              train_args("lightweight",
                         pathjoin(vid_base, "model"),
                         pathjoin(vid_base, "faces"),
                         iterations=1,
                         batchsize=1,
                         extra_args="-wl"))
  
      if was_trained:
          run_test(
              "Convert video.",
              convert_args(
 
</s> remove #: lib/cli/args.py:697
 </s> add #: lib/cli/args.py:696 </s> remove                 if self._command == "train" and output.startswith("Reading training images"):
                    print(output.strip(), file=sys.stdout)
 </s> add                 if self._process_training_determinate_function(output): </s> remove         if not all(os.path.isfile(os.path.join(self.model_dir, fname))
 </s> add         if not all(os.path.isfile(os.path.join(self.io.model_dir, fname)) </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]"
                       batchsize=1))
              train_args("lightweight",
                         pathjoin(vid_base, "model"),
                         pathjoin(vid_base, "faces"),
                         iterations=1,
                       batchsize=1))
                       batchsize=1))
  
      if was_trained:
          run_test(
              "Convert video.",
              convert_args(
keep keep keep keep replace 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 
tests/simple_tests.py