task: metadata: install: pip install -e .
patch: diff --git a/ignite/contrib/handlers/tqdm_logger.py b/ignite/contrib/handlers/tqdm_logger.py
index b2d41e6b..9e18f420 100644
--- a/ignite/contrib/handlers/tqdm_logger.py
+++ b/ignite/contrib/handlers/tqdm_logger.py
@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
-import numbers
 import warnings
 
 import torch
@@ -53,7 +52,7 @@ class ProgressBar(BaseLogger):
             pbar.attach(trainer, ['loss'])
 
             # Progress bar will looks like
-            # Epoch [2/50]: [64/128]  50%|█████      , loss=12.34e-02 [06:17<12:34]
+            # Epoch [2/50]: [64/128]  50%|█████      , loss=0.123 [06:17<12:34]
 
         Directly attach the engine's output
 
@@ -65,7 +64,7 @@ class ProgressBar(BaseLogger):
             pbar.attach(trainer, output_transform=lambda x: {'loss': x})
 
             # Progress bar will looks like
-            # Epoch [2/50]: [64/128]  50%|█████      , loss=12.34e-02 [06:17<12:34]
+            # Epoch [2/50]: [64/128]  50%|█████      , loss=0.123 [06:17<12:34]
 
     Note:
         When adding attaching the progress bar to an engine, it is recommend that you replace
@@ -91,6 +90,7 @@ class ProgressBar(BaseLogger):
 
     def __init__(self, persist=False,
                  bar_format='{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]',
+
                  **tqdm_kwargs):
 
         try:
@@ -226,18 +226,18 @@ class _OutputHandler(BaseOutputHandler):
 
         rendered_metrics = {}
         for key, value in metrics.items():
-            if isinstance(value, numbers.Number) or \
-                    isinstance(value, torch.Tensor) and value.ndimension() == 0:
-                rendered_metrics[key] = "{:.2e}".format(value)
-            elif isinstance(value, torch.Tensor) and value.ndimension() == 1:
-                for i, v in enumerate(value):
-                    k = "{}_{}".format(key, i)
-                    rendered_metrics[k] = "{:.2e}".format(v)
-            elif isinstance(value, str):
-                rendered_metrics[key] = value
+            if isinstance(value, torch.Tensor):
+                if value.ndimension() == 0:
+                    rendered_metrics[key] = value.item()
+                elif value.ndimension() == 1:
+                    for i, v in enumerate(value):
+                        k = "{}_{}".format(key, i)
+                        rendered_metrics[k] = v.item()
+                else:
+                    warnings.warn("ProgressBar can not log "
+                                  "tensor with {} dimensions".format(value.ndimension()))
             else:
-                warnings.warn("ProgressBar can not log "
-                              "metrics value type {}".format(type(value)))
+                rendered_metrics[key] = value
 
         if rendered_metrics:
             logger.pbar.set_postfix(**rendered_metrics)

problem_statement: Improve TQDM, progress bar value formatting
Idea is to use tqdm's value renderer instead of using current implementation. Newer version will looks like:
```python
import tqdm
with tqdm.tqdm() as pbar:
    pbar.set_postfix({'a': 123, 'b': 12.3456788, 'd': 0.12345, 'c': torch.tensor(123).item(), 'e': 'text', 'f': lambda x: x})
```
out:
```
0it [00:00, ?it/s, a=123, b=12.3, d=0.123, c=123, e=text, f=<function <lambda> at 0x1234>]
```

This will help to better display integer values as `123` instead of `1.23e+02`.

cc @miguelvr 
repo: pytorch/ignite
test_cmd: pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
test_patch: diff --git a/tests/ignite/contrib/handlers/test_tqdm_logger.py b/tests/ignite/contrib/handlers/test_tqdm_logger.py
index f5d8b0fa..b6fd4f1d 100644
--- a/tests/ignite/contrib/handlers/test_tqdm_logger.py
+++ b/tests/ignite/contrib/handlers/test_tqdm_logger.py
@@ -31,7 +31,7 @@ def test_pbar(capsys):
     err = captured.err.split('\r')
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
-    expected = u'Epoch [2/2]: [1/2]  50%|█████     , a=1.00e+00 [00:00<00:00]'
+    expected = u'Epoch [2/2]: [1/2]  50%|█████     , a=1 [00:00<00:00]'
     assert err[-1] == expected
 
 
@@ -79,7 +79,7 @@ def test_pbar_with_metric(capsys):
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
     actual = err[-1]
-    expected = u'Epoch: [1/2]  50%|█████     , batchloss=5.00e-01 [00:00<00:00]'
+    expected = u'Epoch: [1/2]  50%|█████     , batchloss=0.5 [00:00<00:00]'
     assert actual == expected
 
 
@@ -110,7 +110,7 @@ def test_pbar_with_all_metric(capsys):
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
     actual = err[-1]
-    expected = u'Epoch: [1/2]  50%|█████     , another batchloss=1.50e+00, batchloss=5.00e-01 [00:00<00:00]'
+    expected = u'Epoch: [1/2]  50%|█████     , another batchloss=1.5, batchloss=0.5 [00:00<00:00]'
     assert actual == expected
 
 
@@ -148,7 +148,7 @@ def test_pbar_with_output(capsys):
     err = captured.err.split('\r')
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
-    expected = u'Epoch [2/2]: [1/2]  50%|█████     , a=1.00e+00 [00:00<00:00]'
+    expected = u'Epoch [2/2]: [1/2]  50%|█████     , a=1 [00:00<00:00]'
     assert err[-1] == expected
 
 
@@ -174,7 +174,7 @@ def test_pbar_with_scalar_output(capsys):
     err = captured.err.split('\r')
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
-    expected = u'Epoch [2/2]: [1/2]  50%|█████     , output=1.00e+00 [00:00<00:00]'
+    expected = u'Epoch [2/2]: [1/2]  50%|█████     , output=1 [00:00<00:00]'
     assert err[-1] == expected
 
 
@@ -209,7 +209,7 @@ def test_pbar_with_tqdm_kwargs(capsys):
     err = captured.err.split('\r')
     err = list(map(lambda x: x.strip(), err))
     err = list(filter(None, err))
-    expected = u'My description:  [10/10]: [4/5]  80%|████████  , output=1.00e+00 [00:00<00:00]'.format()
+    expected = u'My description:  [10/10]: [4/5]  80%|████████  , output=1 [00:00<00:00]'.format()
     assert err[-1] == expected
 
 
@@ -230,30 +230,36 @@ def test_pbar_for_validation(capsys):
 
 
 def test_pbar_output_tensor(capsys):
-    loader = [1, 2, 3, 4, 5]
 
-    def update_fn(engine, batch):
-        return torch.Tensor([batch, 0])
+    def _test(out_tensor, out_msg):
+        loader = [1, 2, 3, 4, 5]
 
-    engine = Engine(update_fn)
+        def update_fn(engine, batch):
+            return out_tensor
 
-    pbar = ProgressBar(desc="Output tensor")
-    pbar.attach(engine, output_transform=lambda x: x)
-    engine.run(loader, max_epochs=1)
+        engine = Engine(update_fn)
 
-    captured = capsys.readouterr()
-    err = captured.err.split('\r')
-    err = list(map(lambda x: x.strip(), err))
-    err = list(filter(None, err))
-    expected = u'Output tensor: [4/5]  80%|████████  , output_0=5.00e+00, output_1=0.00e+00 [00:00<00:00]'
-    assert err[-1] == expected
+        pbar = ProgressBar(desc="Output tensor")
+        pbar.attach(engine, output_transform=lambda x: x)
+        engine.run(loader, max_epochs=1)
+
+        captured = capsys.readouterr()
+        err = captured.err.split('\r')
+        err = list(map(lambda x: x.strip(), err))
+        err = list(filter(None, err))
+        expected = u'Output tensor: [4/5]  80%|████████  , {} [00:00<00:00]'.format(out_msg)
+        assert err[-1] == expected
+
+    _test(out_tensor=torch.tensor([5, 0]), out_msg="output_0=5, output_1=0")
+    _test(out_tensor=torch.tensor(123), out_msg="output=123")
+    _test(out_tensor=torch.tensor(1.234), out_msg="output=1.23")
 
 
 def test_pbar_output_warning(capsys):
     loader = [1, 2, 3, 4, 5]
 
     def update_fn(engine, batch):
-        return batch, "abc"
+        return torch.zeros(1, 2, 3, 4)
 
     engine = Engine(update_fn)