[💡SUG] Add progress bar to training an epoch

[tszumowski]

Is your feature request related to a problem? Please describe.
When training on large datasets, it's difficult to know how far along training is and whether or not I should stop training to tweak parameters to speed up the training process.

Describe the solution you'd like
Add tqdm progress bar to training progress. See context section. I have a suggested patch to trainer.py that adds this. Happy to PR it in, or feel free to take as you see appropriate.

Describe alternatives you've considered
One alternative is a manual print. But tqdm is the standard for this typically.

Additional context

trainer.py.patch.txt

I attached a patch file that adds an optional show_progress argument in a similar manner to what's used in DataLoader already. Note: This patch only shows it for the Trainer class, not the child classes. That is because this is for an example only.

The main change is this:

-        for batch_idx, interaction in enumerate(train_data):
+        iter_data = (
+            tqdm(
+                enumerate(train_data),
+                total=len(train_data),
+                desc=f"Train {epoch_idx:>5}",
+            )
+            if show_progress
+            else enumerate(train_data)
+        )
+        for batch_idx, interaction in iter_data:

Here is an example output while training a larger dataset. Scroll right to see full output.

Trainable parameters: 735267
Train     0:  20%|███████████████████████████▌                                                                                                           | 6480/31756 [10:47<42:09,  9.99it/s]

[chenyushuo]

Thanks for your insightful commment. Using tqdm is a good way to add this feature. We'd appreciate it if you would like to PR it in branch 0.1.x. By the way, any PRs are welcome.
Note: trainer.py has been refactor in #521 , be careful to add this feature.

[adebiasio-estilos]

when running the code via jupyter notebook, tqdm print all the time new lines