Batch Inference

6 Steps

1. 1

   Set up the Inference Queue: Create a queue to hold incoming inference requests. This queue will act as a buffer, allowing us to accumulate requests before processing them in batches.

2. 2

   Implement Request Submission: Define a function to submit inference requests to the queue. Each request should contain the necessary data for the LLM to process (e.g., text prompt).

3. 3

   Implement Dynamic Batching: Create a function to dynamically create batches from the queue. This function should check the queue size and create a batch when a certain threshold is reached or a timeout occurs. This example uses a simple size threshold.

4. 4

   Implement Inference Processing: Define a function to process a batch of requests using the LLM. This function simulates an LLM call. In a real-world scenario, this would involve calling an LLM API or running a local LLM model.

5. 5

   Implement Result Aggregation: Define a function to aggregate the results from the processed batch. This function can store the results in a database, file, or any other desired storage mechanism.

6. 6

   Implement Rate Limiting (Optional): If the LLM API has rate limits, implement a mechanism to control the rate at which batches are processed. This can be achieved using techniques like token buckets or leaky buckets. This example uses a simple sleep to simulate rate limiting.