Secure AI TensorFlow Serving with Validating Webhook Configurations

Question

Pulumi · Accepted Answer

To set up a secure AI TensorFlow Serving environment on Kubernetes, you would generally need to configure resources to run the TensorFlow Serving container and expose it via a service. A validating webhook configuration will allow you to intercept and validate requests made to your cluster, ensuring security and compliance with your operational policies.

Here's what each part of the setup entails:

1. **TensorFlow Serving Deployment**: This is a Kubernetes deployment resource that runs your TensorFlow model with TensorFlow Serving. It requires a container image with your trained model and TensorFlow Serving installed.

2. **Service**: A Kubernetes Service is needed to expose the TensorFlow Serving deployment so that it can receive requests.

3. **Validating Webhook Configuration**: This Kubernetes resource will set up a series of webhooks that Kubernetes will hit before admitting a request. This is where you can integrate the logic to validate or mutate the requests to your TensorFlow Serving application, ensuring they meet your criteria. It requires setting up webhook endpoints, which could be an internal service or an external URL, that will receive the admission requests and validate them.

Below is a Pulumi program in Python that creates these necessary resources on Kubernetes to serve a TensorFlow model securely while allowing a validating webhook configuration for additional request checks.

```python
import pulumi
import pulumi_kubernetes as k8s

# The namespace where you want to deploy your resources
namespace = k8s.core.v1.Namespace("ai-serving-namespace",
                                   metadata={"name": "tensorflow-serving"})

# Deployment of the TensorFlow Serving application
app_labels = {"app": "tensorflow-serving"}
deployment = k8s.apps.v1.Deployment("tf-serving-deployment",
                                    metadata={
                                        "namespace": namespace.metadata["name"],
                                        "labels": app_labels
                                    },
                                    spec={
                                        "replicas": 1,
                                        "selector": {
                                            "matchLabels": app_labels
                                        },
                                        "template": {
                                            "metadata": {
                                                "labels": app_labels
                                            },
                                            "spec": {
                                                "containers": [{
                                                    "name": "tensorflow-serving",
                                                    # Replace with the appropriate image for your TensorFlow model
                                                    "image": "tensorflow/serving",
                                                    "ports": [{
                                                        "containerPort": 8500
                                                    }, {
                                                        "containerPort": 8501
                                                    }]
                                                }]
                                            }
                                        }
                                    })

# Service that exposes the TensorFlow Serving deployment
service = k8s.core.v1.Service("tf-serving-service",
                              metadata={
                                  "namespace": namespace.metadata["name"],
                                  "labels": deployment.metadata["labels"]
                              },
                              spec={
                                  "ports": [{
                                      "port": 8501,
                                      "targetPort": 8501
                                  }],
                                  "selector": app_labels
                              })

# ValidateWebhookConfiguration for secure AI serving
# This is a placeholder and should point to your actual validating service or external webhook
validating_webhook_config = k8s.admissionregistration.v1.ValidatingWebhookConfiguration(
    "tf-serving-validation-webhook",
    metadata={"name": "tf-serving-validation-webhook"},
    webhooks=[{
        "name": "validate.tf-serving.tensorflow.org",
        "rules": [{
            "apiGroups": [""],
            "apiVersions": ["v1"],
            "operations": ["CREATE", "UPDATE"],
            "resources": ["services"],
            "scope": "Namespaced"
        }],
        "clientConfig": {
            "service": {
                "name": "webhook-service",
                "namespace": namespace.metadata["name"],
                "path": "/validate"
            }
        },
        "sideEffects": "None",
        "admissionReviewVersions": ["v1"],
    }]
)

# Export the Service name and cluster IP to access the TensorFlow Serving model
pulumi.export('service_name', service.metadata['name'])
pulumi.export('cluster_ip', service.spec['cluster_ip'])
```

### Explanation of the Pulumi Resources:

- The namespace is a way to partition cluster resources between multiple users. It is straightforward and doesn't require complex configuration.

- The deployment resource describes the desired state of the application – what containers should be running and their configurations. The deployment automatically restarts containers when they fail.

- The service resource sets up networking in Kubernetes. It defines a consistent way to access the pods in the deployment, even as they are destroyed or created.

- The `ValidatingWebhookConfiguration` resource creates a webhook that Kubernetes will call before admitting requests. The `webhooks` field specifies the details of how the requests are intercepted and where they are sent to be validated. The `clientConfig` particularly defines how to access the webhook, with `service` pointing to the service responsible for validation within the cluster.

Remember to replace the placeholder values with actual references to your TensorFlow model and validation webhook. The URLs and ports may also need to be changed to match your specific configuration.