Scalable Kubernetes Pods for Real-Time Inference Services

Question

Pulumi · Accepted Answer

To set up a scalable Kubernetes deployment for real-time inference services, we'll focus on creating a Kubernetes `Deployment` that manages the desired state of our pods and scales them as needed. In addition, we will use a `Service` to expose the pods to the network, and a `HorizontalPodAutoscaler` (HPA) to automatically scale our pods based on certain metrics, like CPU or memory usage.

The key components are:
- **Deployment**: Manages the deployment and scaling of a set of Pods, and provides updates to the Pods along with self-healing capabilities.
- **Service**: An abstraction that defines a logical set of Pods and a policy by which to access them. This can be used to expose the application to be accessed via the network.
- **HorizontalPodAutoscaler (HPA)**: Automatically scales the number of Pods in a deployment, replicaset, or statefulset based on observed CPU or memory utilization.

Below I'll illustrate how to define a simple real-time inference service using Pulumi in Python:

1. Create a `Deployment` to run your application code. Within the deployment, define your application's container image, the number of replicas, and resource requests/limits. This ensures that the containers have sufficient computational resources.
2. Define a `Service` to expose your application internally within the Kubernetes cluster or externally via the internet.
3. Define a `HorizontalPodAutoscaler` to automatically scale the number of Pods based on the defined CPU utilization threshold.

Let's put these into a Pulumi program:

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

# Define the container image for your inference service
container_image = "your-inference-service-image:latest"  # Replace with your container image

# Define a Kubernetes Deployment
deployment = Deployment(
    "inference-deployment",
    spec={
        "selector": {"matchLabels": {"app": "inference-service"}},
        "replicas": 1,  # Starting with 1 pod
        "template": {
            "metadata": {"labels": {"app": "inference-service"}},
            "spec": {
                "containers": [{
                    "name": "inference-container",
                    "image": container_image,
                    "resources": {
                        "requests": {"cpu": "100m", "memory": "200Mi"},  # Minimal resources
                        "limits": {"cpu": "500m", "memory": "500Mi"},  # Max resources
                    },
                }],
            },
        },
    })

# Expose the deployment with a Kubernetes Service
service = Service(
    "inference-service",
    spec={
        "selector": {"app": "inference-service"},
        "ports": [{"port": 80, "targetPort": 8080}],  # Expose your app on port 80, container listens on 8080
        "type": "LoadBalancer",  # Use LoadBalancer if you want to expose externally
    })

# Define a HorizontalPodAutoscaler to automatically scale the deployment
hpa = HorizontalPodAutoscaler(
    "inference-hpa",
    spec={
        "scaleTargetRef": {
            "apiVersion": "apps/v1",
            "kind": "Deployment",
            "name": deployment.metadata["name"],
        },
        "minReplicas": 1,
        "maxReplicas": 10,  # Maximum number of replicas
        "metrics": [{
            "type": "Resource",
            "resource": {
                "name": "cpu",
                "target": {
                    "type": "Utilization",
                    "averageUtilization": 70,  # Target CPU utilization before scaling up
                },
            },
        }],
    })

# Export the Service name and endpoint
pulumi.export("service_name", service.metadata["name"])
pulumi.export("service_endpoint", service.status["load_balancer"]["ingress"][0]["ip"])
```

This program will create a Kubernetes Deployment that ensures a single pod is running with your container image. It exposes the Deployment with a Service, which uses a `LoadBalancer` to make your pods accessible from outside the Kubernetes cluster. It also includes an HPA resource that will automatically scale the number of pods running between 1 and 10, based on the CPU load.

To deploy this infrastructure, you first need to have the Pulumi CLI installed and your Kubernetes cluster already set up. Then:
- Save the above Python code to a file called `__main__.py`.
- Run `pulumi up` in the same directory as your Python file to deploy the resources to your Kubernetes cluster.
- Once deployed, the external IP (if `LoadBalancer` is supported by your cluster provider) to access your application will be output.

Remember to replace `"your-inference-service-image:latest"` with the actual image you will be deploying. Also, adjust the CPU and memory requests/limits as well as the target CPU utilization according to the needs of your real-time inference service.