1. API Gateway for Serving Machine Learning Models

    Python

    To serve machine learning models with an API Gateway, you would typically build a RESTful API that can receive requests, process them using a backend service where your machine learning model is hosted, and return the predictions as a response. In the context of AWS, this can be done by using AWS API Gateway to create the REST API and AWS Lambda to host the machine learning model inference code.

    Given that, I'll guide you through setting up an API Gateway integrated with a Lambda function that could serve as an endpoint for a machine learning model. We'll use Pulumi with AWS provider to accomplish this. Here's what we need to do:

    • Set up an AWS Lambda function that will handle the inference requests. The actual machine learning model is not part of this example, but you would package your model and inference code and deploy it as a Lambda function.
    • Create a REST API using AWS API Gateway.
    • Create an API Gateway resource and method for the prediction endpoint.
    • Integrate the Lambda function with the API Gateway endpoint.
    • Deploy the API Gateway.

    Below is a Pulumi program in Python that accomplishes this setup:

    import pulumi import pulumi_aws as aws # Create an IAM role that the Lambda function can assume. lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") # Attach AWS managed LambdaBasicExecutionRole policy to the role. policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Create the Lambda function that will serve the machine learning model. # For this code to work, you must have a deployment package (ZIP file) for Lambda with your machine learning model and the inference code. lambda_function = aws.lambda_.Function("mlModelFunction", role=lambda_role.arn, handler="inference.handler", # This would be the file and method that AWS Lambda calls to start execution. runtime="python3.8", # Specify the runtime. Make sure this matches the runtime of your package. code=pulumi.AssetArchive({ '.': pulumi.FileArchive('./path_to_your_lambda_deployment_package.zip') })) # Create an API Gateway REST API. api = aws.apigateway.RestApi("mlModelApi", description="API for serving a machine learning model") # Create an API Gateway Resource. This represents a path component within your API. resource = aws.apigateway.Resource("mlModelResource", parent_id=api.root_resource_id, path_part="predict", rest_api=api.id) # Create a method for the resource. This is an HTTP method that clients can call. method = aws.apigateway.Method("mlModelMethod", rest_api=api.id, resource_id=resource.id, http_method="POST", authorization="NONE") # Integrate the Lambda function to the API method. integration = aws.apigateway.Integration("mlModelIntegration", rest_api=api.id, resource_id=resource.id, http_method=method.http_method, integration_http_method="POST", # Lambda's HTTP method for invoking the function. type="AWS_PROXY", # Use AWS_PROXY integration to send the entire request and receive the entire response from the Lambda function. uri=lambda_function.invoke_arn) # Deploy the API to make it callable by clients. deployment = aws.apigateway.Deployment("mlModelDeployment", rest_api=api.id, stage_name="v1") # This is the stage your API is deployed to. # Create a stage which is a snapshot of the deployment. stage = aws.apigateway.Stage("mlModelStage", deployment=deployment.id, rest_api=api.id, stage_name="v1") # Export the URL of the API so it can be called. pulumi.export("api_url", pulumi.Output.concat( "https://", api.id, ".execute-api.", aws.config.region, ".amazonaws.com/", stage.stage_name)) # To test the deployed API, you would send a POST request to the output URL with appropriate data for the machine learning model.

    Explanation of the resources used:

    1. IAM Role: An AWS Identity and Access Management (IAM) role that the Lambda function will assume to grant it permissions to run and log to CloudWatch.

    2. Lambda Function: The computation resource where your machine learning inference code resides. You should replace the provided example handler and runtime with your own, and ensure that the deployment package contains your model and inference code.

    3. API Gateway REST API: This is the API definition that will be the entry point for inference requests.

    4. API Gateway Resource: A resource represents a path in your API. In this example, it's where you would send a POST request with input data to get predictions from your model.

    5. API Gateway Method: Defines the method type (e.g., GET, POST, etc.) that clients use to call the API resource.

    6. API Gateway Integration: Connects the API method request to the backend service, which is the Lambda function in this case.

    7. API Gateway Deployment: Deploys the API to an endpoint that can be called by clients. The stage_name is a label to manage different releases or versions of your API.

    Please note that this code assumes that you've set up the Pulumi AWS provider credentials on your local system, and have a deployment package ready for your Lambda function. If you have not yet set up your AWS credentials on Pulumi, please follow the Pulumi AWS Setup Guide. Make sure your deployment package follows the AWS Lambda Deployment Package in Python.

    Remember that this example focuses primarily on infrastructure and not the specifics of the machine learning model or its inference code. You would need to ensure your Lambda deployment package is correctly set up with all the necessary dependencies to execute the model inference.


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