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feat(llm): Determine the best LLM deployment config automatically #2396
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I guess it may belong to the scope of KServe, since Katib focuses on the hyperparameters tuning of models :) |
It's more like a tuning job. You can consider tuning the deployment configs. (e.g. distributed strategy) |
Thank you for creating this @gaocegege! Yes, I think optimization of LLM Deployment makes sense since Katib is able to perform any optimization task (not even ML) and orchestrate any resources as Trials. It would be nice to get someone from the Kubeflow community who can explore the Vidur aspects and see how Katib can be useful. /help |
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@gaocegege @andreyvelich I would love to look into this,can I work on this ? /assign |
Yes, that would be amazing @gjyotin305! /assign @gjyotin305 |
Sure |
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
What you would like to be added?
Inspired by this research paper Vidur: A Large-Scale Simulation Framework For LLM Inference
Optimizing the deployment of Large language models (LLMs) is expensive today since it requires experimentally running an application workload against an LLM implementation while exploring large configuration space formed by system knobs such as parallelization strategies, batching techniques, and scheduling policies.
Why is this needed?
Not sure if it is in the scope of katib, but glad to raise an issue here.
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