1. AI Model Serving with Kubernetes Kong Ingress Controller


    To create an AI model serving infrastructure with Kubernetes using the Kong Ingress Controller, you would need to do the following:

    1. Set up a Kubernetes cluster, if you don’t already have one.
    2. Deploy Kong Ingress Controller to the cluster. Kong will manage external access to the services in a Kubernetes cluster.
    3. Create and deploy your AI model serving application as a Deployment resource and expose it using a Service resource within Kubernetes.
    4. Define Ingress resources that tell Kong how to route external HTTP traffic to your AI model serving application.

    Here's a breakdown of how you would accomplish this with Pulumi:

    • Kubernetes cluster: You can create a cluster using managed Kubernetes services like Amazon EKS, Google GKE, or Azure AKS, or you can define it using the Kubernetes resources directly if you're running on-premises or on VMs. This isn't covered in the Pulumi code below, assuming you already have a cluster configured and your kubectl is configured to communicate with it.

    • Kong Ingress Controller: You would deploy this to your cluster. Kong provides an official Helm chart that makes it simple to install. With Pulumi, you can use the pulumi_kubernetes.helm.v3.Chart class to deploy this chart.

    • AI Model serving application: This should be containerized; if it isn't already, you would need to create a Docker image and push it to a container registry. Then you would define a Deployment and Service in Kubernetes to run and expose your application. This example assumes you have already containerized your AI Model serving app.

    • Ingress resource: You will define an Ingress resource using Pulumi's Kubernetes SDK, which will use Kong as the ingress controller to route traffic to your AI Model serving application.

    Here's a basic Pulumi program in Python that sets up the Kong Ingress Controller and a placeholder AI model serving application:

    import pulumi import pulumi_kubernetes as k8s from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Initialize Kubernetes provider using the current context from kubeconfig k8s_provider = k8s.Provider('k8s-provider', kubeconfig=pulumi.Config('kubernetes').require('kubeconfig')) # Deploy Kong Ingress Controller using the Helm Chart # Here, we assume that the Helm chart is available in the default Helm chart repository. # Please refer to the Kong documentation for the appropriate chart version and repository. kong_ingress_controller = Chart( 'kong', ChartOpts( chart='kong', version='2.5.0', # Specify the version of Kong Ingress Controller you wish to deploy namespace='kube-system', # Deploy into the kube-system namespace or choose an appropriate namespace fetch_opts=k8s.helm.v3.FetchOpts( repo='https://charts.konghq.com', # Kong Helm chart repository ), ), opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Placeholder for AI Model serving application deployment and service # Replace 'your-docker-image' with the image of your AI model serving application ai_model_deployment = k8s.apps.v1.Deployment( 'ai-model-deployment', spec={ 'selector': {'matchLabels': {'app': 'ai-model'}}, 'replicas': 1, 'template': { 'metadata': {'labels': {'app': 'ai-model'}}, 'spec': {'containers': [{'name': 'ai-model', 'image': 'your-docker-image'}]} } }, opts=pulumi.ResourceOptions(provider=k8s_provider) ) ai_model_service = k8s.core.v1.Service( 'ai-model-service', spec={ 'selector': {'app': 'ai-model'}, 'ports': [{'port': 80, 'targetPort': 8080}], # Adjust port and targetPort according to your application's configuration 'type': 'ClusterIP', # Use 'LoadBalancer' if you want to expose your service externally or 'ClusterIP' for internal access }, opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Ingress resource that uses Kong to manage incoming traffic ai_model_ingress = k8s.networking.v1.Ingress( 'ai-model-ingress', metadata={ 'annotations': { 'kubernetes.io/ingress.class': 'kong', # This annotation specifies that Kong should handle this Ingress } }, spec={ 'rules': [ { 'http': { 'paths': [ { 'path': '/ai-model', # Path for accessing the AI model serving application 'pathType': 'Prefix', # The type of path matching to use 'backend': { 'service': { 'name': ai_model_service.metadata['name'], 'port': {'number': 80}, # Port must match the one specified in the service } }, }, ], }, }, ], }, opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Export the AI model serving application URL pulumi.export('ai_model_url', ai_model_ingress.status['loadBalancer']['ingress'][0]['hostname'])

    Remember to replace 'your-docker-image' with the Docker image URL of your AI model serving application and adjust the ports accordingly to match your application's listening port.


    • We import Pulumi's Kubernetes SDK to interact with Kubernetes resources.
    • We define a Kubernetes provider instance using the current context from the kubeconfig file.
    • The Kong Ingress Controller is deployed using its Helm chart, specifying the chart version and attaching it to the Kubernetes provider.
    • Next, we define a placeholder Kubernetes deployment and service for the AI model service using the Docker image URL, ports, and other specifications.
    • We create an Ingress resource that tells the Kong Ingress Controller how to route external HTTP traffic to the AI model serving application, based on the specified path and annotations.
    • Finally, we export the AI model serving application URL, which can be obtained after deployment from the Kong Ingress' external IP or hostname.

    By running this Pulumi program, you should have a fully functional AI model serving application, accessible through Kong as the Ingress Controller.