1. Managing Distributed Inferencing Jobs with AWS Step Functions


    AWS Step Functions is a serverless orchestration service that lets you combine AWS Lambda functions and other AWS services to build business-critical applications. Through Step Functions, you can design and execute workflows that stitch together services such as AWS Compute, Database, Analytics, and Machine Learning.

    In the context of managing distributed inferencing jobs, you'll use AWS Step Functions to coordinate multiple AWS services that are involved in the inferencing pipeline. For instance, your data might go through a sequence of steps which include preprocessing with AWS Lambda, inferencing with Amazon SageMaker, and persisting results in Amazon S3.

    A typical distributed inferencing job may involve these steps:

    1. Data Preparation: A Lambda function could be used to preprocess the data and make it ready for inferencing.
    2. Inferencing: The prepared data is passed on to an Amazon SageMaker endpoint where the machine learning model performs the inferencing.
    3. Result Storage: The results of the inferencing job are then pushed to Amazon S3 for storage and further analysis.

    We will create a Pulumi program which sets up a basic AWS Step Functions state machine that integrates with AWS Lambda and Amazon SageMaker to manage inferencing jobs. To keep things simple, we'll not include actual machine learning models or Lambda function code, focusing primarily on infrastructure setup.

    For this, the necessary AWS services and the corresponding Pulumi classes from the Pulumi AWS SDK are:

    • aws_native.stepfunctions.StateMachine: Represents a Step Functions state machine. We'll define the workflow here.
    • aws_lambda.Function: Represents an AWS Lambda function. This could be our data preprocessing step.
    • aws_sagemaker.Model: Represents an Amazon SageMaker model. This will be used for inferencing.
    • aws_sagemaker.EndpointConfig and aws_sagemaker.Endpoint: These resources allow deploying the model for inferencing.
    • aws.s3.Bucket: Represents an Amazon S3 bucket where inferencing results will be stored.

    Below is a detailed Python Pulumi program that sets up the necessary infrastructure for managing distributed inferencing jobs with AWS Step Functions.

    import pulumi import pulumi_aws as aws import pulumi_aws_native as aws_native import json # Define an IAM role which will be used by Step Functions, Lambda, and Amazon SageMaker role = aws.iam.Role('InferencingRole', assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": ["lambda.amazonaws.com", "sagemaker.amazonaws.com", "states.amazonaws.com"] } }] })) # Attach necessary policies to the role policies = ['arn:aws:iam::aws:policy/AmazonS3FullAccess', 'arn:aws:iam::aws:policy/AWSLambdaFullAccess', 'arn:aws:iam::aws:policy/AmazonSageMakerFullAccess'] for policy in policies: aws.iam.RolePolicyAttachment(policy, role=role.name, policy_arn=policy) # Create an S3 bucket to store data and inferencing results s3_bucket = aws.s3.Bucket('InferencingBucket') # Create a Lambda function for data preprocessing preprocess_lambda = aws.lambda_.Function('PreprocessLambda', runtime=aws.lambda_.Runtime.Python3d8, role=role.arn, handler='index.handler', code=pulumi.FileArchive('./preprocess-lambda.zip')) # Define a SageMaker model model = aws.sagemaker.Model('InferencingModel', role_arn=role.arn, primary_container=aws.sagemaker.ModelPrimaryContainerArgs( image='your-model-image', # Specify your model image model_data_url='s3://your-model-artifacts-path/model.tar.gz', # Specify the S3 path to your model artifacts )) # Define a SageMaker endpoint configuration endpoint_config = aws.sagemaker.EndpointConfig('InferencingEndpointConfig', production_variants=[ aws.sagemaker.EndpointConfigProductionVariantArgs( instance_type='ml.m5.large', # Specify the instance type model_name=model.name, variant_name='AllTraffic', ) ]) # Create a SageMaker endpoint based on the configuration endpoint = aws.sagemaker.Endpoint('InferencingEndpoint', endpoint_config_name=endpoint_config.name) # Define the state machine that orchestrates our Lambda function and SageMaker endpoint state_machine_definition = json.dumps({ "Comment": "A Step Function State Machine for managing distributed inferencing jobs", "StartAt": "PreprocessData", "States": { "PreprocessData": { "Type": "Task", "Resource": f"arn:aws:lambda:{preprocess_lambda._opts.region}:{preprocess_lambda._opts.account}:function:{preprocess_lambda.name}", "Next": "InferenceModel" }, "InferenceModel": { "Type": "Task", "Resource": f"arn:aws:states:::sagemaker:invokeEndpoint.sync", "Parameters": { "EndpointName": endpoint.endpoint_name, "Body.$": "$.input" }, "Next": "StoreResults" }, "StoreResults": { "Type": "Task", "Resource": f"arn:aws:lambda:{preprocess_lambda._opts.region}:{preprocess_lambda._opts.account}:function:{preprocess_lambda.name}", "End": True } } }) # Create the State Machine state_machine = aws_native.stepfunctions.StateMachine('InferencingStateMachine', role_arn=role.arn, definition=state_machine_definition) # Export the state machine ARN and the S3 bucket URI pulumi.export('state_machine_arn', state_machine.arn) pulumi.export('s3_bucket_uri', s3_bucket.bucket.apply(lambda name: f's3://{name}'))

    In this program, we define each part of the workflow, including the S3 Bucket for results, the Lambda function for data preprocessing, the SageMaker model for inferencing, and the Step Functions state machine to coordinate the tasks.

    To run this program, replace the placeholders such as 'your-model-image' and 's3://your-model-artifacts-path/model.tar.gz' with your actual model image and artifacts path. You'll also need a .zip archive containing your Lambda function code as 'preprocess-lambda.zip' to upload during Lambda creation.

    Once deployed, this state machine can be invoked with input data, and it will automatically manage the workflow of preprocessing, inferencing, and result storage.