1. Rolling Update Strategy for AI Applications Using AWS CodeDeploy


    Rolling updates are a deployment strategy that allows you to update instances in an Auto Scaling group with a new launch configuration in a predictable and controlled manner. It gradually replaces instances running the older version with the new one while maintaining the application's availability.

    AWS CodeDeploy is a service that automates software deployments to a variety of compute services, including Amazon EC2, AWS Lambda, and AWS Fargate. CodeDeploy makes it easier for you to rapidly release new features, helps avoid downtime during application deployment, and handles the complexity of updating your applications.

    In this Pulumi Python program, I will show you how to use AWS CodeDeploy to implement a Rolling Update strategy for AI applications. The process includes creating an Application in CodeDeploy, setting up a Deployment Group, defining the Deployment Configuration, and linking it to an Auto Scaling group.

    Here's an overview of the AWS resources you'll be creating with Pulumi:

    • AWS CodeDeploy Application: This logical entity refers to the application you want to deploy. It connects to your GitHub repository or Amazon S3 bucket, where your application code resides.

    • AWS CodeDeploy Deployment Group: This represents a set of individual instances or resources on which the application will be deployed. If you're using Auto Scaling, it would refer to an Auto Scaling group.

    • AWS CodeDeploy Deployment Config: This defines the deployment rules for successfully deploying an application. For a rolling update, you can specify settings such as a minimum number of healthy instances during the deployment.

    • Auto Scaling Group: It’s a collection of EC2 instances that are treated as a logical grouping for the purposes of automatic scaling and management.

    In the code below, I'll create a minimal setup for deploying an AI application using a rolling update strategy:

    import pulumi import pulumi_aws as aws # Create a CodeDeploy Application ai_app = aws.codedeploy.Application("aiApp", compute_platform="Server", # Change compute platform if needed ) # Define Deployment Configuration for a Rolling Update deployment_config = aws.codedeploy.DeploymentConfig("rollingUpdateConfig", deployment_config_name="CodeDeployDefault.OneAtATime", # This is an AWS predefined deployment configuration compute_platform="Server", # Change compute platform if needed minimum_healthy_hosts={ "type": "FLEET_PERCENT", "value": 75, # At least 75% of instances must be healthy during deployment }, ) # Create a Deployment Group attached to the Application and AutoScaling Group deployment_group = aws.codedeploy.DeploymentGroup("aiAppDeploymentGroup", app_name=ai_app.name, deployment_config_name=deployment_config.deployment_config_name, service_role_arn="<Your-CodeDeploy-Service-Role-ARN>", # Replace with your service role ARN auto_scaling_groups=["<Your-AutoScaling-GroupName>"], # Replace with your Auto Scaling Group name trigger_configurations=[{ "trigger_events": ["DeploymentFailure"], # Define triggers like deployment failure "trigger_name": "DeploymentFailureTrigger", "trigger_target_arn": "<Your-SNS-Topic-ARN>", # Replace with your SNS topic ARN for notifications }], ) # Output the ID of the Deployment Group for reference pulumi.export("deployment_group_id", deployment_group.id)

    Make sure that you replace <Your-CodeDeploy-Service-Role-ARN>, <Your-AutoScaling-GroupName>, and <Your-SNS-Topic-ARN> with the actual ARN for your CodeDeploy service role, the name of your Auto Scaling group, and the SNS topic ARN you want to use for notifications, respectively.

    The service role ARM can be created according to AWS IAM specifications, allowing the necessary permissions for CodeDeploy to interact with other AWS services.

    This code lays the ground for rolling out updates to your AI application using Pulumi to manage AWS CodeDeploy. It assumes that you have a basic understanding of how AWS CodeDeploy works and that you have already set up your AWS provider credentials. With this setup, you instruct the CodeDeploy service to deploy updates to your application, ensuring that there are always healthy instances serving your application as new versions are rolled out.

    You can trigger deployments using the AWS CLI, the AWS SDKs, or the AWS Management Console, referencing the Application and Deployment Group you've created.

    Remember to test this in a development or staging environment before applying it to production to ensure that your deployment strategy works as expected.