1. Managing Retries in ML Data Collection Workflows


    When designing a machine learning data collection workflow in the cloud, it's important to handle transient errors and intermittent failures gracefully. Retries are critical in ensuring that temporary issues, such as network timeouts or service unavailability, do not disrupt the workflow. Pulumi can help you define infrastructure as code that includes error handling and retry mechanisms appropriate for your cloud provider and services used.

    Below is an example Pulumi Python program that creates an AWS Step Functions state machine. AWS Step Functions allows you to build resilient serverless workflows. Using Step Functions, you can design and run workflows that stitch together services like AWS Lambda (for compute), Amazon S3 (for storage), and more.

    Each state in a Step Functions workflow can have its own retry policy. You can specify the number of retry attempts, the interval between attempts, a backoff rate, and a catch configuration to handle errors that are not resolved by retries.

    We'll create a simplified state machine with a single task state that invokes an AWS Lambda function. We'll define a retry policy for this state that retries up to three times if the Lambda function returns an error. We'll also use the Catch field to transition to a failure handling state if retries exceed.

    import pulumi import pulumi_aws as aws # Create an AWS IAM role for the state machine state_machine_role = aws.iam.Role("stateMachineRole", assume_role_policy={ # IAM policy that allows Step Functions to assume this role "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": "states.amazonaws.com" }, "Action": "sts:AssumeRole" }] } ) # Attach a policy to the role that grants necessary permissions for the Lambda invocation aws.iam.RolePolicyAttachment("stateMachineRolePolicy", role=state_machine_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_STEP_FUNCTIONS_FULL_ACCESS.value ) # Create an AWS Lambda function (We assume the lambda function code is packaged and uploaded to S3) lambda_function = aws.lambda_.Function("myLambdaFunction", handler="index.handler", role=state_machine_role.arn, # Using the same role created for the state machine runtime="python3.8", code=pulumi.asset.FileArchive("lambda.zip"), # A local path to the lambda function package ) # Define the state machine with retry and catch properties state_machine_def = { "Comment": "A Hello World example of the Amazon States Language using a Pass state", "StartAt": "HelloWorld", "States": { "HelloWorld": { "Type": "Task", "Resource": lambda_function.arn, "End": True, "Retry": [{ "ErrorEquals": ["Lambda.ServiceException", "Lambda.AWSLambdaException", "Lambda.SdkClientException"], "IntervalSeconds": 2, "MaxAttempts": 3, "BackoffRate": 2.0 }], "Catch": [{ "ErrorEquals": ["States.ALL"], "Next": "FailState" }] }, "FailState": { "Type": "Fail", "Error": "DefaultError", "Cause": "The Lambda function failed after 3 retry attempts." } } } # Create the state machine state_machine = aws.sfn.StateMachine("myStateMachine", role_arn=state_machine_role.arn, definition=pulumi.Output.from_input(state_machine_def).apply(pulumi.json.encode) ) # Export the state machine's ARN so you can investigate its execution in the AWS Management Console pulumi.export('state_machine_arn', state_machine.id)

    In this program, we set up a Lambda function and a State Machine in AWS Step Functions. The task in the State Machine invokes the Lambda function and includes retry logic.

    • If the invoked Lambda function results in an error, Step Functions will wait for 2 seconds (IntervalSeconds), then retry up to 3 times (MaxAttempts), doubling the interval each time (BackoffRate).
    • If all retry attempts fail, Step Functions will enter the FailState, and execution of the state machine will end.

    You can monitor state machine executions and dig into logs for detailed troubleshooting through the AWS Management Console.

    Remember to replace lambda.zip with the actual path of your Lambda function code and define your Lambda function handler as necessary.

    By managing retries in the data collection workflows using Pulumi, we can ensure the robustness and reliability of the system, even when transient errors occur.