Serverless AI Inference with GKE and BackendConfig

Question

Pulumi · Accepted Answer

Serverless architectures are an efficient way to deploy machine learning models and other types of artificial intelligence (AI) workloads without worrying about managing the underlying infrastructure. Google Kubernetes Engine (GKE) is a managed service that simplifies running Kubernetes clusters, and it provides a serverless option through Google Cloud Run.

For this scenario, we'll create a basic serverless AI inference application using GKE and `BackendConfig` to serve a machine learning model. This will involve setting up a GKE cluster, deploying a containerized AI inference application, and configuring backend services to handle requests effectively.

Here's an overview of the steps we'll take in the Pulumi program:

1. **Set up a GKE cluster**: We will create a Kubernetes cluster with a specified node pool using Pulumi's `gcp` provider.
2. **Deploy the AI Inference Service**: Our AI service will be containerized and deployed to the GKE cluster. For simplicity, I'll demonstrate this with a placeholder Docker image.
3. **Backend and Frontend Configuration**: We will configure backend services for the application, which includes setting up `BackendConfig` objects to fine-tune how the Google Cloud Load Balancer interacts with our application.

Let's go ahead and build this step by step. Below is a Python program that uses `pulumi-gcp` to create and configure the necessary resources on Google Cloud Platform.

```python
import pulumi
from pulumi_gcp import container, compute

# Configuration variables for the GKE cluster
project_id = 'your-gcp-project-id'
region = 'us-central1'
cluster_name = 'serverless-ai-gke-cluster'
node_pool_name = 'serverless-ai-node-pool'

# 1. Set up a GKE cluster
# The GKE cluster that will run our serverless AI application
gke_cluster = container.Cluster(cluster_name,
                                initial_node_count=1,
                                node_config=container.ClusterNodeConfigArgs(
                                    oauth_scopes=[
                                        "https://www.googleapis.com/auth/cloud-platform"
                                    ],
                                    machine_type="n1-standard-1",
                                ),
                                location=region,
                                project=project_id)

# The node pool within our GKE cluster
node_pool = container.NodePool(node_pool_name,
                               cluster=gke_cluster.name,
                               initial_node_count=1,
                               node_config=container.NodePoolNodeConfigArgs(
                                   machine_type="n1-standard-1",
                                   oauth_scopes=[
                                       "https://www.googleapis.com/auth/cloud-platform"
                                   ],
                               ),
                               location=region,
                               project=project_id)

# 2. Deploy the AI Inference Service
# For demonstration purposes, we'll deploy a basic NGINX image that acts as our AI service
ai_app = container.Deployment("ai-app-deployment",
                              spec=container.DeploymentSpecArgs(
                                  replicas=1,
                                  selector=container.DeploymentSpecSelectorArgs(
                                      match_labels={"app": "ai-app"}),
                                  template=container.DeploymentSpecTemplateArgs(
                                      metadata=container.DeploymentSpecTemplateMetadataArgs(
                                          labels={"app": "ai-app"}),
                                      spec=container.DeploymentSpecTemplateSpecArgs(
                                          containers=[container.DeploymentSpecTemplateSpecContainerArgs(
                                              name="ai-app",
                                              image="nginx:latest"
                                          )]
                                      )
                                  )
                              ),
                              metadata=container.DeploymentMetadataArgs(
                                  namespace="default",
                              ))

# 3. Backend and Frontend Configuration
# Creating the BackendConfig resource to provide configuration to the load balancer for our app
backend_config = compute.BackendService("ai-app-backend-config",
                                        backends=[
                                            compute.BackendServiceBackendArgs(
                                                group=ai_app.metadata.name.apply(
                                                    lambda name: f"projects/{project_id}/locations/{region}/networkEndpointGroups/{name}-neg"
                                                ),
                                            )],
                                        health_checks=["your-health-check-name"],
                                        project=project_id)

# Export the URL of the AI inference service
pulumi.export("ai_app_url", backend_config.self_link)
```

In the above program:

- We create a GKE cluster with a single initial node using `container.Cluster`. The node pools use the `container.NodePool` to define the compute capacity that the cluster will use. We've specified a standard machine type and the necessary OAuth scopes for our nodes.
- We deploy a placeholder AI application using `container.Deployment`. This is where you would replace the `nginx:latest` image with your actual AI inference service image.
- We configure a backend service using `compute.BackendService`, which can be associated with Kubernetes' Ingress or Google Cloud Load Balancer to control the backend properties like session affinity, timeout, etc. The `group` refers to a network endpoint group that would be created based on our deployed services.
- Note: A health check `your-health-check-name` is referenced which should be created separately in your resource configuration; the name provided should match an actual health check created in GCP.
- The `pulumi.export` statement is used to output the `self_link` of the `BackendService`, which can be tracked or used further in the outside resources or CI/CD pipelines.

Please fill in the `your-gcp-project-id` and `your-health-check-name` placeholders with your GCP project ID and the name of the health check you've set up for your AI inference service.

You will need to replace the demonstration `nginx:latest` image with your AI service docker image and update the configuration accordingly to match the requirements of your AI service.