I guess the answer to this is “not supported” when looking at similar requests, but at least… let me add a +1 to the feature request. Simplified, presume I have two approaches to training a model, each with its own sub-approaches. Is there a way to define a sweep config in a way that I can do a search over sub-approaches that depend on the main approach, i.e., how to build something like:
python3 train.py --approach a --sub_approach a1 python3 train.py --approach a --sub_approach a2 python3 train.py --approach b --sub_approach b1 python3 train.py --approach b --sub_approach b2
- do 2 sweeps for a anb b in that toy example, but I think I cannot combine the results of different sweeps into a single graph, right?
- Or I could check in
train.pyand abort the program for invalid combinations; in this case I think I need to tell wandb to continue with the sweep since there would be a number of “crashes”
- Or use a different hyper param optimizer and integrate into wandb (but would like to avoid that).
Any other options? If different hyper param optimizer, any recommendation because of better/easier integration with wandb? Thanks for your input/ideas.