Hi @RomanChernenko and @mmiron,
The preprocess function option is provided in the case that you may want to do feature calculations once in a more efficient manner once outside of the predict loop. You are encouraged to use it for data processing to reduce redundancy. You might also find other ways to reduce redundant calculations, such as caching calculation results in your code, in the assets dictionary that is returned by preprocess and then passed between predict recalls, or on disk in preprocessed_dir.
Overall, we encourage you to structure your code in a way so that it is clear and readable as possible, so that challenge organizers will be able to understand how it works and verify that you are correctly following the requirements about the use of data and time.