Partially it’s because I had some existing code there that loaded in my csv files and parsed them how I wanted, etc.
But also, there is some stuff that is easier to do in Python than in other places, mainly because of all of the sklearn packages, like some data manipulation utilities that aren’t implemented in Fluid stuff yet (randomly splitting a dataset into train and test sets and other numpy-like stuff). Also, the server client management in SC can be annoying when trying to train many MLPs for example–lots of callback functions, server syncing, buffer filling, and the like, where in Python it’s more straight ahead sequential code.
However, the MLP training is faster in FluCoMa than it is in sklearn, which makes sense. I don’t think sklearn is better just different. In the long run I’m planning to be primarily in SC, but the two do feel different, those are some thoughts.