Open AIApprentice101 opened 5 months ago
Hi @AIApprentice101, We don't have the functionality in the ray-llm, you have to set it up by yourself.
For redis solution, do you see any issue or pain points? Or it is more about the integration effort.
@sihanwang41 Thank you for your reply. I saw there's a RFC related to integration of queuing system in Ray serve: https://github.com/ray-project/ray/issues/32292. So I was wondering if that's something Ray-LLM would consider, especially given the inference of LLM usually takes pretty long to run.
In the meantime, we can set up the queuing system ourselves.
Thank you for the great package. I'm interested in hosting an LLM on GKE.
For our existing ML applications, we usually implement a queue-worker system (e.g. redis-queue or redis-celery) to handle long-running background tasks. Does ray-llm have a similar feature implemented under-the-hood? Or do I need to set it up myself?