CURRENT STASH - REVERT THIS

Author: carmocca

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -298,6 +298,9 @@ def on_run_end(self) -> None:
         if self._num_ready_batches_reached():
             self.update_lr_schedulers("epoch", update_plateau_schedulers=True)
 
+            # FIXME: where should this go...
+            # self.batch_progress.current.reset()
+
         # if fault tolerant is enabled and process has been notified, exit.
         self.trainer._exit_gracefully_on_signal()
 

pytorch_lightning/loops/fit_loop.py

@@ -36,7 +36,7 @@ class FitLoop(Loop):
 
     def __init__(
         self,
-        min_epochs: Optional[int] = 1,
+        min_epochs: Optional[int] = 0,
         max_epochs: int = 1000,
     ) -> None:
         super().__init__()
@@ -239,7 +239,9 @@ def advance(self) -> None:
             self.global_step += 1
 
     def on_advance_end(self) -> None:
-        self.epoch_progress.increment_completed()
+        # TODO(@carmocca): remove this check after implementing `loop.stop()`
+        if not self.trainer.should_stop:
+            self.epoch_progress.increment_completed()
 
     def on_run_end(self) -> None:
         """Calls the ``on_train_end`` hook."""

pytorch_lightning/trainer/trainer.py

@@ -321,7 +321,7 @@ def __init__(
                 To enable infinite training, set ``max_epochs = -1``.
 
             min_epochs: Force training for at least these many epochs. Disabled by default (None).
-                If both min_epochs and min_steps are not specified, defaults to ``min_epochs = 1``.
+                If both min_epochs and min_steps are not specified, defaults to ``min_epochs = 0``.
 
             max_steps: Stop training after this number of steps. Disabled by default (-1). If ``max_steps = -1``
                 and ``max_epochs = None``, will default to ``max_epochs = 1000``. To enable infinite training, set
@@ -447,7 +447,7 @@ def __init__(
         self.tuner = Tuner(self)
 
         fit_loop = FitLoop(
-            min_epochs=(1 if (min_epochs is None and min_steps is None and max_time is None) else min_epochs),
+            min_epochs=(0 if (min_epochs is None and min_steps is None and max_time is None) else min_epochs),
             max_epochs=(
                 max_epochs if max_epochs is not None else (1000 if (max_steps == -1 and max_time is None) else -1)
             ),