I wonder if Sylvain or others might have advice on how to make the hyperparameters search more efficient or manageable, time and resource-wise.
I’ve tried slimming down the dataset (500K rows to 90K rows), reducing the number of parameters to tune (to just 1, number of epochs) and changing the “direction” to “minimize” instead of “maximize”.
Is there something else I can do, aside from further cutting down the size of the dataset? I’m running trials on Colab Pro with GPU/high-RAM enabled, and current version looks like it’ll take about 7 hours (perfectly fine for others I’m sure).
I don’t suppose there’s an equivalent of RandomizedSearchCV for trainer?