1. Serverless Inference Endpoints with API Gateway


    If you're aiming to set up a serverless inference endpoint with an API Gateway, I'll guide you through creating such a setup using AWS services through Pulumi. AWS API Gateway acts as the front door for your API, handling all the incoming API calls, routing them to the appropriate serverless function, and then returning the responses to the caller. AWS Lambda is commonly used to run inference code on a serverless architecture; it can execute your model's code on-demand without the need to manage servers.

    Here's a step-by-step explanation of what we are going to do:

    1. Create an AWS Lambda function where your inference code will live. For this example, we'll just use a placeholder Python function.
    2. Set up an AWS API Gateway REST API to define the HTTP endpoints that will trigger the Lambda function.
    3. Integrate the Lambda function with the API Gateway, so HTTP requests to the API Gateway will trigger the Lambda function and return the results as HTTP responses.

    Below is the Pulumi Python program that sets up an inference endpoint using AWS Lambda and Amazon API Gateway:

    import pulumi import pulumi_aws as aws # Create a Lambda role and attach the AWSLambdaBasicExecutionRole policy lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") lambda_role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRolePolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Define the lambda function lambda_function = aws.lambda_.Function("myInferenceFunction", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./path_to_your_inference_code") # Replace with the path to your inference code }), timeout=30, # Optional: set the timeout for the lambda function, in seconds role=lambda_role.arn, handler="inference.handler", # Replace with the correct handler for your inference code runtime="python3.8", # Use the runtime your inference code is compatible with tags={ "Name": "MyInferenceFunction" }) # Create an API Gateway to make the lambda accessible over HTTP api_gateway = aws.apigateway.RestApi("apiGateway", description="API Gateway for Inference Endpoint", tags={ "Name": "InferenceEndpointApiGateway" }) # Create a resource corresponding to the path '/inference' inference_endpoint_resource = aws.apigateway.Resource("inferenceEndpointResource", rest_api=api_gateway.id, parent_id=api_gateway.root_resource_id, # Binding this resource to the root path path_part="inference") # This is the path part # Create a method that clients will use to communicate with the endpoint inference_post_method = aws.apigateway.Method("inferencePostMethod", rest_api=api_gateway.id, resource_id=inference_endpoint_resource.id, http_method="POST", authorization="NONE") # Use appropriate authorization # Define the integration between the lambda and the API method integration = aws.apigateway.Integration("lambdaIntegration", rest_api=api_gateway.id, resource_id=inference_endpoint_resource.id, http_method=inference_post_method.http_method, type="AWS_PROXY", integration_http_method="POST", # The HTTP method for the integration. This is what API Gateway uses to forward the request to Lambda. uri=lambda_function.invoke_arn) # The URI that specifies the Lambda function's invoke resource # Set up a method response - necessary for the integration response to work and should match what the client is expected to receive method_response = aws.apigateway.MethodResponse("methodResponse", rest_api=api_gateway.id, resource_id=inference_endpoint_resource.id, http_method=inference_post_method.http_method, status_code="200") # The response clients will receive # Define the integration response - connects the lambda function response to the method response integration_response = aws.apigateway.IntegrationResponse("integrationResponse", rest_api=api_gateway.id, resource_id=inference_endpoint_resource.id, http_method=inference_post_method.http_method, status_code=method_response.status_code, response_templates={"application/json": ""}) # This template should match the expected format of your inference output # Deploy the API Gateway deployment = aws.apigateway.Deployment("apiGatewayDeployment", rest_api=api_gateway.id, stage_name="prod") # Your deployment stage, e.g., 'prod', 'dev', etc. # Make sure to deploy only after setting up the integration pulumi.output.Output.all(deployment.id, lambda_role_policy_attachment.id).apply(lambda args: args) # Output the endpoint url pulumi.export("endpoint_url", deployment.invoke_url)

    To use this Pulumi program successfully, you will need to replace the placeholder string ./path_to_your_inference_code with the actual path to your Lambda inference code ZIP archive, and inference.handler with the actual handler reference for your inference function.

    Please make sure to have Pulumi and AWS CLI set up and configured correctly before running this code. Once you deploy this Pulumi program, it will create all the necessary resources and output the URL of your inference endpoint, which you can use to perform inferences by sending POST requests.

    This setup will provide you with a solid foundation, which you can further customize as per your requirements such as configuring authorization for the endpoint, adding usage plans, etc.