Canary Deployments of AI Services on Kubernetes

Question

Pulumi · Accepted Answer

Canary deployment is a pattern that allows you to release a new version of a service to a subset of users before rolling it out to the entire user base. In the context of Kubernetes and AI services, it involves running two versions of an AI application simultaneously: the stable version and the new, canary version.

To achieve this, you'll typically use Kubernetes Deployments, where each version of the AI service is handled by its own Deployment. You also need Services to route traffic to the pods managed by these Deployments and a way to manage the traffic distribution, often via an Ingress controller or a service mesh like Istio, which can direct a specific percentage of traffic to your canary deployment.

Let's walk through a basic example of how you could set up a canary deployment for an AI service on Kubernetes using Pulumi and Python:

1. **Namespace**: A dedicated namespace to logically isolate our AI services.
2. **Deployments**: Two deployments, one for the stable version of the AI service and another for the canary version.
3. **Services**: Define services to expose the Deployments. Each service will select pods based on labels that match the version.
4. **Ingress or Service Mesh (optional)**: Define routing rules to split traffic between the stable and canary services. This part of the implementation will depend on the specific cluster configuration and is not covered in this example.

Below is the Pulumi program that sets up the foundational parts of a canary deployment. Please note that actual traffic routing might require additional configuration, such as setting up Istio or a custom Ingress controller, which is beyond the scope of this simple example.

```python
import pulumi
import pulumi_kubernetes as k8s

# Step 1: Create a namespace for the AI services
ns = k8s.core.v1.Namespace("ai-services-ns",
    metadata={"name": "ai-services"})

# Step 2: Create the stable version of your AI service
app_labels = {"app": "ai-service", "version": "stable"}
stable_deployment = k8s.apps.v1.Deployment("ai-service-stable-deployment",
    metadata={
        "namespace": ns.metadata["name"],
        "name": "ai-service-stable",
    },
    spec={
        "selector": {"matchLabels": app_labels},
        "replicas": 1,
        "template": {
            "metadata": {"labels": app_labels},
            "spec": {
                "containers": [
                    {
                        "name": "ai-service",
                        "image": "your-ai-service-stable:latest",
                        # Define resource requirements, ports, env vars etc. here
                    },
                ],
            },
        },
    })

# Step 3: Create the canary version of your AI service
canary_labels = {"app": "ai-service", "version": "canary"}
canary_deployment = k8s.apps.v1.Deployment("ai-service-canary-deployment",
    metadata={
        "namespace": ns.metadata["name"],
        "name": "ai-service-canary",
    },
    spec={
        "selector": {"matchLabels": canary_labels},
        "replicas": 1,  # Start with a smaller number of replicas for canary
        "template": {
            "metadata": {"labels": canary_labels},
            "spec": {
                "containers": [
                    {
                        "name": "ai-service",
                        "image": "your-ai-service-canary:latest",
                        # Define resource requirements, ports, env vars etc. here
                    },
                ],
            },
        },
    })

# Step 4: Expose the stable Deployment with a Service
service_stable = k8s.core.v1.Service("ai-service-stable",
    metadata={
        "namespace": ns.metadata["name"],
        "name": "ai-service-stable",
        "labels": app_labels,
    },
    spec={
        "ports": [{"port": 80, "targetPort": 8080}],
        "selector": app_labels,
        "type": "LoadBalancer",
    })

# Step 5: Optionally, expose the canary Deployment with a Service (could be internally if using a service mesh)
service_canary = k8s.core.v1.Service("ai-service-canary",
    metadata={
        "namespace": ns.metadata["name"],
        "name": "ai-service-canary",
        "labels": canary_labels,
    },
    spec={
        "ports": [{"port": 80, "targetPort": 8080}],
        "selector": canary_labels,
        "type": "LoadBalancer",
    })

# Export the stable and canary service URLs
pulumi.export("stable_service_url", service_stable.status["load_balancer"]["ingress"][0]["ip"])
pulumi.export("canary_service_url", service_canary.status["load_balancer"]["ingress"][0]["ip"])
```

In this example, you see that we create a namespace first to house our resources. Then we make two Deployment definitions: one for the stable version (tagged with a `stable` label) and the other for the canary version (tagged with a `canary` label). Each Deployment has an associated Service that exposes it. In a real-world scenario, you would also include services that manage traffic distribution, which are not covered here.

This gives you the basic setup, and you would need to adjust how you deploy and route traffic based on the specifics of your AI services and desired traffic-splitting strategy. Proper implementation of a canary release strategy might also involve integration with CI/CD systems for automated rollouts and monitoring to assess the canary's performance before promoting it to stable.