1. Event Sourcing for AI Model Training Workflows


    Event sourcing is an architectural pattern where state changes are logged as a sequence of events, which can be replayed to reach the current state of an application. In the context of AI model training workflows, this can help in keeping track of the data and changes to the model over time, allowing for auditing, debugging, and reproducing results.

    To create an event-sourced AI model training workflow, a common approach involves utilizing cloud services for event handling, data storage, and compute resources to process the events and train the model. In the AWS cloud, services like Amazon EventBridge for event handling, Amazon S3 for storage, Amazon SageMaker for model training, and AWS Lambda for serverless computations can be orchestrated to form an event-driven AI training pipeline.

    Below is a Pulumi program written in Python that illustrates how you can set up an event-sourced AI model training workflow on AWS. This program includes:

    • An S3 bucket for storing data.
    • A SageMaker Notebook instance to create and train your AI model.
    • An EventBridge event bus to receive and route events.
    • An AWS Lambda function triggered by incoming events for processing.
    • Permissions and roles required for the services to interact securely.

    This program is an example and you should modify the resources in alignment with your specific workflow requirements.

    import json import pulumi import pulumi_aws as aws # Create an S3 bucket to store the training data and model artifacts data_bucket = aws.s3.Bucket("dataBucket") # Create an AWS IAM role for Amazon SageMaker to access AWS services sagemaker_role = aws.iam.Role("sagemakerRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole" }] }) ) # Attach policies to the SageMaker role that allows access to S3 sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemakerAttachment", role=sagemaker_role, policy_arn=aws.iam.ManagedPolicy.AMAZON_S3_FULL_ACCESS.value ) # Create a SageMaker Notebook Instance for building and training the AI models notebook_instance = aws.sagemaker.NotebookInstance("aiModelNotebookInstance", role_arn=sagemaker_role.arn, tags={ "Name": "AIModelTrainingNotebook" } ) # Create an EventBridge bus for capturing model training events event_bus = aws.cloudwatch.EventBus("modelTrainingEventBus") # IAM role for the Lambda function that will process event data lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": {"Service": "lambda.amazonaws.com"}, "Action": "sts:AssumeRole" }] }) ) # Attach the basic execution role policy to the Lambda role lambda_basic_execution = aws.iam.RolePolicyAttachment("lambdaBasicExecution", role=lambda_role, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE.value ) # Create a Lambda function that will be invoked by EventBridge and process the training events event_processor_lambda = aws.lambda_.Function("eventProcessorLambda", role=lambda_role.arn, handler="index.handler", runtime=aws.lambda_.Runtime.PYTHON3_8.value, code=pulumi.FileArchive("./lambda") # The 'FileArchive' points to a zipped directory of the Lambda's code. # You would replace './lambda' with the path to your Lambda function's code. ) # Give EventBridge permission to invoke the Lambda function invoke_permission = aws.lambda_.Permission("invokePermission", action="lambda:InvokeFunction", function=event_processor_lambda.name, principal="events.amazonaws.com", source_arn=event_bus.arn ) # Define the rule for event triggering event_rule = aws.cloudwatch.EventRule("triggerOnModelTraining", event_bus_name=event_bus.name, event_pattern=json.dumps({ "source": ["my.sagemaker.training.jobs"], # Replace with the appropriate event source or pattern for your use case }) ) # Associate the Lambda function to the event rule, so it gets invoked when an event is received. event_target = aws.cloudwatch.EventTarget("lambdaEventTarget", arn=event_processor_lambda.arn, rule=event_rule.name, event_bus_name=event_bus.name ) # Export the S3 bucket name and EventBridge bus ARN for later reference pulumi.export("data_bucket_name", data_bucket.id) pulumi.export("event_bus_arn", event_bus.arn) pulumi.export("notebook_instance_name", notebook_instance.id)

    In this example:

    • We begin by creating an S3 bucket to store our input data and model artifacts.
    • We establish an IAM Role for Amazon SageMaker with the appropriate permissions.
    • We set up a SageMaker Notebook instance to run Jupyter notebooks, where we can write code for our machine learning models.
    • We create an event bus in EventBridge, which will route the events according to rules we define.
    • We create an AWS Lambda function to act when events occur and set the permissions for the event bus to invoke this function.
    • We create an EventBridge rule to specify the conditions under which our Lambda function will trigger, using a simplified event pattern.

    Remember, for deploying this Pulumi program, you need to have configured AWS credentials and the Pulumi CLI. The FileArchive("./lambda") line assumes you have a directory named lambda where your AWS Lambda source code and dependencies are stored, which should be zipped for deployment. Modify the event pattern and other resource properties as necessary for your specific requirements.