Managing Inference Request Queues for LLMs with AWS SQS

Question

Pulumi · Accepted Answer

AWS Simple Queue Service (SQS) is a fully managed message queuing service that allows you to decouple and scale microservices, distributed systems, and serverless applications. It's a suitable choice for setting up a queue that can handle inference requests for large language models (LLMs), as it can handle high volumes of messages and provides features like message durability, visibility timeouts, and automatic scaling.

Here's how you would manage inference request queues for LLMs with AWS SQS using Pulumi in Python:

1. **Resource Initialization** - Use the `aws.sqs.Queue` resource from the `pulumi_aws` package to create a new queue.
2. **Configuration** - Configure various options for the queue, such as:
   - **`fifoQueue`**: If your application requires that messages be processed exactly once, in the exact order that they are sent, you can enable this to create a FIFO (First-In-First-Out) queue.
   - **`visibilityTimeoutSeconds`**: This parameter defines the duration during which a message will be invisible to other queue readers after it's been read. This is useful for inference tasks that might take some time to process without allowing the message to be picked by another worker.
   - **`messageRetentionSeconds`**: This parameter defines how long SQS will keep your message in the queue before deleting it, which can be useful for auditing or replaying lost messages.
3. **Permissions** - You might need to set up permissions for other AWS services or users to interact with your SQS queue, which can be done using `aws.sqs.QueuePolicy`.
4. **Scaling and Monitoring** - Set up monitoring for the queue to track metrics like the number of messages published, their size, and the number of messages consumed. You could also add auto-scaling policies based on these metrics to handle varying loads gracefully.

Below is a Pulumi program that sets up an inference request queue for an LLM with AWS SQS:

```python
import pulumi
import pulumi_aws as aws

# Create a standard SQS queue to manage inference requests for LLMs
inference_queue = aws.sqs.Queue("inferenceQueue",
    # A FIFO queue would ensure that messages are processed in the order they are received and exactly once.
    fifo_queue=True,
    # Visibility timeout to give enough time for inference before another consumer can receive the message.
    visibility_timeout_seconds=300,  # Adjust the timeout based on expected inference processing time.
    # Retain messages for a certain period (in seconds) in case of need to recover or audit, up to 14 days.
    message_retention_seconds=1209600,
    # Enable content-based deduplication to avoid message duplication in FIFO queue.
    content_based_deduplication=True,
)

# Attach a queue policy if you need to define more granular controls and permissions.
queue_policy = aws.sqs.QueuePolicy("queuePolicy",
    queue_url=inference_queue.id,
    policy=pulumi.Output.all(inference_queue.arn).apply(lambda arn: {
        "Version": "2012-10-17",
        "Id": f"{arn}/SQSPolicy",
        "Statement": [{
            "Effect": "Allow",
            "Principal": "*",
            # Note: You should restrict the Principal and Actions according to your security requirements.
            "Action": "sqs:*",
            "Resource": arn,
        }]
    }),
)

# Export the queue URL so that other services can use it to post messages to this queue
pulumi.export("inference_queue_url", inference_queue.id)
```

In the code above, we are declaring a new SQS queue with FIFO enabled to ensure message ordering and exactly-once processing. We are also setting a visibility timeout that should give enough time for an LLM to process a request without the message becoming visible to other consumers. Additionally, we set the message retention for maximum time for auditing purposes. A content-based deduplication is also enabled to help prevent duplicates in the queue.

Remember to adjust the `visibility_timeout_seconds` and `message_retention_seconds` to match your specific requirements for inference time and message recovery needs, respectively.

Finally, we export the queue URL for other AWS services or external applications to send inference requests to this queue.

This program should be run in a Pulumi environment previously set up with AWS credentials configured to allow the creation of SQS resources.