Kubernetes Hosted Inference Services for Real-time Predictions

Question

Pulumi · Accepted Answer

To run real-time prediction services on Kubernetes, you would typically need a few components:

1. **A Kubernetes cluster**: The physical infrastructure to run your containers.
2. **Containerized model inference service**: Your prediction model packaged as a Docker container, which can be deployed as a service.
3. **Ingress or LoadBalancer**: To expose your service so that it can receive external traffic for inference requests.

For the Kubernetes cluster, you can either use a managed Kubernetes service like Amazon EKS (Elastic Kubernetes Service), Google GKE (Google Kubernetes Engine), Azure AKS (Azure Kubernetes Service), or other cloud providers' managed services. Or you can configure your own on a set of virtual machines.

Given that you've requested a Kubernetes scenario, I’ll provide you with a Pulumi program that sets up an AWS EKS cluster, deploys a sample inference service (you'd replace this with your container), and sets up a LoadBalancer service to expose your application.

First, we need to set up an EKS cluster using Pulumi's `eks` package, which simplifies creating and managing an EKS cluster. Then we'll define a Kubernetes Deployment and Service to run and expose our inference service.

Here's a Pulumi program that performs the aforementioned tasks:

```python
import pulumi
import pulumi_eks as eks
import pulumi_kubernetes as k8s

# Create an EKS cluster with the default configuration.
cluster = eks.Cluster('eks-cluster')

# Define the Kubernetes Deployment for the inference service.
# For illustration purposes, we use the `nginxdemos/hello` image; replace this with your inference service image.
app_labels = {'app': 'inference-service'}
deployment = k8s.apps.v1.Deployment(
    'inference-deployment',
    metadata={'namespace': cluster.core_v1_namespace_name},
    spec={
        'selector': {'matchLabels': app_labels},
        'replicas': 2,  # You can scale it based on your needs.
        'template': {
            'metadata': {'labels': app_labels},
            'spec': {'containers': [{'name': 'inference', 'image': 'nginxdemos/hello'}]}
        }
    },
    opts=pulumi.ResourceOptions(provider=cluster.provider)
)

# Define a Kubernetes Service with a LoadBalancer to expose the inference service.
service = k8s.core.v1.Service(
    'inference-service',
    metadata={'namespace': cluster.core_v1_namespace_name},
    spec={
        'type': 'LoadBalancer',
        'selector': app_labels,
        'ports': [{'port': 80, 'targetPort': 80}]
    },
    opts=pulumi.ResourceOptions(provider=cluster.provider)
)

# Export the URL to access the inference service.
pulumi.export('inference_service_url', service.status.apply(lambda s: s['load_balancer']['ingress'][0].get('hostname')))
```

Let's break down what's happening in this program:

- We instantiate an EKS cluster with the `eks.Cluster` class. This creates an AWS EKS cluster with all the necessary underlying resources like an EC2 node group. Pulumi manages all necessary configurations and setups automatically.
  
- We then define a Kubernetes Deployment named `inference-deployment`. Deployments manage stateless services running on the cluster.

- We create a Kubernetes Service of type `LoadBalancer`, which distributes incoming network traffic across the replicas of our deployment. It exposes a single access point for our service, abstracting individual pod IPs in the cluster.

- Lastly, we export the LoadBalancer's URL as an output of the Pulumi program, which you can use to interact with the inference service once everything is set up.

Make sure you have Docker installed and configured, and replace `nginxdemos/hello` with the Docker image of your inference service. You can then deploy this Pulumi program using the Pulumi CLI.

By running this Pulumi program, an EKS cluster would be provisioned along with the necessary Kubernetes resources to host your inference service, accessible via a LoadBalancer URL.