Batch Inference

Efficiently process large volumes of LLM inference requests by batching them together, optimizing throughput and resource utilization for offline processing.

intermediate30-60 minutes6 steps

The play

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.
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).
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.
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.
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.
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.

Starter code

import queue

inference_queue = queue.Queue()

Source

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