Deprecate ProgressBar and rename it to TQDMProgressBar

CHANGELOG.md

@@ -209,6 +209,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `lr_sch_names` from `LearningRateMonitor` ([#10066](https://github.com/PyTorchLightning/pytorch-lightning/pull/10066))
 
 
+- Deprecated `ProgressBar` callback in favor of `TQDMProgressBar` ([#10134](https://github.com/PyTorchLightning/pytorch-lightning/pull/10134))
+
+
 ### Removed
 
 - Removed deprecated `metrics` ([#8586](https://github.com/PyTorchLightning/pytorch-lightning/pull/8586/))

docs/source/common/trainer.rst

@@ -1240,7 +1240,7 @@ 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``
+Please pass :class:`~pytorch_lightning.callbacks.progress.TQDMProgressBar` with ``refresh_rate``
 directly to the Trainer's ``callbacks`` argument instead. To disable the progress bar,
 pass ``enable_progress_bar = False`` to the Trainer.
 

pyproject.toml

@@ -67,6 +67,7 @@ module = [
     "pytorch_lightning.callbacks.gradient_accumulation_scheduler",
     "pytorch_lightning.callbacks.lr_monitor",
     "pytorch_lightning.callbacks.model_summary",
+    "pytorch_lightning.callbacks.progress",
     "pytorch_lightning.callbacks.pruning",
     "pytorch_lightning.callbacks.rich_model_summary",
     "pytorch_lightning.core.optimizer",

pytorch_lightning/callbacks/__init__.py

@@ -22,7 +22,7 @@
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 from pytorch_lightning.callbacks.model_summary import ModelSummary
 from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
-from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase, RichProgressBar
+from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase, RichProgressBar, TQDMProgressBar
 from pytorch_lightning.callbacks.pruning import ModelPruning
 from pytorch_lightning.callbacks.quantization import QuantizationAwareTraining
 from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary
@@ -52,4 +52,5 @@
     "RichProgressBar",
     "StochasticWeightAveraging",
     "Timer",
+    "TQDMProgressBar",
 ]

pytorch_lightning/callbacks/progress/__init__.py

@@ -19,5 +19,6 @@
 
 """
 from pytorch_lightning.callbacks.progress.base import ProgressBarBase  # noqa: F401
+from pytorch_lightning.callbacks.progress.progress import ProgressBar  # noqa: F401
 from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar  # noqa: F401
-from pytorch_lightning.callbacks.progress.tqdm_progress import ProgressBar  # noqa: F401
+from pytorch_lightning.callbacks.progress.tqdm_progress import TQDMProgressBar  # noqa: F401

pytorch_lightning/callbacks/progress/progress.py

@@ -0,0 +1,24 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pytorch_lightning.callbacks.progress.tqdm_progress import TQDMProgressBar
+from pytorch_lightning.utilities import rank_zero_deprecation
+
+
+class ProgressBar(TQDMProgressBar):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        rank_zero_deprecation(
+            "`ProgressBar` has been deprecated in v1.5 and will be removed in v1.7."
+            " It has been renamed to `TQDMProgressBar` instead."
+        )

pytorch_lightning/callbacks/progress/tqdm_progress.py

@@ -54,7 +54,7 @@ def format_num(n) -> str:
         return n
 
 
-class ProgressBar(ProgressBarBase):
+class TQDMProgressBar(ProgressBarBase):
     r"""
     This is the default progress bar used by Lightning. It prints to ``stdout`` using the
     :mod:`tqdm` package and shows up to four different bars:
@@ -75,7 +75,7 @@ class ProgressBar(ProgressBarBase):
 
     Example:
 
-        >>> class LitProgressBar(ProgressBar):
+        >>> class LitProgressBar(TQDMProgressBar):
         ...     def init_validation_tqdm(self):
         ...         bar = super().init_validation_tqdm()
         ...         bar.set_description('running validation ...')

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -20,9 +20,9 @@
     GradientAccumulationScheduler,
     ModelCheckpoint,
     ModelSummary,
-    ProgressBar,
     ProgressBarBase,
     RichProgressBar,
+    TQDMProgressBar,
 )
 from pytorch_lightning.callbacks.rich_model_summary import RichModelSummary
 from pytorch_lightning.callbacks.timer import Timer
@@ -82,14 +82,14 @@ def on_trainer_init(
         if process_position != 0:
             rank_zero_deprecation(
                 f"Setting `Trainer(process_position={process_position})` is deprecated in v1.5 and will be removed"
-                " in v1.7. Please pass `pytorch_lightning.callbacks.progress.ProgressBar` with"
+                " in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with"
                 " `process_position` directly to the Trainer's `callbacks` argument instead."
             )
 
         if progress_bar_refresh_rate is not None:
             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"
+                " will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with"
                 " `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress"
                 " bar pass `enable_progress_bar = False` to the Trainer."
             )
@@ -230,7 +230,7 @@ def configure_progress_bar(self, refresh_rate=None, process_position=0):
         if len(progress_bars) == 1:
             progress_bar_callback = progress_bars[0]
         elif refresh_rate > 0:
-            progress_bar_callback = ProgressBar(refresh_rate=refresh_rate, process_position=process_position)
+            progress_bar_callback = TQDMProgressBar(refresh_rate=refresh_rate, process_position=process_position)
             self.trainer.callbacks.append(progress_bar_callback)
         else:
             progress_bar_callback = None

pytorch_lightning/trainer/trainer.py

@@ -293,7 +293,7 @@ def __init__(
 
                 .. deprecated:: v1.5
                     ``process_position`` has been deprecated in v1.5 and will be removed in v1.7.
-                    Please pass :class:`~pytorch_lightning.callbacks.progress.ProgressBar` with ``process_position``
+                    Please pass :class:`~pytorch_lightning.callbacks.progress.TQDMProgressBar` with ``process_position``
                     directly to the Trainer's ``callbacks`` argument instead.
 
             progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
@@ -302,7 +302,7 @@ 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``
+                    Please pass :class:`~pytorch_lightning.callbacks.progress.TQDMProgressBar` with ``refresh_rate``
                     directly to the Trainer's ``callbacks`` argument instead. To disable the progress bar,
                     pass ``enable_progress_bar = False`` to the Trainer.
 

tests/callbacks/test_progress_bar.py → tests/callbacks/test_tqdm_progress_bar.py

@@ -23,7 +23,7 @@
 from torch.utils.data.dataloader import DataLoader
 
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBar, ProgressBarBase
+from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBarBase, TQDMProgressBar
 from pytorch_lightning.callbacks.progress.tqdm_progress import Tqdm
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -37,12 +37,12 @@
         ([], None),
         ([], 1),
         ([], 2),
-        ([ProgressBar(refresh_rate=1)], 0),
-        ([ProgressBar(refresh_rate=2)], 0),
-        ([ProgressBar(refresh_rate=2)], 1),
+        ([TQDMProgressBar(refresh_rate=1)], 0),
+        ([TQDMProgressBar(refresh_rate=2)], 0),
+        ([TQDMProgressBar(refresh_rate=2)], 1),
     ],
 )
-def test_progress_bar_on(tmpdir, callbacks: list, refresh_rate: Optional[int]):
+def test_tqdm_progress_bar_on(tmpdir, callbacks: list, refresh_rate: Optional[int]):
     """Test different ways the progress bar can be turned on."""
 
     trainer = Trainer(
@@ -63,7 +63,7 @@ def test_progress_bar_on(tmpdir, callbacks: list, refresh_rate: Optional[int]):
     "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):
+def test_tqdm_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(
@@ -73,19 +73,19 @@ def test_progress_bar_off(tmpdir, callbacks: list, refresh_rate: Union[bool, int
         enable_progress_bar=enable_progress_bar,
     )
 
-    progress_bars = [c for c in trainer.callbacks if isinstance(c, ProgressBar)]
+    progress_bars = [c for c in trainer.callbacks if isinstance(c, TQDMProgressBar)]
     assert 0 == len(progress_bars)
     assert not trainer.progress_bar_callback
 
 
-def test_progress_bar_misconfiguration():
+def test_tqdm_progress_bar_misconfiguration():
     """Test that Trainer doesn't accept multiple progress bars."""
-    callbacks = [ProgressBar(), ProgressBar(), ModelCheckpoint(dirpath="../trainer")]
+    callbacks = [TQDMProgressBar(), TQDMProgressBar(), ModelCheckpoint(dirpath="../trainer")]
     with pytest.raises(MisconfigurationException, match=r"^You added multiple progress bar callbacks"):
         Trainer(callbacks=callbacks)
 
 
-def test_progress_bar_totals(tmpdir):
+def test_tqdm_progress_bar_totals(tmpdir):
     """Test that the progress finishes with the correct total steps processed."""
 
     model = BoringModel()
@@ -138,7 +138,7 @@ def test_progress_bar_totals(tmpdir):
     assert bar.test_batch_idx == k
 
 
-def test_progress_bar_fast_dev_run(tmpdir):
+def test_tqdm_progress_bar_fast_dev_run(tmpdir):
     model = BoringModel()
 
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
@@ -174,12 +174,12 @@ def test_progress_bar_fast_dev_run(tmpdir):
 
 
 @pytest.mark.parametrize("refresh_rate", [0, 1, 50])
-def test_progress_bar_progress_refresh(tmpdir, refresh_rate: int):
+def test_tqdm_progress_bar_progress_refresh(tmpdir, refresh_rate: int):
     """Test that the three progress bars get correctly updated when using different refresh rates."""
 
     model = BoringModel()
 
-    class CurrentProgressBar(ProgressBar):
+    class CurrentProgressBar(TQDMProgressBar):
 
         train_batches_seen = 0
         val_batches_seen = 0
@@ -239,7 +239,7 @@ def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, datal
 def test_num_sanity_val_steps_progress_bar(tmpdir, limit_val_batches: int):
     """Test val_progress_bar total with 'num_sanity_val_steps' Trainer argument."""
 
-    class CurrentProgressBar(ProgressBar):
+    class CurrentProgressBar(TQDMProgressBar):
         val_pbar_total = 0
         sanity_pbar_total = 0
 
@@ -271,7 +271,7 @@ def on_validation_epoch_end(self, *args):
     assert progress_bar.val_pbar_total == limit_val_batches
 
 
-def test_progress_bar_default_value(tmpdir):
+def test_tqdm_progress_bar_default_value(tmpdir):
     """Test that a value of None defaults to refresh rate 1."""
     trainer = Trainer(default_root_dir=tmpdir)
     assert trainer.progress_bar_callback.refresh_rate == 1
@@ -281,7 +281,7 @@ def test_progress_bar_default_value(tmpdir):
 
 
 @mock.patch.dict(os.environ, {"COLAB_GPU": "1"})
-def test_progress_bar_value_on_colab(tmpdir):
+def test_tqdm_progress_bar_value_on_colab(tmpdir):
     """Test that Trainer will override the default in Google COLAB."""
     trainer = Trainer(default_root_dir=tmpdir)
     assert trainer.progress_bar_callback.refresh_rate == 20
@@ -293,7 +293,7 @@ def test_progress_bar_value_on_colab(tmpdir):
     assert trainer.progress_bar_callback.refresh_rate == 19
 
 
-class MockedUpdateProgressBars(ProgressBar):
+class MockedUpdateProgressBars(TQDMProgressBar):
     """Mocks the update method once bars get initializied."""
 
     def _mock_bar_update(self, bar):
@@ -428,10 +428,10 @@ def predict_step(self, *args, **kwargs):
 
 
 @mock.patch("pytorch_lightning.callbacks.progress.tqdm_progress.Tqdm.write")
-def test_progress_bar_print(tqdm_write, tmpdir):
+def test_tqdm_progress_bar_print(tqdm_write, tmpdir):
     """Test that printing in the LightningModule redirects arguments to the progress bar."""
     model = PrintModel()
-    bar = ProgressBar()
+    bar = TQDMProgressBar()
     trainer = Trainer(
         default_root_dir=tmpdir,
         num_sanity_val_steps=0,
@@ -455,10 +455,10 @@ def test_progress_bar_print(tqdm_write, tmpdir):
 
 
 @mock.patch("pytorch_lightning.callbacks.progress.tqdm_progress.Tqdm.write")
-def test_progress_bar_print_no_train(tqdm_write, tmpdir):
+def test_tqdm_progress_bar_print_no_train(tqdm_write, tmpdir):
     """Test that printing in the LightningModule redirects arguments to the progress bar without training."""
     model = PrintModel()
-    bar = ProgressBar()
+    bar = TQDMProgressBar()
     trainer = Trainer(
         default_root_dir=tmpdir,
         num_sanity_val_steps=0,
@@ -482,10 +482,10 @@ def test_progress_bar_print_no_train(tqdm_write, tmpdir):
 
 @mock.patch("builtins.print")
 @mock.patch("pytorch_lightning.callbacks.progress.tqdm_progress.Tqdm.write")
-def test_progress_bar_print_disabled(tqdm_write, mock_print, tmpdir):
+def test_tqdm_progress_bar_print_disabled(tqdm_write, mock_print, tmpdir):
     """Test that printing in LightningModule goes through built-in print function when progress bar is disabled."""
     model = PrintModel()
-    bar = ProgressBar()
+    bar = TQDMProgressBar()
     trainer = Trainer(
         default_root_dir=tmpdir,
         num_sanity_val_steps=0,
@@ -507,8 +507,8 @@ def test_progress_bar_print_disabled(tqdm_write, mock_print, tmpdir):
     tqdm_write.assert_not_called()
 
 
-def test_progress_bar_can_be_pickled():
-    bar = ProgressBar()
+def test_tqdm_progress_bar_can_be_pickled():
+    bar = TQDMProgressBar()
     trainer = Trainer(fast_dev_run=True, callbacks=[bar], max_steps=1)
     model = BoringModel()
 
@@ -522,14 +522,14 @@ def test_progress_bar_can_be_pickled():
 
 
 @RunIf(min_gpus=2, special=True)
-def test_progress_bar_max_val_check_interval_0(tmpdir):
+def test_tqdm_progress_bar_max_val_check_interval_0(tmpdir):
     _test_progress_bar_max_val_check_interval(
         tmpdir, total_train_samples=8, train_batch_size=4, total_val_samples=2, val_batch_size=1, val_check_interval=0.2
     )
 
 
 @RunIf(min_gpus=2, special=True)
-def test_progress_bar_max_val_check_interval_1(tmpdir):
+def test_tqdm_progress_bar_max_val_check_interval_1(tmpdir):
     _test_progress_bar_max_val_check_interval(
         tmpdir, total_train_samples=8, train_batch_size=4, total_val_samples=2, val_batch_size=1, val_check_interval=0.5
     )
@@ -567,7 +567,7 @@ def _test_progress_bar_max_val_check_interval(
 
 
 def test_get_progress_bar_metrics(tmpdir: str):
-    class TestProgressBar(ProgressBar):
+    class TestProgressBar(TQDMProgressBar):
         def get_metrics(self, trainer: Trainer, model: LightningModule):
             items = super().get_metrics(trainer, model)
             items.pop("v_num", None)
@@ -588,9 +588,9 @@ def get_metrics(self, trainer: Trainer, model: LightningModule):
     assert "v_num" not in standard_metrics.keys()
 
 
-def test_progress_bar_main_bar_resume():
+def test_tqdm_progress_bar_main_bar_resume():
     """Test that the progress bar can resume its counters based on the Trainer state."""
-    bar = ProgressBar()
+    bar = TQDMProgressBar()
     trainer = Mock()
     model = Mock()
 

tests/deprecated_api/test_remove_1-7.py

@@ -20,6 +20,7 @@
 from pytorch_lightning import Callback, LightningDataModule, Trainer
 from pytorch_lightning.callbacks.gpu_stats_monitor import GPUStatsMonitor
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
+from pytorch_lightning.callbacks.progress import ProgressBar
 from pytorch_lightning.callbacks.xla_stats_monitor import XLAStatsMonitor
 from pytorch_lightning.loggers import LoggerCollection, TestTubeLogger
 from tests.callbacks.test_callbacks import OldStatefulCallback
@@ -391,6 +392,11 @@ def test_v1_7_0_deprecate_xla_stats_monitor(tmpdir):
         _ = XLAStatsMonitor()
 
 
+def test_v1_7_0_progress_bar():
+    with pytest.deprecated_call(match="has been deprecated in v1.5 and will be removed in v1.7."):
+        _ = ProgressBar()
+
+
 def test_v1_7_0_deprecated_max_steps_none(tmpdir):
     with pytest.deprecated_call(match="`max_steps = None` is deprecated in v1.5"):
         _ = Trainer(max_steps=None)