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Track and Visualize Experiments (expert)

Audience: Users who want to make their own progress bars or integrate new experiment managers.

Change the progress bar

If you’d like to change the way the progress bar displays information you can use some of our built-in progress bard or build your own.

Use the TQDMProgressBar

To use the TQDMProgressBar pass it into the callbacks Trainer argument.

from lightning.pytorch.callbacks import TQDMProgressBar

trainer = Trainer(callbacks=[TQDMProgressBar()])

Use the RichProgressBar

The RichProgressBar can add custom colors and beautiful formatting for your progress bars. First, install the `rich <https://github.com/Textualize/rich>`_ library

pip install rich

Then pass the callback into the callbacks Trainer argument:

from lightning.pytorch.callbacks import RichProgressBar

trainer = Trainer(callbacks=[RichProgressBar()])

The rich progress bar can also have custom themes

from lightning.pytorch.callbacks import RichProgressBar
from lightning.pytorch.callbacks.progress.rich_progress import RichProgressBarTheme

# create your own theme!
theme = RichProgressBarTheme(description="green_yellow", progress_bar="green1")

# init as normal
progress_bar = RichProgressBar(theme=theme)
trainer = Trainer(callbacks=progress_bar)

Customize a progress bar

To customize either the TQDMProgressBar or the RichProgressBar, subclass it and override any of its methods.

from lightning.pytorch.callbacks import TQDMProgressBar

class LitProgressBar(TQDMProgressBar):
    def init_validation_tqdm(self):
        bar = super().init_validation_tqdm()
        bar.set_description("running validation...")
        return bar

Build your own progress bar

To build your own progress bar, subclass ProgressBar

from lightning.pytorch.callbacks import ProgressBar

class LitProgressBar(ProgressBar):
    def __init__(self):
        super().__init__()  # don't forget this :)
        self.enable = True

    def disable(self):
        self.enable = False

    def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx):
        super().on_train_batch_end(trainer, pl_module, outputs, batch_idx)  # don't forget this :)
        percent = (self.train_batch_idx / self.total_train_batches) * 100
        sys.stdout.write(f"{percent:.01f} percent complete \r")

bar = LitProgressBar()
trainer = Trainer(callbacks=[bar])

Integrate an experiment manager

To create an integration between a custom logger and Lightning, subclass LightningLoggerBase

from lightning.pytorch.loggers import Logger

class LitLogger(Logger):
    def name(self) -> str:
        return "my-experiment"

    def version(self):
        return "version_0"

    def log_metrics(self, metrics, step=None):
        print("my logged metrics", metrics)

    def log_hyperparams(self, params, *args, **kwargs):
        print("my logged hyperparameters", params)

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