1. Scalable AI Inference Functions in AWS US-East-1 with Lambda


    To create scalable AI inference functions in AWS using AWS Lambda, you'll need to deploy your AI inference code as Lambda functions. Furthermore, you'll likely want to automatically scale these functions in response to the incoming traffic, which is a built-in feature of AWS Lambda.

    To do this, we'll walk through the following steps using Pulumi:

    1. Define the Lambda function: Write the code that will perform AI inference. This code needs to be packaged into a format that Lambda can execute.

    2. Create an IAM Role: Lambda functions need an execution role with permissions to run the function and to access other AWS services.

    3. Deploy the Lambda function: We'll use Pulumi to deploy the function, configure environment variables, set memory and timeout settings, and link it to the IAM role.

    4. Test the function: Invoke the Lambda function to ensure that it works as expected.

    import pulumi import pulumi_aws as aws # Step 1: Define your Lambda function code. # In a real-world scenario, you would write the code for your AI inference task, # package it and then upload it to AWS Lambda. For the sake of this example, assume you have # a zipped file named 'inference_function.zip' that contains your code. # Step 2: Create an IAM Role for AWS Lambda. # Lambda functions require an IAM role with the necessary permissions. This role will be assumed # by your Lambda function upon execution. lambda_execution_role = aws.iam.Role("aiInferenceLambdaRole", assume_role_policy=pulumi.Output.secret("""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com" }, "Effect": "Allow", "Sid": "" }] }""") ) # Attach the AWSLambdaBasicExecutionRole policy to the role created. This policy # grants the Lambda function permissions to write log messages to CloudWatch Logs. lambda_execution_policy_attachment = aws.iam.RolePolicyAttachment("aiInferenceLambdaPolicyAttachment", role=lambda_execution_role, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE ) # Step 3: Deploy the Lambda Function. ai_inference_lambda = aws.lambda_.Function("aiInferenceFunction", role=lambda_execution_role.arn, runtime="python3.8", # Replace with the runtime environment of your inference code. handler="index.handler", # Replace with the entry point to your inference code. code=pulumi.FileArchive("inference_function.zip"), memory_size=1024, # Adjust depending on your function's requirements. timeout=30, # Adjust the max execution time of your Lambda functions, in seconds. # NOTE: The actual values for memory_size and timeout will depend on your specific use case and requirements. ) # Step 4: Test the function (replace with your function's input format). test_inference = ai_inference_lambda.invoke(payload=pulumi.Output.secret("""{ "input_data": "your_input_data" }""")) # Export the resulting inference result and the Lambda Function's ARN. pulumi.export("inference_result", test_inference.result) pulumi.export("lambda_function_arn", ai_inference_lambda.arn)

    Here's an explanation of what the above code does:

    1. Define Lambda function code: You'll start by writing the inference code for AI in your preferred programming environment, then package it into a ZIP file. For simplicity, we're assuming that's already done and you have a file named inference_function.zip.

    2. Create an IAM Role: The Lambda function needs permissions to access AWS resources. The IAM role with the appropriate policy is created for that purpose. The policy we've attached allows the Lambda function to write logs.

    3. Deploy the Lambda function: We define a Lambda resource with Pulumi. handler is the function within your code that Lambda calls to start execution. runtime is the programming language environment, and code is a reference to the zipped source package.

    4. Test the function: We invoke the Lambda function with sample input data to test it. This is a simplified version of how you might run a test; you might need to adapt it for your actual input and inference process.

    By deploying with Pulumi, we've created a scalable, serverless AI inference function that will automatically handle incoming traffic at scale. If your inference functions need to be invoked via an HTTP endpoint, combining them with API Gateway or other trigger mechanisms would be the next steps.