1. Continuous Delivery of AI Services with AWS CodeDeploy


    Continuous Delivery is a software development practice where code changes are automatically prepared for a release to production. It's a key at enabling a more rapid and reliable software development lifecycle. AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services such as Amazon EC2, AWS Fargate, AWS Lambda, and your on-premises servers.

    In this guide, we'll use Pulumi to set up a Continuous Delivery pipeline for AI services using AWS CodeDeploy along with other AWS services that work in tandem to automate the deployment process.


    Before you begin, you will need the following:

    1. An AWS account with the proper permissions to create the necessary resources.
    2. Pulumi CLI installed on your machine.
    3. AWS CLI installed and configured with your credentials on your development machine.

    Overview of the Pulumi Program

    Our Pulumi program in Python will perform several steps:

    1. Create an Application: In CodeDeploy, an application is a container that specifies the settings for deploying revisions.

    2. Define a Deployment Group: Deployment groups contain the information about the code, deployment configuration, and the infrastructure on which the AWS CodeDeploy application is to be deployed.

    3. Specify Deployment Config: It specifies the deployment process settings, such as the minimum number of healthy instances that must be available during the deployment process.

    4. Set up IAM roles: IAM roles are required to grant AWS CodeDeploy access to other AWS services.

    5. Create a CodeDeploy Service Role: This role grants AWS CodeDeploy access to perform actions on the outlined resources.

    Now let's start with the Pulumi program to create this setup.

    import pulumi import pulumi_aws as aws # Create an AWS CodeDeploy application app = aws.codedeploy.Application("aiServiceApp", compute_platform="Lambda", # or "Server" or "ECS" depending on your compute platform ) # Define a deployment group with settings for how deployments are to be carried out deployment_group = aws.codedeploy.DeploymentGroup("aiServiceDeploymentGroup", app_name=app.name, service_role_arn=aws.iam.Role("codedeployRole").arn, # IAM role for CodeDeploy deployment_style={ "deploymentType": "IN_PLACE", # or "BLUE_GREEN" if you are using ECS "deploymentOption": "WITH_TRAFFIC_CONTROL", # Controls how traffic is shifted to your updated Lambda function version }, # Other configurations can include load balancer info, auto-scaling groups, etc. ) # Specify the deployment configuration (example: CodeDeployDefault.LambdaAllAtOnce) # that AWS CodeDeploy will use for deployments deployment_config = aws.codedeploy.DeploymentConfig("aiServiceDeploymentConfig", deployment_config_name="CodeDeployDefault.LambdaAllAtOnce", # Standard predefined configuration for Lambda ) # Create an IAM role and policy that allows AWS CodeDeploy to interact with AWS services codedeploy_role = aws.iam.Role("codedeployRole", assume_role_policy={ "Version": "2012-10-17", "Statement":[{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "codedeploy.amazonaws.com"} }] } ) # Attach policy to the CodeDeploy IAM role with proper permissions codedeploy_policy = aws.iam.RolePolicyAttachment("codedeployPolicyAttachment", role=codedeploy_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSCodeDeployRole" ) # Export the CodeDeploy application name pulumi.export('code_deploy_app_name', app.name) # Export the Deployment Group name pulumi.export('deployment_group_name', deployment_group.id)

    In the Pulumi program above, we:

    • Create a CodeDeploy application designed for a Lambda-based service, but this can also be an ECS or EC2/on-premises based service depending on your requirements.
    • Define a deployment group that references the application and a role that allows AWS CodeDeploy to access other specified AWS services.
    • Set deployment configuration to define the deployment rules.
    • Set up an IAM role (codedeployRole) that AWS CodeDeploy will assume when deploying the application.
    • Attach a predefined AWS policy to the IAM role which grants the necessary permissions for CodeDeploy operations.

    To deploy this program:

    • Navigate to the directory where this file is located.
    • Run pulumi up to preview and deploy the changes.

    Upon successful deployment, Pulumi will output the names of the CodeDeploy application and the deployment group. These IDs can be used for further automation or integration into your CI/CD pipeline.

    Remember to modify the application type (compute_platform), deployment group settings, and the IAM role's policy based on your specific use case and security requirements. Always ensure that you grant least privilege access in your IAM roles and policies.