Table of Contents
Shortcuts

tqdm_logger#

TQDM logger.

Classes

ProgressBar

TQDM progress bar handler to log training progress and computed metrics.
class ignite.contrib.handlers.tqdm_logger.ProgressBar(persist=False, bar_format='{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]', **tqdm_kwargs)[source]#

TQDM progress bar handler to log training progress and computed metrics.

Parameters

  • persist (bool) – set to True to persist the progress bar after completion (default = False)

  • bar_format (str) – Specify a custom bar string formatting. May impact performance. [default: ‘{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]’]. Set to None to use tqdm default bar formatting: ‘{l_bar}{bar}{r_bar}’, where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]’. For more details on the formatting, see tqdm docs.

  • tqdm_kwargs (Any) – kwargs passed to tqdm progress bar. By default, progress bar description displays “Epoch [5/10]” where 5 is the current epoch and 10 is the number of epochs; however, if max_epochs are set to 1, the progress bar instead displays “Iteration: [5/10]”. If tqdm_kwargs defines desc, e.g. “Predictions”, than the description is “Predictions [5/10]” if number of epochs is more than one otherwise it is simply “Predictions”.

Examples

Simple progress bar

trainer = create_supervised_trainer(model, optimizer, loss)

pbar = ProgressBar()
pbar.attach(trainer)

# Progress bar will looks like
# Epoch [2/50]: [64/128]  50%|█████      [06:17<12:34]

Log output to a file instead of stderr (tqdm’s default output)

trainer = create_supervised_trainer(model, optimizer, loss)

log_file = open("output.log", "w")
pbar = ProgressBar(file=log_file)
pbar.attach(trainer)

Attach metrics that already have been computed at ITERATION_COMPLETED (such as RunningAverage)

trainer = create_supervised_trainer(model, optimizer, loss)

RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')

pbar = ProgressBar()
pbar.attach(trainer, ['loss'])

# Progress bar will looks like
# Epoch [2/50]: [64/128]  50%|█████      , loss=0.123 [06:17<12:34]

Directly attach the engine’s output

trainer = create_supervised_trainer(model, optimizer, loss)

pbar = ProgressBar()
pbar.attach(trainer, output_transform=lambda x: {'loss': x})

# Progress bar will looks like
# 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 every print operation in the engine’s handlers triggered every iteration with pbar.log_message to guarantee the correct format of the stdout.

Note

When using inside jupyter notebook, ProgressBar automatically uses tqdm_notebook. For correct rendering, please install ipywidgets. Due to tqdm notebook bugs, bar format may be needed to be set to an empty string value.

attach(engine, metric_names=None, output_transform=None, event_name=Events.ITERATION_COMPLETED, closing_event_name=Events.EPOCH_COMPLETED)[source]#

Attaches the progress bar to an engine object.

Parameters

  • engine (Engine) – engine object.

  • metric_names (Optional[Union[str, List[str]]]) – list of metric names to plot or a string “all” to plot all available metrics.

  • output_transform (Optional[Callable]) – a function to select what you want to print from the engine’s output. This function may return either a dictionary with entries in the format of {name: value}, or a single scalar, which will be displayed with the default name output.

  • event_name (Union[Events, CallableEventWithFilter]) – event’s name on which the progress bar advances. Valid events are from Events.

  • closing_event_name (Union[Events, CallableEventWithFilter]) – event’s name on which the progress bar is closed. Valid events are from Events.

Return type

None

Note

Accepted output value types are numbers, 0d and 1d torch tensors and strings.

attach_opt_params_handler(engine, event_name, *args, **kwargs)[source]#

Intentionally empty

Parameters
Return type

RemovableEventHandle

log_message(message)[source]#

Logs a message, preserving the progress bar correct output format.

Parameters

message (str) – string you wish to log.

Return type

None