1. Scheduling Periodic Model Retraining with AWS Step Functions


    To schedule periodic model retraining with AWS Step Functions, we'll leverage several AWS services in conjunction with Pulumi. Here's the high-level strategy to achieve this:

    1. AWS SageMaker: This service will be used for model training. SageMaker provides various machine learning models that you can use to train and deploy your algorithms.

    2. AWS Step Functions: This is a serverless orchestration service that lets you combine AWS Lambda functions and other AWS services to build business-critical applications. We'll use Step Functions to coordinate the model retraining workflow.

    3. AWS CloudWatch Events (now called Amazon EventBridge): We use this to trigger the Step Functions state machine on a schedule (i.e., to run training jobs periodically).

    4. AWS Lambda: Lambda functions can be invoked at different steps of the workflow to perform tasks such as data preprocessing, model evaluation, or notifications.

    Pulumi Program to Schedule Periodic Model Retraining

    Below is a Pulumi Python program that creates the necessary resources to schedule periodic model retraining with AWS Step Functions. The program will:

    • Define an AWS Step Functions state machine with tasks to train a model using SageMaker.
    • Set up a CloudWatch Event Rule to trigger this state machine on a schedule (e.g., daily).
    • Bind the CloudWatch Event Rule to the Step Functions state machine as a target.
    import pulumi import pulumi_aws as aws # Create an IAM role for the Step Functions state machine step_functions_role = aws.iam.Role("stepFunctionsRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "states.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""" ) # Attach policies to the IAM role aws.iam.RolePolicyAttachment("lambda-attach", policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaRole", role=step_functions_role.name) aws.iam.RolePolicyAttachment("sagemaker-attach", policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess", role=step_functions_role.name) # Define the state machine state_machine_definition = """{ "Comment": "A simple AWS Step Functions state machine that triggers an AWS SageMaker training job.", "StartAt": "SageMakerTrainingJob", "States": { "SageMakerTrainingJob": { "Type": "Task", "Resource": "arn:aws:states:::sagemaker:createTrainingJob.sync", "Parameters": { "TrainingJobName": "MyTrainingJob", "AlgorithmSpecification": { "TrainingInputMode": "File", "AlgorithmName": "MyAlgorithm" }, "RoleArn": "${role_arn}", "InputDataConfig": [ { "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://my-bucket/my-training-data/", "S3DataDistributionType": "FullyReplicated" } } } ], // Additional configuration omitted for brevity }, "End": true } } }""".replace("${role_arn}", step_functions_role.arn) # Create the state machine state_machine = aws.sfn.StateMachine("stateMachine", definition=state_machine_definition, role_arn=step_functions_role.arn) # Set up CloudWatch Event Rule to trigger on a schedule schedule_rule = aws.cloudwatch.EventRule("scheduleRule", schedule_expression="cron(0 0 * * ? *)") # Set to trigger daily at midnight UTC # Target the state machine with the event rule schedule_target = aws.cloudwatch.EventTarget("scheduleTarget", rule=schedule_rule.name, arn=state_machine.id) # Use a Lambda permission to allow the invocation of the function from CloudWatch lambda_permission = aws.lambda_.Permission("lambdaPermission", action="lambda:InvokeFunction", function=state_machine.arn, principal="events.amazonaws.com", source_arn=schedule_rule.arn) # Expose the state machine ARN as a stack output pulumi.export('state_machine_arn', state_machine.arn)

    This program performs the following steps:

    1. IAM Role & Policies: It creates an IAM role that the Step Functions state machine will assume when executing. This role has policies attached to it that grant permissions for the necessary AWS services.

    2. State Machine: The program defines a Step Functions state machine with a single task that initiates a SageMaker training job. The job configuration, including the algorithm specification and the training data location, are specified in the task parameters.

    3. Event Rule: An EventBridge rule is defined to trigger on a specified schedule. This example uses a CRON expression to run the state machine daily at midnight UTC.

    4. Event Target: The state machine is set as the target for the scheduled event.

    5. Permissions: To allow the Event Rule to trigger the Step Functions state machine, the program sets a permission for the CloudWatch Events service to invoke the state machine.

    6. Outputs: Finally, we export the ARN of the Step Functions state machine so that it can be referenced outside the Pulumi program.

    After deploying this Pulumi program, you'll have a scheduled job that triggers your model training workflow on a daily basis. You can customize the schedule expression, state machine definition, and resource names as needed for your specific use case.