[wip] Make RichProgressBar use auto_refresh=True

.github/workflows/ci-pytorch-test-full.yml

@@ -16,7 +16,6 @@ jobs:
 
   pl-cpu:
     runs-on: ${{ matrix.os }}
-    if: github.event.pull_request.draft == false
     strategy:
       fail-fast: false
       matrix:

.github/workflows/ci-pytorch-test-slow.yml

@@ -21,7 +21,6 @@ concurrency:
 jobs:
   pl-slow:
     runs-on: ${{ matrix.os }}
-    if: github.event.pull_request.draft == false
     strategy:
       fail-fast: false
       matrix:

src/pytorch_lightning/callbacks/progress/rich_progress.py

@@ -289,18 +289,13 @@ def _init_progress(self, trainer):
             self.progress = CustomProgress(
                 *self.configure_columns(trainer),
                 self._metric_component,
-                auto_refresh=False,
                 disable=self.is_disabled,
                 console=self._console,
             )
             self.progress.start()
             # progress has started
             self._progress_stopped = False
 
-    def refresh(self) -> None:
-        if self.progress:
-            self.progress.refresh()
-
     def on_train_start(self, trainer, pl_module):
         self._init_progress(trainer)
 
@@ -319,7 +314,6 @@ def on_sanity_check_start(self, trainer, pl_module):
     def on_sanity_check_end(self, trainer, pl_module):
         if self.progress is not None:
             self.progress.update(self.val_sanity_progress_bar_id, advance=0, visible=False)
-        self.refresh()
 
     def on_train_epoch_start(self, trainer, pl_module):
         total_batches = self.total_batches_current_epoch
@@ -335,8 +329,6 @@ def on_train_epoch_start(self, trainer, pl_module):
                 self.main_progress_bar_id, total=total_batches, description=train_description, visible=True
             )
 
-        self.refresh()
-
     def on_validation_batch_start(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
     ) -> None:
@@ -359,8 +351,6 @@ def on_validation_batch_start(
                 self.total_val_batches_current_dataloader, self.validation_description, visible=False
             )
 
-        self.refresh()
-
     def _add_task(self, total_batches: int, description: str, visible: bool = True) -> Optional[int]:
         if self.progress is not None:
             return self.progress.add_task(
@@ -376,15 +366,13 @@ def _update(self, progress_bar_id: int, current: int, visible: bool = True) -> N
             leftover = current % self.refresh_rate
             advance = leftover if (current == total and leftover != 0) else self.refresh_rate
             self.progress.update(progress_bar_id, advance=advance, visible=visible)
-            self.refresh()
 
     def _should_update(self, current: int, total: Union[int, float]) -> bool:
         return current % self.refresh_rate == 0 or current == total
 
     def on_validation_epoch_end(self, trainer, pl_module):
         if self.val_progress_bar_id is not None and trainer.state.fn == "fit":
             self.progress.update(self.val_progress_bar_id, advance=0, visible=False)
-            self.refresh()
 
     def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         if trainer.state.fn == "fit":
@@ -406,7 +394,6 @@ def on_test_batch_start(
         if self.test_progress_bar_id is not None:
             self.progress.update(self.test_progress_bar_id, advance=0, visible=False)
         self.test_progress_bar_id = self._add_task(self.total_test_batches_current_dataloader, self.test_description)
-        self.refresh()
 
     def on_predict_batch_start(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
@@ -419,12 +406,10 @@ def on_predict_batch_start(
         self.predict_progress_bar_id = self._add_task(
             self.total_predict_batches_current_dataloader, self.predict_description
         )
-        self.refresh()
 
     def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
         self._update(self.main_progress_bar_id, self.train_batch_idx + self._val_processed)
         self._update_metrics(trainer, pl_module)
-        self.refresh()
 
     def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         self._update_metrics(trainer, pl_module)
@@ -437,15 +422,16 @@ def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx,
             if self.main_progress_bar_id is not None:
                 self._update(self.main_progress_bar_id, self.train_batch_idx + self._val_processed)
             self._update(self.val_progress_bar_id, self.val_batch_idx)
-        self.refresh()
+
+        # TODO: Find out why an error occurs without refresh here.
+        if self.progress:
+            self.progress.refresh()
 
     def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
         self._update(self.test_progress_bar_id, self.test_batch_idx)
-        self.refresh()
 
     def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
         self._update(self.predict_progress_bar_id, self.predict_batch_idx)
-        self.refresh()
 
     def _get_train_description(self, current_epoch: int) -> str:
         train_description = f"Epoch {current_epoch}"