Scalable Inference Request Handling Using AWS SQS

Question

Pulumi · Accepted Answer

Scalable inference request handling using AWS Simple Queue Service (SQS) can be a critical component of a machine learning (ML) infrastructure. SQS allows you to decouple the components of a cloud application and provides a reliable, highly-scalable hosted queue for storing messages that may be processed asynchronously.

In a typical ML inference scenario using AWS, you might have an endpoint where you receive inference requests (e.g., HTTP requests for image recognition). These requests can be sent to an SQS queue, where they are stored until a backend service (such as an EC2 instance or AWS Lambda function) processes them and perhaps sends the results to another queue or data store.

Below is a Pulumi program written in Python that sets up an SQS queue which can be used as part of such an infrastructure.

Here is how you can set up the SQS queue using Pulumi:

```python
import pulumi
import pulumi_aws as aws

# Create an SQS queue for inference requests
inference_queue = aws.sqs.Queue("inferenceQueue",
                                delay_seconds=0,
                                max_message_size=262144,
                                message_retention_seconds=345600,
                                receive_wait_time_seconds=10,
                                visibility_timeout_seconds=30)

# Export the queue URL and ARN to be used in other parts of the application
pulumi.export('inference_queue_url', inference_queue.id)
pulumi.export('inference_queue_arn', inference_queue.arn)
```

- We create an SQS queue named `inferenceQueue`. We configure it with some attributes such as `delay_seconds`, `max_message_size`, `message_retention_seconds`, `receive_wait_time_seconds`, and `visibility_timeout_seconds`. These attributes control how the queue behaves—for example, how long messages are kept in the queue and how long a message is hidden from other consumers after a consumer picks it up.
- After the queue is created, we export its URL and ARN (Amazon Resource Name) so that they can be easily referenced in other parts of your application, for instance, by the processing service or for setting up permissions.

Please note that for a complete ML application setup, additional resources like the ML model endpoint, a compute service to process the messages from the queue, and perhaps a database or another queue (for results) will be necessary. This code only covers the setup of the queue for handling incoming inference requests.