1. Event-Driven Model Deployment with API Gateway


    To create an event-driven model deployment with an API Gateway, you'll need a few components in place:

    1. API Gateway: This is the entry point for your clients to send requests. In the context of AWS, it can be set up to trigger a Lambda function.

    2. Lambda Function: AWS Lambda will handle the computation or any CRUD operation you need to perform in response to API requests.

    3. Model Deployment: This could be in the form of a container running on AWS Fargate or an already-deployed machine learning model. If your model is hosted in AWS, you can trigger an inference endpoint directly from Lambda.

    The resources used will be from the AWS provider in Pulumi, specifically the API Gateway (aws.apigateway.RestApi, aws.apigateway.Resource, aws.apigateway.Method, etc.) to set up the endpoint and Lambdas (aws.lambda.Function) to execute your logic.

    In the following program, I'll demonstrate how to create a REST API Gateway that triggers a Lambda function. This setup can be used as a foundation for an event-driven model by instructing the Lambda function to make any necessary call to your model deployment (not included in the code, as it's specific to your model).

    import pulumi import pulumi_aws as aws # Create an IAM role that the Lambda function will use. 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 a policy to the IAM role for the necessary Lambda permissions. policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) # Define the Lambda function. lambda_function = aws.lambda_.Function("modelLambdaFunction", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./path-to-your-lambda-code") }), role=lambda_role.arn, handler="index.handler", # Assuming we have a file index.js with a handler function. runtime="python3.8" # The runtime language for your Lambda function. Change as needed. ) # Create an API Gateway Rest API. rest_api = aws.apigateway.RestApi("api", description="API for model deployment." ) # Create a resource under the Rest API. We'll use this as the endpoint route. resource = aws.apigateway.Resource("resource", rest_api_id=rest_api.id, parent_id=rest_api.root_resource_id, path_part="model" # The path for the model deployment endpoint. ) # Create a method for the resource. This is the HTTP method clients will use. method = aws.apigateway.Method("method", rest_api_id=rest_api.id, resource_id=resource.id, http_method="POST", # Or any other method your model requires. authorization="NONE" # Using no auth for simplicity. Secure your API accordingly. ) # Create the integration between the Lambda function and the API Gateway. integration = aws.apigateway.Integration("integration", rest_api_id=rest_api.id, resource_id=resource.id, http_method=method.http_method, integration_http_method="POST", # The HTTP method used by the Lambda function. type="AWS_PROXY", # Using the Lambda proxy integration. uri=lambda_function.invoke_arn ) # Create a deployment to activate the REST API on a stage. deployment = aws.apigateway.Deployment("deployment", rest_api_id=rest_api.id, stage_name="prod" # Production stage. Change as needed. ) # Create the stage. stage = aws.apigateway.Stage("stage", rest_api_id=rest_api.id, deployment_id=deployment.id, stage_name="v1" # Versioning. Change as needed. ) # Output the HTTPS endpoint URL. pulumi.export("api_url", rest_api.execution_arn.apply(lambda arn: f"{arn}/{stage.stage_name}/model"))


    • IAM Role and Policy: Before any AWS Lambda can execute, it needs the correct permissions. We define a role and policy that allows the Lambda to be invoked and execute under the AWSLambdaBasicExecutionRole.

    • Lambda Function: The function code is in a local directory packaged as a zip and uploaded. Replace ./path-to-your-lambda-code with the path to your Lambda function's code.

    • API Gateway: We set up the Rest API and a resource corresponding to the model endpoint. We create a POST method (can be changed to GET or other HTTP methods, depending on the requirements).

    • Integration: The API Gateway is connected to the Lambda using an AWS_PROXY integration, so the Lambda function receives the full request and returns a full response to the caller.

    • Deployment & Stage: Finally, we have to deploy our API to make it accessible. Here we've deployed it to a stage named prod. You can change the stage name as per needs.

    To run this Pulumi program:

    1. Ensure that you have AWS CLI installed and configured with the necessary IAM permissions.
    2. Install the Pulumi CLI and sign up for an account if you haven't already.
    3. Replace the placeholders (like the path to your Lambda code) with actual values.
    4. Run the program using pulumi up.

    This setup can be augmented to include additional resources, such as a dynamoDB to store requests and responses or an S3 bucket to manage larger payloads. The Lambda function will need the logic to interact with your model deployment - typically, this means calling out to an endpoint on SageMaker or a containerized service.