1. API Gateway as a Frontend for SageMaker Endpoints


    To set up API Gateway as a frontend for SageMaker endpoints using Pulumi in Python, you'll need to create an API Gateway and configure it to integrate with your SageMaker endpoint. Below, I'll guide you through a detailed program where we create an API Gateway REST API, set up a resource and method for invocation, and integrate it with a SageMaker endpoint.

    Before we start, make sure you have the following prerequisites in place:

    • An AWS Account
    • Configured Pulumi with AWS Provider
    • An existing SageMaker model and endpoint ready to be used

    First, we import the necessary Pulumi libraries for AWS. Then, we set up the API Gateway REST API. We create a resource and a GET method associated with this resource that will act as the SageMaker endpoint invoker. We will use integration to direct the requests coming into the API Gateway to the SageMaker endpoint.

    After creating the API Gateway, we need to grant permission to the API Gateway to invoke the SageMaker endpoint. We achieve this through the aws.lambda.Permission resource.

    Here's how we put it all together in a Pulumi program in Python:

    import pulumi import pulumi_aws as aws # First, retrieve the SageMaker endpoint name from its resource or state. # For the purposes of this example, we'll assume it is provided as a runtime parameter. sagemaker_endpoint_name = '<your-sagemaker-endpoint-name>' # Create an API Gateway REST API resource. api_gateway = aws.apigateway.RestApi( "apiGateway", name="SageMakerEndpointAPI", description="API Gateway to invoke SageMaker endpoint.", ) # Create a resource within our API Gateway. A resource represents a URL path component. # In this case, we're creating a resource to represent our SageMaker invocation path, # e.g., '/predict'. sagemaker_resource = aws.apigateway.Resource( "sagemakerResource", rest_api=api_gateway.id, parent_id=api_gateway.root_resource_id, # The root path of the API Gateway path_part="predict", # The path where the SageMaker endpoint can be invoked ) # Create a GET method for the '/predict' resource. # This method will capture the incoming GET request for the model predictions. get_method = aws.apigateway.Method( "getMethod", rest_api=api_gateway.id, resource_id=sagemaker_resource.id, http_method="GET", authorization="NONE", # Assuming public access here; configure as needed. ) # Set up the integration between the API Gateway method and the SageMaker endpoint. # For that, we create an Integration resource indicating the SageMaker endpoint URI. # The 'uri' is typically the Invocation URL of the SageMaker endpoint. sagemaker_integration = aws.apigateway.Integration( "sagemakerIntegration", rest_api=api_gateway.id, resource_id=sagemaker_resource.id, http_method=get_method.http_method, type="AWS_PROXY", # Type of integration; AWS_PROXY for full AWS service proxy integration_http_method="POST", # SageMaker endpoints expect POST requests for invocations uri=pulumi.Output.concat( "arn:aws:apigateway:", pulumi_aws.get_region().name, ":sagemaker:path/endpoints/", sagemaker_endpoint_name, "/invocations" ), ) # Deploy the API Gateway to make it accessible. deployment = aws.apigateway.Deployment( "apiGatewayDeployment", rest_api=api_gateway.id, stage_name="v1", # Define the stage name for the deployment depends_on=[get_method], ) # Optionally, you can also set up a domain name to be used with the API Gateway. # This step is beyond the basic setup and would require creating a custom domain name, # a certificate, and mapping the domain name to the API Gateway deployment stage. # Export the invoke URL of the deployed API Gateway. pulumi.export("invoke_url", deployment.invoke_url)

    This program sets up an API Gateway with a /predict endpoint that, when called, will internally invoke the specified SageMaker endpoint. The integration is done through the setup in the sagemakerIntegration resource where it's configured to communicate with SageMaker. The deployment then makes the created APIs available as a stage accessible over HTTP.

    Make sure to replace <your-sagemaker-endpoint-name> with your actual SageMaker endpoint name, and adjust the authorization method and other configuration parameters as necessary for your use case.

    By deploying this Pulumi program, you've created a serverless and scalable API Gateway fronting your machine learning model, which can be a powerful architecture for real-time inference at scale.