I have a suggestion for a small change in Ray-Tune's Trainable - add the flag is_evaluation_step as a member to allow for better control over when evaluate the model.
It could speed-up performance greatly in tuning algorithms where inference takes a long time (such as tuning the very popular variants of 1NN-DTW for time-series, or similar); or when the tuning-by-metric takes a lot of time to calculate.
I have a suggestion for a small change in Ray-Tune's Trainable - add the flag is_evaluation_step as a member to allow for better control over when evaluate the model. It could speed-up performance greatly in tuning algorithms where inference takes a long time (such as tuning the very popular variants of 1NN-DTW for time-series, or similar); or when the tuning-by-metric takes a lot of time to calculate.
Originally posted by @erezinman in https://github.com/ray-project/ray/discussions/11723
Also, discussed in this discussion and added as an issue per @richardliaw's request.