1. Model Inference Endpoint Response Customization


    Creating a model inference endpoint involves deploying a machine learning model to a managed cloud service, which provides an API to perform online predictions. Customizing the response of a model inference endpoint might entail modifying the output of the prediction request to match a specific format or include additional metadata.

    In this example, I'll demonstrate how to deploy a model inference endpoint in AWS using Amazon SageMaker, a fully managed service that provides the ability to build, train, and deploy machine learning models quickly. We'll define an endpoint configuration with a production variant and customize the inference response by leveraging AWS Lambda to transform the results.

    To accomplish this, we will use several Pulumi AWS resources, including the aws.sagemaker.EndpointConfiguration and the aws.sagemaker.Endpoint. We will also define an AWS Lambda function using the aws.lambda_.Function resource to customize the SageMaker endpoint responses.

    We'll start by creating a SageMaker model, defining an endpoint configuration with a custom output path for our inference logs, and deploying an endpoint to serve predictions. We'll then create a Lambda function and attach it to our SageMaker endpoint for response transformation.

    Here's what each part of our Pulumi program does:

    1. SageMaker Model: Defines the ML model on AWS SageMaker.
    2. Endpoint Configuration: Specifies how SageMaker deploys the model, including the instance types to be used and the output path for data capture.
    3. SageMaker Endpoint: The HTTP endpoint that provides inference from the deployed model.
    4. Lambda Function: It is responsible for customizing the SageMaker endpoint responses.
    5. Lambda Permission: Grants SageMaker service to invoke the Lambda function.
    6. Model Inference Data Capture: Optionally, configure data capture for requests and responses on the SageMaker endpoint.

    Let's implement this in code.

    import pulumi import pulumi_aws as aws # Define a SageMaker model resource sagemaker_model = aws.sagemaker.Model("exampleModel", execution_role_arn="arn:aws:iam::123456789012:role/SageMakerRole", # Replace with the ARN of your IAM role primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/your-container-image:latest", # Replace with your ECR image }) # Define an endpoint configuration endpoint_config = aws.sagemaker.EndpointConfiguration("exampleEndpointConfig", production_variants=[{ "variant_name": "variant-1", "modelName": sagemaker_model.name, "initial_instance_count": 1, "instance_type": "ml.m4.xlarge", }], data_capture_config={ "enableCapture": True, "initialSamplingPercentage": 100, "destinationS3Uri": "s3://your-bucket/data_capture", # Replace with your S3 path }) # Deploy an endpoint based on the configured model and endpoint configuration sagemaker_endpoint = aws.sagemaker.Endpoint("exampleEndpoint", endpoint_config_name=endpoint_config.name) # Define a Lambda function for response transformation response_lambda = aws.lambda_.Function("exampleResponseLambda", role="arn:aws:iam::123456789012:role/my-lambda-role", # Replace with the ARN of your IAM role for Lambda runtime="python3.8", handler="lambda_function.handler", # Replace with the handler name in your Lambda code code=pulumi.FileArchive("./lambda.zip")) # Replace with the path to your Lambda function code package # Grant SageMaker permission to invoke the Lambda function sagemaker_invoke_permission = aws.lambda_.Permission("exampleInvokePermission", action="lambda:InvokeFunction", function=response_lambda.name, principal="sagemaker.amazonaws.com", source_arn=sagemaker_endpoint.arn) # Optionally, capture inference request and response data s3_capture_option = aws.s3.Bucket("s3CaptureBucket", force_destroy=True, # Set to false if you do not want to force deletion of bucket bucket="your-capture-bucket-name") # Replace with your desired S3 bucket name # Export the endpoint URL for direct use pulumi.export("endpoint_url", sagemaker_endpoint.endpoint_url)

    In this Pulumi program:

    • We start by defining a SageMaker Model with a container image from Amazon ECR that represents our machine learning model.
    • Then we create an Endpoint Configuration, specifying the SageMaker model and deployment settings such as instance type and instance count for serving predictions.
    • The SageMaker Endpoint is the actual HTTP endpoint created using the defined model and endpoint configuration.
    • A Lambda Function represents our custom code, which will transform the response from SageMaker.
    • We give SageMaker the necessary Permission to invoke the Lambda function.
    • Optionally, if data capture is enabled, all prediction requests and responses will be stored in the specified S3 capture bucket.
    • Finally, we export the endpoint URL for direct use.

    To deploy this program, save it as a .py file, ensuring you have configured Pulumi with the required AWS credentials and run pulumi up in the command line within the same directory as your program file. Remember to replace placeholders like ARN of your IAM roles, ECR image, and S3 bucket names with your actual AWS resource identifiers.

    Keep in mind that you would need actual machine learning model code packaged in a container image hosted in Amazon ECR and an IAM role with the necessary permissions for SageMaker to make the above infrastructure functional.