1. Low-Latency AI Inference Endpoints with AWS Lambda Function URLs


    To set up low-latency AI inference endpoints with AWS Lambda, we'll create a Lambda function with a Function URL. AWS Lambda is a serverless computing service that allows you to run code without provisioning or managing servers, and Function URLs are a feature that provides a dedicated HTTP(S) endpoint for your Lambda functions. This setup is particularly useful for AI inference because it allows for quick and easy deployment of machine learning models to be invoked via a simple HTTP request.

    Here's what we'll do:

    1. AWS Lambda Function: We'll start by creating an AWS Lambda function. This function will contain the code that loads your machine learning model and performs inference. We'll assume you have a model and inference code ready for deployment.

    2. Lambda Function URL: Once the Lambda function is in place, we'll enable a Function URL for it. The Function URL serves as an endpoint that you can call from any client over HTTPS to invoke your Lambda function.

    3. Setting CORS: Cross-Origin Resource Sharing (CORS) is important if you are calling your Function URL from a web application that is hosted on a different domain. You'll have the option to specify CORS settings for your Function URL.

    4. IAM Role: The Lambda function will need an IAM role that provides permissions to execute the function and other AWS services it may need to interact with.

    Let's go ahead and write a program in Python using Pulumi to create these resources.

    import pulumi import pulumi_aws as aws # Create an IAM role for the Lambda function 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 the AWS managed LambdaBasicExecutionRole policy to the role. # This policy includes permissions to write logs to CloudWatch. policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole") # Assuming you have an `inference_handler.py` that contains your model loading and inference logic, # and `model.zip` that contains your model files and `inference_handler.py`. lambda_function = aws.lambda_.Function("aiInferenceFunction", role=lambda_role.arn, handler="inference_handler.handler", # 'handler' is the entry point in your 'inference_handler.py' runtime="python3.8", code=pulumi.FileArchive("./model.zip")) # Create a Lambda Function URL for AI inference lambda_function_url = aws.lambda_.FunctionUrl("aiInferenceFunctionUrl", function_name=lambda_function.name, authorization_type="NONE", # Publicly accessible; for private use "AWS_IAM". cors=aws.lambda_.FunctionUrlCorsArgs( allow_methods=["POST"], # Assuming inference requests use POST allow_headers=["*"], allow_origins=["*"], # Adjust this if you want to restrict the origins )) # Export the Function URL endpoint to access from the client pulumi.export("aiInferenceEndpoint", lambda_function_url.function_url)

    In the above program:

    • We first create an IAM role with the necessary trust relationship that allows AWS Lambda to assume the role.
    • We attach the AWSLambdaBasicExecutionRole policy to the IAM role so that our Lambda function can write logs to CloudWatch.
    • We define a Lambda function, specifying the inference handler and runtime. We need to provide a ZIP archive that contains our inference code and any other necessary files.
    • We enable a Function URL for our Lambda function. We've chosen to make this endpoint public (authorization_type="NONE") for simplicity, but for production workloads, you would typically use AWS_IAM as the authorization type and manage access via IAM policies.
    • CORS settings are specified to allow client applications to interact with the Function URL.
    • Finally, we export the Function URL endpoint as an output of our Pulumi program, which you can then use to invoke the Lambda function from any HTTP client.

    Please ensure you have the AWS CLI installed and configured with the appropriate credentials, and Pulumi CLI is installed to run this Pulumi program. Also, adapt paths to your model and handler code as needed.