It looks like the Ray Tune docs push you to write a Trainable class with a _train method that does incremental training and reports metrics as a dict. There is some persistance of state through the _load _restore methods.
TLDR; There is no stated good way to run nested parallel runs based on a config and log the results etc. Does this pattern break the tune flow?
If you want to use aggregated scores over different train/test pairs (with different models), it the intended pattern that you map (parellel) over the train/test pairs and models inside the _train method?
If there is a better place for this question about Tune usage let me know.