AI Workload Traffic Management with Kong and Kubernetes

Question

Pulumi · Accepted Answer

To set up AI workload traffic management using Kong and Kubernetes, we need to take the following steps:

1. **Set up a Kubernetes cluster**: This will be the environment where your application and the Kong API Gateway will be running. If you're using a cloud provider like AWS, Azure, or GCP, you can use their managed Kubernetes services like EKS, AKS, or GKE respectively.

2. **Deploy Kong**: Kong can be deployed as an Ingress controller on Kubernetes, managing access to your services using custom resources defined in Kubernetes (`Ingress`, `Service`, etc.).

3. **Configure traffic management**: Define the necessary Kong plugins, routes, and services to manage traffic to your AI workloads effectively.

Here is a Pulumi program that creates a Kubernetes cluster using a high-level Pulumi component and deploys Kong as an Ingress controller to manage traffic:

```python
import pulumi
import pulumi_kubernetes as k8s
from pulumi_kubernetes.helm.v3 import Chart, ChartOpts

# Initialize a Kubernetes provider using default kubeconfig credentials on the local machine
k8s_provider = k8s.Provider("k8s-provider")

# Deploy a Helm Chart for Kong
# In this example, we assume that you have access to the Kubernetes cluster configured in your kubeconfig.
kong_chart = Chart(
    "kong",
    ChartOpts(
        chart="kong",
        version="2.3.0", # Example version, please choose an appropriate version for your needs
        # The values can be adjusted to configure Kong to suit your specific requirements.
        # Refer to the official Kong Helm chart for configuration details:
        # https://github.com/Kong/charts/blob/main/charts/kong/README.md
        values={
            "ingressController": {
                "installCRDs": False, # Set to True if you want Helm to install and manage the CRDs
                "enabled": True, # Enable the Kong Ingress controller
            },
            "proxy": {
                "type": "LoadBalancer", # Expose the Kong proxy using a LoadBalancer service
            },
            # You might want to change the following configurations as per your own resource requirements
            "resources": {
                "requests": {
                    "cpu": "100m",
                    "memory": "256Mi",
                },
                "limits": {
                    "cpu": "500m",
                    "memory": "1Gi",
                },
            },
        },
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Export the load balancer IP or hostname to access Kong's proxy service
pulumi.export("kong_proxy_ip", kong_chart.get_resource('v1/Service', 'kong-kong-proxy').status.apply(lambda status: status.load_balancer.ingress[0]))
```

This program will install Kong in your Kubernetes cluster using the Helm chart for Kong. Please adjust the chart version and values according to your requirements. The values provided for resources are minimal and should be scalable according to the specific needs of your AI workloads.

The `pulumi.export` line will output the IP address or hostname of the Kong proxy service upon completion, which you can use as the entry point for your API traffic.

To manage the traffic for AI workloads, you can define specific routes, services, and plugins in Kong. These can be specified as Kubernetes resources (`Ingress`, `Service`, etc.) or using Kong's CRDs if you enable `installCRDs: True` in the chart options.

Remember to replace the chart version with the one that suits your compatibility. You can find more details about the configuration options for Kong in the [official Helm chart documentation](https://github.com/Kong/charts/blob/main/charts/kong/README.md).

This program assumes you have a Kubernetes cluster already running and kubectl configured to interact with it on your local environment. If you need to create a Kubernetes cluster using Pulumi, you would use a respective cloud provider's managed Kubernetes service, such as `eks.Cluster` for AWS, `aks.Cluster` for Azure, or `gke.Cluster` for Google Cloud, with the corresponding Pulumi packages.