AI Model Serving with Kubernetes and Gloo Edge

Question

Pulumi · Accepted Answer

To serve an AI model with Kubernetes and Gloo Edge, you'll need to set up a few things:

1. A Kubernetes cluster to run your model and serve predictions.
2. An AI model containerized, typically as a Docker image, that can be deployed to your Kubernetes cluster.
3. Gloo Edge as an API gateway to manage, secure, and route traffic to your model's serving endpoints.

Let's go through the program step by step:

### Step 1: Setting up a Kubernetes Cluster

We'll define a Kubernetes cluster using Pulumi's ecosystem for AWS (Amazon Web Services). Feel free to adapt this to your cloud provider of choice.

### Step 2: Containerizing the AI Model

You'll need to have your AI model containerized. This typically involves creating a Docker image that packages your model and a server (like Flask or FastAPI for Python-based models) that can respond to prediction requests.

### Step 3: Deploying the Model to Kubernetes

Once you have your model containerized, you'll use Pulumi to define a Kubernetes Deployment to run your model in the cluster.

### Step 4: Setting up Gloo Edge

For the API gateway, we'll configure Gloo Edge to route incoming requests to the model's serving endpoints. Note that Gloo Edge requires separate steps for installation and configuration, which are not covered in this program.

Below is a Pulumi program that outlines these steps. I'll explain how each part works after the code.

```python
import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as k8s
import pulumi_gloo as gloo

# Step 1: Create a Kubernetes cluster
eks_cluster = aws.eks.Cluster("eks-cluster", ...)

# Step 2: Assume you have a Docker image for your AI model
# `pulumi_docker.Image` can be used to build and publish Docker images. 
# Here we assume the image is already available at `ai-model-image-url`
image_url = "your-repo/ai-model-image-url:latest"

# Step 3: Deploy the AI Model to the Kubernetes Cluster
app_labels = {"app": "ai-model"}
ai_deployment = k8s.apps.v1.Deployment(
    "ai-model-deployment",
    spec={
        "selector": {"matchLabels": app_labels},
        "replicas": 2,
        "template": {
            "metadata": {"labels": app_labels},
            "spec": {
                "containers": [
                    {
                        "name": "ai-model",
                        "image": image_url,
                        "ports": [{"containerPort": 8080}],
                    }
                ]
            },
        },
    },
    opts=pulumi.ResourceOptions(provider=eks_cluster.provider),
)

# Step 4: Setup Gloo Edge to manage and route traffic
# This is a conceptual representation and might not be complete
gloo_api_gateway = gloo.VirtualService(
    "ai-model-virtual-service",
    virtual_host=gloo.VirtualServiceVirtualHostArgs(
        domains=["ai-model.example.com"],
        routes=[
            gloo.VirtualServiceVirtualHostRouteArgs(
                matchers=[gloo.VirtualServiceVirtualHostRouteMatchersArgs(prefix="/predict")],
                route_action=gloo.VirtualServiceVirtualHostRouteActionArgs(
                    single=gloo.VirtualServiceVirtualHostRouteActionSingleArgs(
                        kube=gloo.VirtualServiceVirtualHostRouteActionSingleKubeArgs(
                            ref=gloo.VirtualServiceVirtualHostRouteActionSingleKubeRefArgs(
                                name=ai_deployment.metadata["name"],
                                namespace=ai_deployment.metadata["namespace"],
                            ),
                            port=8080,
                        ),
                    ),
                ),
            ),
        ],
    ),
    opts=pulumi.ResourceOptions(provider=eks_cluster.provider),
)

# Output the endpoint for the AI model
pulumi.export("ai_model_endpoint", gloo_api_gateway.status["loadBalancer"]["ingress"][0]["hostname"])
```

Here's a breakdown of the Pulumi program:

- We created an EKS cluster (Amazon's Kubernetes service) as the environment where our AI model will execute.
- We assume the presence of a Docker image for your AI Model (replace `your-repo/ai-model-image-url:latest` with your actual image URL).
- We deploy the AI Model onto the cluster using a `Deployment` object, which ensures that the desired number of replicas of your model are running.
- We set up Gloo Edge, creating a `VirtualService` to define how traffic should be routed to our AI model's endpoints. The domain `ai-model.example.com` and the matcher `/predict` should be adjusted to match your actual domain and prediction endpoint.
- We output the endpoint hostname of the AI model, which you'd use to send prediction requests.

This setup is a simplified view, and the actual implementation might vary depending on the specifics of your model, Gloo Edge's installation, and your cloud provider's configuration details. If you're a novice, it's recommended to start with Kubernetes and AI model deployment basics, and gradually introduce complexity such as Gloo Edge for traffic management.