just train if trainable is true, otherwise rise info in logger

src/run_modules/training.py

@@ -28,6 +28,7 @@ class Training(RunEnvironment):
         self.lr_sc = self.data_store.get("lr_decay", "general.model")
         self.hist = self.data_store.get("hist", "general.model")
         self.experiment_name = self.data_store.get("experiment_name", "general")
+        self._trainable = self.data_store.get("trainable", "general")
         self._run()
 
     def _run(self) -> None:
@@ -44,8 +45,11 @@ class Training(RunEnvironment):
         """
         self.set_generators()
         self.make_predict_function()
+        if self._trainable:
             self.train()
             self.save_model()
+        else:
+            logging.info("No training has started, because trainable parameter was false.")
 
     def make_predict_function(self) -> None:
         """