[Python] How to efficiently make concurrent calls to a local Llama 3.1 model using the...

I'm using a local Llama 3.1:7b model. I am interacting with the model using Python and the ollama-python package.

I need to make over 20,000 calls to the model, but I'm currently invoking it sequentially, which is inefficient. I'm looking for ways to improve efficiency. Specifically, I'm trying to find out if there's a way to:

1. Batch inputs to send multiple requests at once.
2. Use threading, multiprocessing, or any form of concurrent processing to handle multiple calls simultaneously, instead of making them sequentially.
3. Improve overall speed and response times for a large set of queries.

Does anyone have suggestions or code examples for achieving this with the ollama-python package, or general techniques for handling concurrent API calls in Python? Even if the solution doesn’t specifically involve ollama, I'm open to hearing about other approaches as well.

So far, I’ve tried running the model sequentially and I'm currently tring to thread the calls.

Continue reading...