Integrate Ray Tune for Internal Hyperparameter Search

[etgld]

Docs https://docs.ray.io/en/master/tune/index.html

Core example https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/pbt_transformers/pbt_transformers.py

[etgld]

There might be a way to do this that's still worth trying but need to get a proof of concept working. Long story short the most high power method which was monotonically increasing I think was Population-Based Bandits.

The main issue around supporting Ray Tune I think involves UI. There are enough options for a Ray Tune run that including them in cnlp_args would create a lot of clutter I think. Maybe something better would involve some basic instructions/recipes plus a sample script.

Granted this has been a while but now that I have more facility with setting up these kinds of things on E2 I might be able to try it again.

[tmills]

I think there is also some cmd line parameters we inherit from HF that are supposed to automatically do tuning -- we should look into whether we can just use those as is.

[etgld]

For reference, there haven't been many updated examples for using Ray Tune with HuggingFace but this one looks helpful https://docs.ray.io/en/latest/train/examples/transformers/huggingface_text_classification.html Although it's sparse there are some up to date search space guidelines from HuggingFace for different backends https://huggingface.co/docs/transformers/en/hpo_train https://huggingface.co/docs/setfit/en/how_to/hyperparameter_optimization I remember from last year there was this nice paper about automatic gradient descent without hyperparameters, the caveat being that it doesn't presently work for transformers, I looked for the first author to see if there were any updates but nothing yet. https://arxiv.org/abs/2304.05187

There are some things we can definitely inherit from cnlp_args although a lot of these backends have a lot of options and for some cases a config JSON might be the easiest way.