I have been doing some performance tests for model loading of DIETClassifiers without pre-trained word vectors. Looks like there is a lot of smart caching / loading going on. Does anybody know why loading the first model is so much slower than the latter ones? Also is there a way to speed up the load time for the very first model?
@koaning would be awesome if you could point me in the right direction.
from pathlib import Path import time from rasa.nlu.model import Interpreter load_times =  for model_path in Path("./models_10").glob("*"): start = time.time() nlu_interpreter = Interpreter.load(model_path) load_times.append(time.time() - start) print(load_times) >>>>>>>>> [4.209419012069702, 1.2521369457244873, 1.2520649433135986, 1.256606101989746, 1.4406280517578125, 1.234318733215332, 1.238109827041626, 1.2923908233642578, 1.4882800579071045]