Thanks for responding quickly. For this specific use case I need a regression sklearn model (trained in 10-fold CV) that I want to hyperoptimize using optuna. As my datasets are updated regularly, I'd like to define all of this in a pipeline such that I can easily run everything again once the data is changed.
I'm now thinking I need some main process that runs first a base_template task such that all gets initialized well. In the same process start the HPO which will add subtasks to the queue. This main process (also a task) will then wait until all other tasks (i.e. hyperparameter setups) have completed before wrapping up and reporting back.
Hi @<1577468611524562944:profile|MagnificentBear85> , can you please elaborate a bit on how exactly you want to stricture this?
