Prevent flickering progress bar

CHANGELOG.md

@@ -297,6 +297,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed missing `process_dataloader` call for `TPUSpawn` when in distributed mode ([#6015](https://github.com/PyTorchLightning/pytorch-lightning/pull/6015))
 
 
+- Fixed progress bar flickering by appending 0 to floats/strings ([#6009](https://github.com/PyTorchLightning/pytorch-lightning/pull/6009))
+
+
 - Fixed synchronization issues with TPU training ([#6027](https://github.com/PyTorchLightning/pytorch-lightning/pull/6027))
 
 

pytorch_lightning/callbacks/progress.py

@@ -26,12 +26,37 @@
 from typing import Optional, Union
 
 if importlib.util.find_spec('ipywidgets') is not None:
-    from tqdm.auto import tqdm
+    from tqdm.auto import tqdm as _tqdm
 else:
-    from tqdm import tqdm
+    from tqdm import tqdm as _tqdm
 
 from pytorch_lightning.callbacks import Callback
 
+_PAD_SIZE = 5
+
+
+class tqdm(_tqdm):
+    """
+    Custom tqdm progressbar where we append 0 to floating points/strings to
+    prevent the progress bar from flickering
+    """
+
+    @staticmethod
+    def format_num(n) -> str:
+        """ Add additional padding to the formatted numbers """
+        should_be_padded = isinstance(n, (float, str))
+        if not isinstance(n, str):
+            n = _tqdm.format_num(n)
+        if should_be_padded and 'e' not in n:
+            if '.' not in n and len(n) < _PAD_SIZE:
+                try:
+                    _ = float(n)
+                except ValueError:
+                    return n
+                n += '.'
+            n += "0" * (_PAD_SIZE - len(n))
+        return n
+
 
 class ProgressBarBase(Callback):
     r"""

tests/callbacks/test_progress_bar.py

@@ -20,6 +20,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBar, ProgressBarBase
+from pytorch_lightning.callbacks.progress import tqdm
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 
@@ -371,3 +372,12 @@ def training_step(self, batch, batch_idx):
     pbar = trainer.progress_bar_callback.main_progress_bar
     actual = str(pbar.postfix)
     assert actual.endswith("foo=0.123, bar={'baz': tensor([1])}")
+
+
+@pytest.mark.parametrize(
+    "input_num, expected", [[1, '1'], [1.0, '1.000'], [0.1, '0.100'], [1e-3, '0.001'], [1e-5, '1e-5'], ['1.0', '1.000'],
+                            ['10000', '10000'], ['abc', 'abc']]
+)
+def test_tqdm_format_num(input_num, expected):
+    """ Check that the specialized tqdm.format_num appends 0 to floats and strings """
+    assert tqdm.format_num(input_num) == expected