Spinnaker Managed Canary Releases for AI Applications

Question

Pulumi · Accepted Answer

To set up a Spinnaker managed canary release for AI applications, we typically require an environment where Spinnaker is installed and configured to interface with your cloud provider, and a canary release strategy in place for your AI application.

However, Spinnaker setup and configuration are beyond the scope of a Pulumi program. Pulumi focuses on declaring cloud resources in code, and while it can certainly describe the infrastructure that would host a Spinnaker instance (such as VMs, network, and security configurations), the detailed installation of Spinnaker and canary release setup is a process that would be handled either manually or using configuration management tools.

For the purpose of this learning exercise, I'll demonstrate how to use Pulumi to create a basic AWS infrastructure that could hypothetically support a deployment tool like Spinnaker.

Let me guide you through the process:

1. We will create an infrastructure for an AI application with core components, such as a compute instance for hosting the app and an auto-scaling setup for managing the deployment capacity based on demand.
2. For canary releasing, in a real-world scenario, you would configure Spinnaker to deploy new versions of your application to a subset of your fleet and then evaluate the performance of the new version compared to the old before a full rollout.

Here's a Pulumi Python program that creates an Auto Scaling Group (ASG) in AWS for deploying an AI application. The ASG could scale based on metrics, which would be part of a canary release strategy. I won't be adding Spinnaker-specific code here because that requires the Spinnaker infrastructure with specific pipelines now managed via Pulumi.

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role for the EC2 instance profile
instance_role = aws.iam.Role("aiAppInstanceRole", 
    assume_role_policy={
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "ec2.amazonaws.com",
            },
        }],
    })

# Attach a policy to the IAM role to grant the necessary permissions
policy_attachment = aws.iam.RolePolicyAttachment("aiAppInstanceRoleAttachment",
    role=instance_role.name,
    policy_arn=aws.iam.ManagedPolicy.AMAZON_EC2_CONTAINER_SERVICE_FOR_EC2_ROLE.value)

# Create an instance profile which will be used by the EC2 instances in the ASG
instance_profile = aws.iam.InstanceProfile("aiAppInstanceProfile", role=instance_role.name)

# Define the desired AMI for the EC2 instances (This should be replaced with your AI application AMI)
ami = aws.ec2.get_ami(most_recent=True, owners=["amazon"], filters=[{
    "name": "name",
    "values": ["amzn2-ami-hvm-*-x86_64-ebs"],
}])

# Launch configuration for the ASG instances
launch_configuration = aws.ec2.LaunchConfiguration("aiAppLaunchConfiguration",
    image_id=ami.id,
    instance_type="t2.micro",
    iam_instance_profile=instance_profile.name,
    # Add user_data script to pull and run your AI application or any initialization
)

# Create an Auto Scaling Group (ASG) for the AI application
auto_scaling_group = aws.autoscaling.Group("aiAppAutoScalingGroup",
    launch_configuration=launch_configuration.id,
    min_size=2,
    max_size=5,
    desired_capacity=2,
    vpc_zone_identifiers=[
        # List of subnet IDs where instances should be created
    ],
    # Define scaling policies and set up CloudWatch metrics as triggers, adjust as necessary
)

# Export the name of the Auto Scaling Group
pulumi.export('auto_scaling_group_name', auto_scaling_group.name)
```

In this program, we define an IAM role and instance profile for our instances, find the most recent Amazon Linux AMI, set up a launch configuration, and create the ASG. This is a foundational setup and you'd need to configure scaling policies based on metrics relevant to your AI application's performance, perhaps informed by machine learning models' accuracy or throughput.

For canary releasing with a system like Spinnaker, you would have additional configuration steps, including setting up Spinnaker pipelines that specify the percentage of traffic to route to your canary instances and the success criteria for promoting the canary to full production.

This Pulumi script creates infrastructure where a Spinnaker server and additional tooling could be installed to manage the deployment lifecycle of your AI application. Remember, the actual installation and configuration of Spinnaker itself is a separate process.