1. Scheduling Sequential Training Jobs Using AWS Step Functions


    AWS Step Functions is a serverless orchestration service that lets you combine AWS services and third-party applications to build complex workflows. When scheduling sequential training jobs, Step Functions is an excellent choice as it facilitates error handling, retries, parallel tasks, and workflow status tracking, among other features. One of the many uses of Step Functions is to manage machine learning workflows, which can include sequential training jobs.

    Here is how you would create a Step Functions State Machine (a workflow composed of states) to execute sequential training jobs with AWS SageMaker using Pulumi and Python:

    1. Define each job as a state within the Step Functions State Machine.
    2. Chain these states to ensure they execute sequentially.
    3. Use SageMaker's managed machine learning training capabilities to run each job.
    4. Define the workflow in the State Machine Definition Language (Amazon States Language) and pass it to the State Machine resource.

    Below is a Pulumi program that demonstrates how to schedule sequential training jobs using AWS Step Functions and AWS SageMaker. Make sure you have AWS credentials configured for Pulumi before running this program.

    import pulumi import pulumi_aws as aws # Define the IAM role that AWS Step Functions will use to execute the training jobs # For simplicity, we'll attach the AWS managed `AmazonSageMakerFullAccess` policy. # In production, you'd want to scope this down to only the permissions needed. sfn_role = aws.iam.Role("sfn_role", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "states.amazonaws.com"}, "Action": "sts:AssumeRole" } ] }""") policy_attachment = aws.iam.RolePolicyAttachment("sfn_role_attachment", role=sfn_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaRole") # Define the State Machine with sequential states for the training jobs. # The specific details of the training jobs (like the training algorithm and model data) # will depend on your particular use case and are represented here as placeholders. state_machine = aws.sfn.StateMachine("training_state_machine", role_arn=sfn_role.arn, definition=pulumi.Output.all().apply(lambda _: """{ "Comment": "A state machine that runs training jobs sequentially", "StartAt": "TrainModelStep1", "States": { "TrainModelStep1": { "Type": "Task", "Resource": "arn:aws:states:::sagemaker:createTrainingJob.sync", "Parameters": { "TrainingJobName": "training-job-step-1", "AlgorithmSpecification": { "TrainingInputMode": "File", "AlgorithmName": "placeholder-algorithm" }, "InputDataConfig": [ { "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://bucket-name/path/to/training/data" } } } ], "OutputDataConfig": { "S3OutputPath": "s3://bucket-name/path/to/output" }, "ResourceConfig": { "InstanceType": "ml.m4.xlarge", "InstanceCount": 1, "VolumeSizeInGB": 10 }, "StoppingCondition": { "MaxRuntimeInSeconds": 3600 }, "RoleArn": "${sfn_role.arn}" }, "Next": "TrainModelStep2" }, "TrainModelStep2": { "Type": "Task", "Resource": "arn:aws:states:::sagemaker:createTrainingJob.sync", "Parameters": { "TrainingJobName": "training-job-step-2", // ... parameters for the second job "RoleArn": "${sfn_role.arn}" }, "End": true } } }""")) # Export the ARN of the State Machine to be used in the AWS console or AWS CLI pulumi.export('state_machine_arn', state_machine.id)

    In this program:

    • We create an IAM role that AWS Step Functions will assume when executing the training jobs. We attach a policy giving it the necessary permissions.
    • A State Machine is defined where each state corresponds to a SageMaker training job. The "Next" field indicates the sequential execution flow.
    • We've also included placeholders for algorithm specification and SageMaker job parameters. You need to replace these with details that match your specific SageMaker training job configurations.
    • Finally, we export the ARN of the state machine, which you can use to start executions directly in the AWS Console or programmatically using the AWS CLI or SDKs.

    Please adjust the placeholders and configuration to match your training job's requirements. Ensure you have the necessary permissions and resources available in your AWS environment.