Open gaocegege opened 1 month ago
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 /area llm /remove-label lifecycle/needs-triage
@andreyvelich: This request has been marked as needing help from a contributor.
Please ensure the request meets the requirements listed here.
If this request no longer meets these requirements, the label can be removed
by commenting with the /remove-help
command.
@andreyvelich: The label(s) /remove-label lifecycle/needs-triage
cannot be applied. These labels are supported: tide/merge-method-merge, tide/merge-method-rebase, tide/merge-method-squash
. Is this label configured under labels -> additional_labels
or labels -> restricted_labels
in plugin.yaml
?
@gaocegege @andreyvelich I would love to look into this,can I work on this ?
/assign
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.
Love this feature?
Give it a π We prioritize the features with most π