              self._tk_vars.is_training.set(True)
          print("Loading...")
  
          self._statusbar.message.set(f"Executing - {self._command}.py")
          mode = "indeterminate" if self._command in ("effmpeg", "train") else "determinate"
          self._statusbar.start(mode)
  
          args = self._build_args(category)
          self._tk_vars.display.set(self._command)
          logger.debug("Prepared for execution")
 
</s> remove         msg = "[Saved optimizer state for Snapshot]" if force_save_optimizer else "[Saved models]"
 </s> add         msg = "[Saved optimizer state for Snapshot]" if force_save_optimizer else "[Saved model]" </s> remove         self._set_mode("determinate")
 </s> add         self._mode = "determinate"
        self._set_mode(self._mode) </s> remove     def _set_mode(self, mode):
        """ Set the progress bar mode """
        self._pbar.config(mode=mode)
 </s> add     def _set_mode(self, mode: T.Literal["indeterminate", "determinate"]) -> None:
        """ Set the progress bar mode

        Parameters
        ----------
        mode: ["indeterminate", "determinate"]
            The mode that the progress bar should be executed in
        """
        self._mode = mode
        self._pbar.config(mode=self._mode) </s> remove         if "[saved models]" not in output.strip().lower():
 </s> add         t_output = output.strip().lower()
        if "[saved model]" not in t_output or t_output.endswith("[saved model]"):
            # Not a saved model line or saving the model for a reason other than standard saving
        mode: T.Literal["indeterminate",
                        "determinate"] = ("indeterminate" if self._command in ("effmpeg", "train")
                                          else "determinate")
              self._tk_vars.is_training.set(True)
          print("Loading...")
  
          self._statusbar.message.set(f"Executing - {self._command}.py")
        mode: T.Literal["indeterminate",
                        "determinate"] = ("indeterminate" if self._command in ("effmpeg", "train")
                                          else "determinate")
          self._statusbar.start(mode)
  
          args = self._build_args(category)
          self._tk_vars.display.set(self._command)
          logger.debug("Prepared for execution")
keep keep keep keep replace keep keep keep keep keep
 Learning Rate finder (#1341)

* Add LR Finder support structure

* Move trainer.Feeder to lib.training.generator

* Expose model.io

* Add lr_finder

* Update docs and locales

* Pre-PR fixups
  - Fix training graph not displaying
  - CI fixes
  - Switch lr finder progress to tqdm
  - Exit lr finder early on NaN
  - Display lr finder progress in GUI 
lib/gui/wrapper.py