kubernetes as k8s

# Configurations for Kubernetes cluster and autoscaling are provided directly or through Pulumi's config system.
# Here, they are hardcoded for simplicity.

# Create a Kubernetes cluster on your chosen cloud provider
# (this could be AWS EKS, Azure AKS, GCP GKE, or any other supported Kubernetes provider).
# For this example, the details of the cluster creation are abstracted away.
# If you prefer a specific cloud provider (AWS, Azure, GCP, etc.), you can import the necessary Pulumi package.
# You need to configure the Pulumi CLI with the access credentials for your cloud provider.

# ... Cluster creation logic ...

# Create a Kubernetes provider instance using the generated kubeconfig from the cluster creation step.
k8s_provider = k8s.Provider("k8s-provider", kubeconfig=my_kubeconfig)

# Define the deployment of the AI model serving application.
app_labels = {"app": "ai-model-serving"}
app_deployment = k8s.apps.v1.Deployment("ai-model-serving-deployment",
    metadata={
        "name": "ai-model-serving"
    },
    spec={
        "selector": {
            "matchLabels": app_labels
        },
        "replicas": 1,  # Start with a single replica.
        "template": {
            "metadata": {
                "labels": app_labels
            },
            "spec": {
                "containers": [{
                    "name": "model-serving-container",
                    "image": "your-model-serving-image:latest", # Replace with your actual image.
                    "ports": [{
                        "containerPort": 8080
                    }],
                    # Define any resource requests and limits for the container
                    "resources": {
                        "requests": {
                            "cpu": "100m",
                            "memory": "200Mi"
                        },
                        "limits": {
                            "cpu": "500m",
                            "memory": "500Mi"
                        }
                    }
                    # Configure liveness and readiness probes as appropriate for your application
                }]
            }
        }
    }, opts=pulumi.ResourceOptions(provider=k8s_provider))

# Create an autoscaling policy based on CPU utilization.
cpu_autoscaler = k8s.autoscaling.v1.HorizontalPodAutoscaler("cpu-autoscaler",
    metadata={
        "name": "cpu-hpa",
        "namespace": "default"
    },
    spec={
        "scaleTargetRef": {
            "apiVersion": "apps/v1",
            "kind": "Deployment",
            "name": "ai-model-serving"
        },
        "minReplicas": 1,
        "maxReplicas": 10,  # Maximum number of replicas
        "targetCPUUtilizationPercentage": 50  # Autoscale when CPU utilization is at 50%
    }, opts=pulumi.ResourceOptions(provider=k8s_provider))

# Export the cluster's kubeconfig.
pulumi.export('kubeconfig', my_kubeconfig)
```

In this code:

- `app_labels` sets the labels for the Kubernetes services to identify which pods to target.
- `app_deployment` defines the desired state of the deployment for the AI model serving application.
- `cpu_autoscaler` implements the autoscaling policy based on CPU utilization.
- `my_kubeconfig` should be obtained from your specific cluster creation step. This might be loaded from an output of another Pulumi stack, your cloud provider after creating the Kubernetes cluster, or a local file if it's an existing cluster.

This is a simplified version and does not include real-world complexities like setting up the underlying storage, networking configurations, multiple environments (dev, stage, prod), security concerns, or sophisticated monitoring and logging. Moreover, the autoscaling here is rudimentary and only based on CPU usage. More complex autoscaling strategies can consider other metrics or even custom metrics specific to the application workload.