1. LLM Inference Request Scaling using EventRuleEventSubscription


    In the context of cloud infrastructure and event-driven architectures, an "Event Rule" can be thought of as a way to trigger actions in response to specific events occurring within cloud services. These rules can listen for patterns of events and react by invoking serverless functions, sending messages, or even initiating other cloud-based processes. For instance, an event could be an inference request coming from an AI service like an LLM (Large Language Model) that might then need to scale based on the volume of requests.

    In AWS, one common way to handle such event-driven scaling is to use Amazon CloudWatch Events (Event Rules) alongside AWS Lambda for the execution of scaling logic, and other AWS services like Auto Scaling or application services that handle the scaling of the inference requests themselves.

    Here's how to set up an AWS CloudWatch Event Rule that listens for a specific event pattern, and how to create an AWS Lambda function subscribed to that event rule. This function could then handle scaling logic. We'll use Pulumi's Python SDK to create the CloudWatch Event Rule and Lambda function.

    Before we proceed with the code, we should have the inference scaling logic inside an AWS Lambda function ready, and we're assuming the target LLM service sends events that CloudWatch can detect and pattern match against. Now, let's set up the infrastructure.

    import pulumi import pulumi_aws as aws # Define the AWS Lambda function, which will handle the scaling logic. scaling_lambda = aws.lambda_.Function("llm_scaling_lambda", runtime="python3.8", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./lambda") # Assuming your lambda code is in the 'lambda' directory }), handler="handler.main", # Assuming your entry point is called 'main' in a file named 'handler.py' role=iam_role.arn # Assuming you have an IAM Role named 'iam_role' with necessary permissions ) # Define the CloudWatch Event Rule that triggers on a specific pattern # Replace 'event_pattern' with the pattern you're interested in. event_rule = aws.cloudwatch.EventRule("llm_inference_request_rule", event_pattern=pulumi.Output('{"source": ["my.llm.service"], "detail-type": ["Inference Request"]}') ) # Set the Lambda function as the target for our Event Rule. event_target = aws.cloudwatch.EventTarget("llm_scaling_lambda_target", rule=event_rule.name, arn=scaling_lambda.arn ) # Give CloudWatch Events permission to invoke our Lambda function. lambda_permission = aws.lambda_.Permission("llm_scaling_lambda_permission", action="lambda:InvokeFunction", function=scaling_lambda.name, principal="events.amazonaws.com", source_arn=event_rule.arn ) # Export the Lambda Function Name and CloudWatch Event Rule Name pulumi.export("lambda_function_name", scaling_lambda.name) pulumi.export("event_rule_name", event_rule.name)

    In this program, we are performing the following actions:

    1. We create an AWS Lambda function (scaling_lambda) which contains our scaling logic code. This is the code that will be executed when the CloudWatch Event Rule is triggered.

    2. We then define a CloudWatch Event Rule (event_rule) with an event_pattern that looks for specific events. In this example pattern, we're looking for events from a source assumed to be my.llm.service with a detail-type Inference Request. You would modify this to match the actual events emitted by your LLM service.

    3. We set up an EventTarget that associates the Lambda function with the Event Rule we created. This means that when the Event Rule is triggered by a matching event, it will cause our Lambda function to execute.

    4. We grant permission to CloudWatch Events to invoke our Lambda function using a Permission resource. This is necessary because AWS services require explicit permission to trigger each other.

    Finally, we export the Lambda function name and the CloudWatch Event Rule name. These outputs will be visible once the Pulumi deployment is complete and can be used for reference or integration in other systems or parts of the infrastructure.

    Please ensure that you replace the placeholders and assumptions with the actual details from your LLM service and ensure that your Lambda function code (the scaling logic) is in place and tested before deploying this infrastructure.