1. RESTful API for Model Inference with AWS API Gateway

    Python

    To create a RESTful API for model inference using AWS API Gateway, we will follow these steps:

    1. Define an API Gateway REST API resource to act as the entry point to our service.
    2. Define an API Gateway Resource to represent an endpoint in the API.
    3. Define API Gateway Method resources for each HTTP method supported by the endpoint.
    4. Define an API Gateway Integration to connect the API Gateway to the backend, such as an AWS Lambda function.
    5. Optionally, define an API Gateway Authorizer for securing the API, if necessary.
    6. Deploy API changes with an API Gateway Deployment resource.
    7. Expose the API through a stage using an API Gateway Stage resource.

    Let's write the Pulumi program to deploy such an API:

    import pulumi import pulumi_aws as aws # Step 1: Define the API rest_api = aws.apigateway.RestApi("modelInferenceApi", description="RESTful API for model inference", ) # Step 2: Define an API resource (e.g., /inference) resource = aws.apigateway.Resource("inferenceResource", parent_id=rest_api.root_resource_id, path_part="inference", rest_api=rest_api.id, ) # Step 3: Define methods for the API resource # Here we define a POST method. You might adjust this based on your model inference requirements. post_method = aws.apigateway.Method("inferencePostMethod", http_method="POST", authorization="NONE", # To use an authorizer, change this and configure an authorizer resource resource_id=resource.id, rest_api=rest_api.id, ) # Step 4: Define the integration with backend (assuming an AWS Lambda function for the model inference) inference_lambda = aws.lambda_.Function("inferenceLambda", # Lambda configuration (code, roles, handler, etc.) # ... ) integration = aws.apigateway.Integration("inferenceIntegration", http_method=post_method.http_method, resource_id=resource.id, rest_api=rest_api.id, integration_http_method="POST", type="AWS_PROXY", uri=inference_lambda.invoke_arn, ) # Step 5: (Optional) Define an Authorizer if your API requires authentication # ... # Step 6: Deploy the API deployment = aws.apigateway.Deployment("apiDeployment", rest_api=rest_api.id, # It's good practice to use a variable (e.g., a git SHA or a stage name) # to trigger a new deployment on changes. stage_name="v1", ) # Step 7: Set up an API Stage stage = aws.apigateway.Stage("apiStage", deployment=deployment.id, rest_api=rest_api.id, stage_name="v1", ) # Export the HTTPS endpoint of the REST API pulumi.export("endpoint", deployment.invoke_url.apply(lambda url: url + resource.path_part))

    To break this down a bit:

    • aws.apigateway.RestApi creates an API Gateway that serves as the "front door" for applications to access data, business logic, or functionality from your backend services.
    • aws.apigateway.Resource represents a logical part of the API that we can apply HTTP methods to.
    • aws.apigateway.Method attaches an HTTP method to the resource, configuring how the API Gateway responds to requests.
    • aws.apigateway.Integration is used to define the backend service (here we assume a Lambda function) that executes the business logic.
    • aws.apigateway.Deployment is necessary to deploy the API Gateway so it can serve requests.
    • aws.apigateway.Stage represents a logical reference to a lifecycle state of your API (e.g., dev, prod).

    Remember to replace placeholder comment # Lambda configuration (code, roles, handler, etc.) with actual AWS Lambda configuration according to your needs.

    The provided Pulumi program will create a simple RESTful API Gateway setup, which can be adapted for different use-cases, such as model inference.


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