Add enable_progress_bar to Trainer constructor

CHANGELOG.md

@@ -150,6 +150,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `RichModelSummary` callback ([#9546](https://github.com/PyTorchLightning/pytorch-lightning/pull/9546))
 
 
+- Added `enable_progress_bar` to Trainer constructor ([#9664](https://github.com/PyTorchLightning/pytorch-lightning/pull/9664))
+
+
 - Added `pl_legacy_patch` load utility for loading old checkpoints that have pickled legacy Lightning attributes ([#9166](https://github.com/PyTorchLightning/pytorch-lightning/pull/9166))
 
 
@@ -262,7 +265,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `LightningLoggerBase.close`, `LoggerCollection.close` in favor of `LightningLoggerBase.finalize`, `LoggerCollection.finalize` ([#9422](https://github.com/PyTorchLightning/pytorch-lightning/pull/9422))
 
 
-- Deprecated passing `progress_bar_refresh_rate` to the `Trainer` constructor in favor of adding the `ProgressBar` callback with `refresh_rate` directly to the list of callbacks ([#9616](https://github.com/PyTorchLightning/pytorch-lightning/pull/9616))
+- Deprecated passing `progress_bar_refresh_rate` to the `Trainer` constructor in favor of adding the `ProgressBar` callback with `refresh_rate` directly to the list of callbacks, or passing `enable_progress_bar=False` to disable the progress bar ([#9616](https://github.com/PyTorchLightning/pytorch-lightning/pull/9616))
 
 
 ### Removed

benchmarks/test_basic_parity.py

@@ -157,7 +157,7 @@ def lightning_loop(cls_model, idx, device_type: str = "cuda", num_epochs=10):
     trainer = Trainer(
         # as the first run is skipped, no need to run it long
         max_epochs=num_epochs if idx > 0 else 1,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
         gpus=1 if device_type == "cuda" else 0,
         checkpoint_callback=False,

docs/source/common/trainer.rst

@@ -1281,6 +1281,10 @@ See the :doc:`profiler documentation <../advanced/profiler>`. for more details.
 
 progress_bar_refresh_rate
 ^^^^^^^^^^^^^^^^^^^^^^^^^
+``progress_bar_refresh_rate`` has been deprecated in v1.5 and will be removed in v1.7.
+Please pass :class:`~pytorch_lightning.callbacks.progress.ProgressBar` with ``refresh_rate``
+directly to the Trainer's ``callbacks`` argument instead. To disable the progress bar,
+pass ``enable_progress_bar = False`` to the Trainer.
 
 .. raw:: html
 
@@ -1305,6 +1309,19 @@ Note:
       Lightning will set it to 20 in these environments if the user does not provide a value.
     - This argument is ignored if a custom callback is passed to :paramref:`~Trainer.callbacks`.
 
+enable_progress_bar
+^^^^^^^^^^^^^^^^^^^
+
+Whether to enable or disable the progress bar. Defaults to True.
+
+.. testcode::
+
+    # default used by the Trainer
+    trainer = Trainer(enable_progress_bar=True)
+
+    # disable progress bar
+    trainer = Trainer(enable_progress_bar=False)
+
 reload_dataloaders_every_n_epochs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -273,7 +273,6 @@ def add_arguments_to_parser(self, parser):
             {
                 "trainer.max_epochs": 15,
                 "trainer.weights_summary": None,
-                "trainer.progress_bar_refresh_rate": 1,
                 "trainer.num_sanity_val_steps": 0,
             }
         )

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -38,6 +38,7 @@ def on_trainer_init(
         self,
         callbacks: Optional[Union[List[Callback], Callback]],
         checkpoint_callback: bool,
+        enable_progress_bar: bool,
         progress_bar_refresh_rate: Optional[int],
         process_position: int,
         default_root_dir: Optional[str],
@@ -79,10 +80,16 @@ def on_trainer_init(
             rank_zero_deprecation(
                 f"Setting `Trainer(progress_bar_refresh_rate={progress_bar_refresh_rate})` is deprecated in v1.5 and"
                 " will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.ProgressBar` with"
-                " `refresh_rate` directly to the Trainer's `callbacks` argument instead."
+                " `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress"
+                " bar pass `enable_progress_bar = False` to the Trainer."
             )
 
-        self.trainer._progress_bar_callback = self.configure_progress_bar(progress_bar_refresh_rate, process_position)
+        if enable_progress_bar:
+            self.trainer._progress_bar_callback = self.configure_progress_bar(
+                progress_bar_refresh_rate, process_position
+            )
+        else:
+            self.trainer._progress_bar_callback = None
 
         # configure the ModelSummary callback
         self._configure_model_summary_callback(weights_summary)

pytorch_lightning/trainer/trainer.py

@@ -136,6 +136,7 @@ def __init__(
         ipus: Optional[int] = None,
         log_gpu_memory: Optional[str] = None,
         progress_bar_refresh_rate: Optional[int] = None,  # TODO: remove in v1.7
+        enable_progress_bar: bool = True,
         overfit_batches: Union[int, float] = 0.0,
         track_grad_norm: Union[int, float, str] = -1,
         check_val_every_n_epoch: int = 1,
@@ -285,7 +286,10 @@ def __init__(
                 .. deprecated:: v1.5
                     ``progress_bar_refresh_rate`` has been deprecated in v1.5 and will be removed in v1.7.
                     Please pass :class:`~pytorch_lightning.callbacks.progress.ProgressBar` with ``refresh_rate``
-                    directly to the Trainer's ``callbacks`` argument instead.
+                    directly to the Trainer's ``callbacks`` argument instead. To disable the progress bar,
+                    pass ``enable_progress_bar = False`` to the Trainer.
+
+            enable_progress_bar: Whether to enable to progress bar by default.
 
             profiler: To profile individual steps during training and assist in identifying bottlenecks.
 
@@ -450,6 +454,7 @@ def __init__(
         self.callback_connector.on_trainer_init(
             callbacks,
             checkpoint_callback,
+            enable_progress_bar,
             progress_bar_refresh_rate,
             process_position,
             default_root_dir,

tests/accelerators/test_ddp_spawn.py

@@ -52,7 +52,7 @@ def test_multi_gpu_model_ddp_spawn(tmpdir):
         limit_val_batches=10,
         gpus=[0, 1],
         accelerator="ddp_spawn",
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
 
     model = BoringModel()
@@ -73,7 +73,7 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
 
     trainer = Trainer(
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         max_epochs=1,
         limit_train_batches=0.2,
         limit_val_batches=0.2,

tests/accelerators/test_dp.py

@@ -88,7 +88,7 @@ def test_multi_gpu_model_dp(tmpdir):
         limit_val_batches=10,
         gpus=[0, 1],
         accelerator="dp",
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
 
     model = BoringModel()

tests/callbacks/test_callbacks.py

@@ -35,7 +35,7 @@ def configure_callbacks(self):
 
     model = TestModel()
     trainer_options = dict(
-        default_root_dir=tmpdir, checkpoint_callback=False, fast_dev_run=True, progress_bar_refresh_rate=0
+        default_root_dir=tmpdir, checkpoint_callback=False, fast_dev_run=True, enable_progress_bar=False
     )
 
     def assert_expected_calls(_trainer, model_callback, trainer_callback):
@@ -86,9 +86,7 @@ def configure_callbacks(self):
             return [model_callback_mock]
 
     model = TestModel()
-    trainer = Trainer(
-        default_root_dir=tmpdir, fast_dev_run=True, checkpoint_callback=False, progress_bar_refresh_rate=1
-    )
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, checkpoint_callback=False)
 
     callbacks_before_fit = trainer.callbacks.copy()
     assert callbacks_before_fit

tests/callbacks/test_early_stopping.py

@@ -141,7 +141,7 @@ def validation_epoch_end(self, outputs):
         callbacks=[early_stop_callback],
         num_sanity_val_steps=0,
         max_epochs=10,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
     trainer.fit(model)
     assert trainer.current_epoch == expected_stop_epoch
@@ -177,7 +177,7 @@ def training_epoch_end(self, outputs):
         callbacks=[early_stop_callback],
         num_sanity_val_steps=0,
         max_epochs=10,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
     trainer.fit(model)
     assert trainer.current_epoch == expected_stop_epoch
@@ -444,7 +444,7 @@ def validation_step(self, batch, batch_idx):
         default_root_dir=tmpdir,
         limit_val_batches=1,
         callbacks=EarlyStopping(monitor="foo"),
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         **kwargs,
     )
 

tests/callbacks/test_finetuning_callback.py

@@ -425,7 +425,7 @@ def forward(self, x):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=2,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         callbacks=[ckpt, BackboneFinetuning(unfreeze_backbone_at_epoch=1)],
     )
     trainer.fit(BackboneBoringModel())
@@ -436,7 +436,7 @@ def forward(self, x):
         limit_train_batches=1,
         limit_val_batches=1,
         max_epochs=3,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         callbacks=BackboneFinetuning(unfreeze_backbone_at_epoch=1),
         resume_from_checkpoint=ckpt.last_model_path,
     )

tests/callbacks/test_lr_monitor.py

@@ -251,7 +251,7 @@ def configure_optimizers(self):
         limit_val_batches=0.1,
         limit_train_batches=0.5,
         callbacks=[lr_monitor],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
     )
     trainer.fit(TestModel())
@@ -272,7 +272,7 @@ def configure_optimizers(self):
         limit_val_batches=2,
         limit_train_batches=2,
         callbacks=[lr_monitor],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
     )
     trainer.fit(TestModel())
@@ -310,7 +310,7 @@ def configure_optimizers(self):
         limit_val_batches=2,
         limit_train_batches=2,
         callbacks=[lr_monitor],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
     )
 
@@ -388,7 +388,7 @@ def finetune_function(self, pl_module, epoch: int, optimizer, opt_idx: int):
         limit_val_batches=0,
         limit_train_batches=2,
         callbacks=[TestFinetuning(), lr_monitor, Check()],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
         checkpoint_callback=False,
     )

tests/callbacks/test_progress_bar.py

@@ -59,11 +59,19 @@ def test_progress_bar_on(tmpdir, callbacks: list, refresh_rate: Optional[int]):
     assert progress_bars[0] is trainer.progress_bar_callback
 
 
-@pytest.mark.parametrize("callbacks,refresh_rate", [([], 0), ([], False), ([ModelCheckpoint(dirpath="../trainer")], 0)])
-def test_progress_bar_off(tmpdir, callbacks: list, refresh_rate: Union[bool, int]):
+@pytest.mark.parametrize(
+    "callbacks,refresh_rate,enable_progress_bar",
+    [([], 0, True), ([], False, True), ([ModelCheckpoint(dirpath="../trainer")], 0, True), ([], 1, False)],
+)
+def test_progress_bar_off(tmpdir, callbacks: list, refresh_rate: Union[bool, int], enable_progress_bar: bool):
     """Test different ways the progress bar can be turned off."""
 
-    trainer = Trainer(default_root_dir=tmpdir, callbacks=callbacks, progress_bar_refresh_rate=refresh_rate)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        callbacks=callbacks,
+        progress_bar_refresh_rate=refresh_rate,
+        enable_progress_bar=enable_progress_bar,
+    )
 
     progress_bars = [c for c in trainer.callbacks if isinstance(c, ProgressBar)]
     assert 0 == len(progress_bars)

tests/callbacks/test_pruning.py

@@ -107,7 +107,7 @@ def train_with_pruning_callback(
 
     trainer = Trainer(
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
         checkpoint_callback=False,
         logger=False,
@@ -225,7 +225,7 @@ def apply_lottery_ticket_hypothesis(self):
     )
     trainer = Trainer(
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
         checkpoint_callback=False,
         logger=False,
@@ -252,7 +252,7 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent: bool
 
     trainer = Trainer(
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         weights_summary=None,
         checkpoint_callback=False,
         logger=False,
@@ -321,7 +321,7 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint):
     ckpt_callback = ModelCheckpoint(
         monitor="test", save_top_k=2, save_last=True, save_on_train_epoch_end=save_on_train_epoch_end
     )
-    trainer = Trainer(callbacks=[pruning_callback, ckpt_callback], max_epochs=3, progress_bar_refresh_rate=0)
+    trainer = Trainer(callbacks=[pruning_callback, ckpt_callback], max_epochs=3, enable_progress_bar=False)
     with caplog.at_level(INFO):
         trainer.fit(model)
 

tests/callbacks/test_stochastic_weight_avg.py

@@ -121,7 +121,7 @@ def train_with_swa(
 
     trainer = Trainer(
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         max_epochs=max_epochs,
         limit_train_batches=5,
         limit_val_batches=0,

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -43,7 +43,7 @@ def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_inter
         weights_summary=None,
         val_check_interval=val_check_interval,
         limit_val_batches=1,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
     trainer.fit(model)
 
@@ -119,7 +119,7 @@ def training_epoch_end(self, outputs) -> None:
     trainer = Trainer(
         callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss_step", save_top_k=k, mode="max")],
         default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         max_epochs=epochs,
         weights_summary=None,
         val_check_interval=val_check_interval,