Automated Canary Deployments for AI Applications on Kubernetes with Linkerd

Question

Pulumi · Accepted Answer

Automated canary deployments are a technique used to reduce the risk of introducing a new software version into production by slowly rolling out the change to a small subset of users before rolling it out to the entire infrastructure. It involves deploying two versions of an application, the stable and the new canary version, routing a small amount of traffic to the canary, and then gradually increasing it as confidence in the new version grows.

To implement automated canary deployments for AI applications on Kubernetes, you would typically follow these steps:

1. **Set up a Kubernetes cluster**: You'll need a running Kubernetes cluster.

2. **Install and configure Linkerd**: Linkerd is a service mesh that adds observability, reliability, and security to Kubernetes applications without requiring any changes to the application's code. You will use Linkerd to manage the traffic between the stable and the canary versions of your application.

3. **Deploy your AI application**: The application must be packaged into Docker containers and deployed to Kubernetes.

4. **Create routing rules for canary deployments**: You will create rules that will route a small percentage of traffic to your canary deployment.

5. **Monitor metrics and automate rollouts**: Linkerd provides capabilities to monitor the performance of canary deployments. You can use these metrics to automate the decision to promote the canary to full production or roll it back.

Let's write a Pulumi program in Python to automate the setup of a Kubernetes cluster with Linkerd and a mock AI application ready for canary deployments. This program assumes you have Docker images for your AI application, context for Kubernetes, and necessary permissions already set up.

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

# Initialize a Pulumi Kubernetes provider using the current context from your kubeconfig file.
# This requires that you have already configured kubectl to point to your Kubernetes cluster.
kubeconfig = pulumi.Config('kubernetes').require('kubeconfig')
k8s_provider = k8s.Provider('k8s-provider', kubeconfig=kubeconfig)

# Install Linkerd into your Kubernetes cluster using the Helm chart.
# This will install the Linkerd control plane, which will be used to manage
# service-to-service communication, including the canary deployments.
linkerd_chart = Chart(
    'linkerd',
    ChartOpts(
        chart='linkerd2',
        version='stable-2.9.4',
        fetch_opts=FetchOpts(
            repo='https://helm.linkerd.io/stable'
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# This dummy application simulates your AI application.
# The application should be packaged into a Docker image and uploaded to your Docker registry.
deployment = k8s.apps.v1.Deployment(
    'ai-app-deployment',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name='ai-app',
    ),
    spec=k8s.apps.v1.DeploymentSpecArgs(
        replicas=2,
        selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={'app': 'ai-app'},
        ),
        template=k8s.core.v1.PodTemplateSpecArgs(
            metadata=k8s.meta.v1.ObjectMetaArgs(
                labels={'app': 'ai-app'},
            ),
            spec=k8s.core.v1.PodSpecArgs(
                containers=[k8s.core.v1.ContainerArgs(
                    name='ai-app',
                    image='your-docker/ai-app:stable',  # Replace with your actual Docker image.
                )],
            ),
        ),
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# This Kubernetes service will act as the load balancer for our AI application.
service = k8s.core.v1.Service(
    'ai-app-service',
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name='ai-app-service',
    ),
    spec=k8s.core.v1.ServiceSpecArgs(
        selector={'app': 'ai-app'},
        ports=[k8s.core.v1.ServicePortArgs(
            port=80,
            target_port=8080,
        )],
        type='LoadBalancer',
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider)
)

# Output the Linkerd status and AI application service endpoint.
pulumi.export('linkerd_status', linkerd_chart.status)
pulumi.export('ai_app_service_endpoint', service.status.load_balancer.ingress[0].ip)
```

In the above program, you installed the Linkerd service mesh using Helm, one of the most popular package managers for Kubernetes. You then defined a mock deployment and service for your AI application.

**Note**: Replace `'your-docker/ai-app:stable'` with the actual Docker image for your application.

To implement canary deployments and traffic shifting, you'll need an additional layer on top of this, typically using Linkerd's Service Profile CRD and Traffic Split CRD. You can create these resources programmatically in Pulumi as well, but the specifics will depend on your application's architecture and the metrics you want to use for canary analysis.

After deploying your AI application and its service, you'd continue by defining `ServiceProfile` and `TrafficSplit` resources. The `ServiceProfile` would define the routes for your service, and the `TrafficSplit` resource will control the percentage of traffic that is directed to each of those versions.

This setup would allow you to observe the canary's behavior under live traffic, and use Linkerd's metrics to decide if the canary is healthy enough to roll out to all users. You can extend the Pulumi program to include those resources and integrate monitoring and automation tools as required.