Scheduling Periodic Model Retraining with AWS Step Functions

Question

Pulumi · Accepted Answer

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.

```python
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.