Submitted by alyflex t3_125nf35 in MachineLearning
I have been trying to find a nice tech stack I like for designing and running machine learning models, and currently I'm trying out mlflow, hydra, and optuna.
However, hydra seems to have several limitations that are really annoying and are making me reconsider my choice. Most problematic is the inability to group parameters together in a multirun. Hydra only supports trying all combinations of parameters, as described in https://github.com/facebookresearch/hydra/issues/1258, which does not seem to be a priority for hydra. Furthermore, hydras optuna optimizer implementation does not allow for early pruning of bad runs, which while not a deal breaker is definitely a nice to have feature.
What I do like about hydra is their ability to combine config yaml, using defaults. So does anyone have any good alternatives or suggestions for how to fix this or what to switch to?
RicketyCricket t1_je50mbi wrote
https://github.com/fidelity/spock