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for #330, use tqdm.auto in trainer (#752)

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* use tqdm.auto in trainer

This will import the ipywidgets version of tqdm if available. This works nicely in notebooks by not filling up the log.

In the terminal it will use the same old tqdm.

We might also want to consider passing in the tqdm we want as an argument since there may be some edge cases where ipywidgets is available but the interface doesn't support it (e.g. vscode?) or isn't working. In which case people will get a warning message, but may want to configure it themselves.

* use `from tqdm.auto` in eval loop

* indents
This commit is contained in:
Mike Clark
2020-01-26 10:19:09 -05:00
committed by William Falcon
parent 7deec2c14e
commit deffbaba7f
2 changed files with 10 additions and 10 deletions
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pytorch_lightning/trainer/evaluation_loop.py
+4 -4
View File
@@ -127,7 +127,7 @@ import sys
from abc import ABC, abstractmethod
import torch
import tqdm
from tqdm.auto import tqdm
from pytorch_lightning.utilities.debugging import MisconfigurationException
@@ -293,9 +293,9 @@ class TrainerEvaluationLoopMixin(ABC):
# main progress bar will already be closed when testing so initial position is free
position = 2 * self.process_position + (not test)
desc = 'Testing' if test else 'Validating'
pbar = tqdm.tqdm(desc=desc, total=max_batches, leave=test, position=position,
disable=not self.show_progress_bar, dynamic_ncols=True,
unit='batch', file=sys.stdout)
pbar = tqdm(desc=desc, total=max_batches, leave=test, position=position,
disable=not self.show_progress_bar, dynamic_ncols=True,
unit='batch', file=sys.stdout)
setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)
# run evaluation
pytorch_lightning/trainer/trainer.py
+6 -6
View File
@@ -7,7 +7,7 @@ import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import tqdm
from tqdm.auto import tqdm
from torch.optim.optimizer import Optimizer
from pytorch_lightning.trainer.auto_mix_precision import TrainerAMPMixin
@@ -850,13 +850,13 @@ class Trainer(TrainerIOMixin,
ref_model.on_train_start()
if not self.disable_validation and self.num_sanity_val_steps > 0:
# init progress bars for validation sanity check
pbar = tqdm.tqdm(desc='Validation sanity check',
pbar = tqdm(desc='Validation sanity check',
total=self.num_sanity_val_steps * len(self.get_val_dataloaders()),
leave=False, position=2 * self.process_position,
disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch')
self.main_progress_bar = pbar
# dummy validation progress bar
self.val_progress_bar = tqdm.tqdm(disable=True)
self.val_progress_bar = tqdm(disable=True)
eval_results = self.evaluate(model, self.get_val_dataloaders(),
self.num_sanity_val_steps, False)
@@ -870,9 +870,9 @@ class Trainer(TrainerIOMixin,
self.early_stop_callback.check_metrics(callback_metrics)
# init progress bar
pbar = tqdm.tqdm(leave=True, position=2 * self.process_position,
disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
file=sys.stdout)
pbar = tqdm(leave=True, position=2 * self.process_position,
disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
file=sys.stdout)
self.main_progress_bar = pbar
# clear cache before training