139 lines
5.0 KiB
ReStructuredText
139 lines
5.0 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.trainer.trainer import Trainer
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.. _progress_bar:
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Progress Bar
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============
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Lightning supports two different types of progress bars (`tqdm <https://github.com/tqdm/tqdm>`_ and `rich <https://github.com/Textualize/rich>`_). :class:`~pytorch_lightning.callbacks.TQDMProgressBar` is used by default,
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but you can override it by passing a custom :class:`~pytorch_lightning.callbacks.TQDMProgressBar` or :class:`~pytorch_lightning.callbacks.RichProgressBar` to the ``callbacks`` argument of the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
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You could also use the :class:`~pytorch_lightning.callbacks.ProgressBarBase` class to implement your own progress bar.
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-------------
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TQDMProgressBar
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---------------
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The :class:`~pytorch_lightning.callbacks.TQDMProgressBar` uses the `tqdm <https://github.com/tqdm/tqdm>`_ library internally and is the default progress bar used by Lightning.
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It prints to ``stdout`` and shows up to four different bars:
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- **sanity check progress:** the progress during the sanity check run
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- **main progress:** shows training + validation progress combined. It also accounts for multiple validation runs during training when :paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval` is used.
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- **validation progress:** only visible during validation; shows total progress over all validation datasets.
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- **test progress:** only active when testing; shows total progress over all test datasets.
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For infinite datasets, the progress bar never ends.
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You can update ``refresh_rate`` (rate (number of batches) at which the progress bar get updated) for :class:`~pytorch_lightning.callbacks.TQDMProgressBar` by:
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.. code-block:: python
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from pytorch_lightning.callbacks import TQDMProgressBar
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trainer = Trainer(callbacks=[TQDMProgressBar(refresh_rate=10)])
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If you want to customize the default :class:`~pytorch_lightning.callbacks.TQDMProgressBar` used by Lightning, you can override
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specific methods of the callback class and pass your custom implementation to the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
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.. code-block:: python
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class LitProgressBar(TQDMProgressBar):
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def init_validation_tqdm(self):
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bar = super().init_validation_tqdm()
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bar.set_description("running validation...")
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return bar
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trainer = Trainer(callbacks=[LitProgressBar()])
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.. seealso::
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- :class:`~pytorch_lightning.callbacks.TQDMProgressBar` docs.
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- `tqdm library <https://github.com/tqdm/tqdm>`__
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----------------
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RichProgressBar
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---------------
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`Rich <https://github.com/Textualize/rich>`_ is a Python library for rich text and beautiful formatting in the terminal.
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To use the :class:`~pytorch_lightning.callbacks.RichProgressBar` as your progress bar, first install the package:
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.. code-block:: bash
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pip install rich
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Then configure the callback and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. code-block:: python
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from pytorch_lightning.callbacks import RichProgressBar
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trainer = Trainer(callbacks=[RichProgressBar()])
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Customize the theme for your :class:`~pytorch_lightning.callbacks.RichProgressBar` like this:
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.. code-block:: python
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from pytorch_lightning.callbacks import RichProgressBar
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from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBarTheme
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# create your own theme!
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progress_bar = RichProgressBar(
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theme=RichProgressBarTheme(
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description="green_yellow",
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progress_bar="green1",
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progress_bar_finished="green1",
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progress_bar_pulse="#6206E0",
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batch_progress="green_yellow",
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time="grey82",
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processing_speed="grey82",
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metrics="grey82",
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)
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)
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trainer = Trainer(callbacks=progress_bar)
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You can customize the components used within :class:`~pytorch_lightning.callbacks.RichProgressBar` with ease by overriding the
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:func:`~pytorch_lightning.callbacks.RichProgressBar.configure_columns` method.
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.. code-block:: python
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from rich.progress import TextColumn
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custom_column = TextColumn("[progress.description]Custom Rich Progress Bar!")
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class CustomRichProgressBar(RichProgressBar):
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def configure_columns(self, trainer):
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return [custom_column]
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progress_bar = CustomRichProgressBar()
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If you wish for a new progress bar to be displayed at the end of every epoch, you should enable
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:paramref:`RichProgressBar.leave <pytorch_lightning.callbacks.RichProgressBar.leave>` by passing ``True``
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.. code-block:: python
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from pytorch_lightning.callbacks import RichProgressBar
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trainer = Trainer(callbacks=[RichProgressBar(leave=True)])
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.. seealso::
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- :class:`~pytorch_lightning.callbacks.RichProgressBar` docs.
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- :class:`~pytorch_lightning.callbacks.RichModelSummary` docs to customize the model summary table.
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- `Rich library <https://github.com/Textualize/rich>`__.
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.. note::
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Progress bar is automatically enabled with the Trainer, and to disable it, one should do this:
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.. code-block:: python
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trainer = Trainer(enable_progress_bar=False)
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