RichProgressBar¶
- class lightning.pytorch.callbacks.RichProgressBar(refresh_rate=1, leave=False, theme=RichProgressBarTheme(description='white', progress_bar='#6206E0', progress_bar_finished='#6206E0', progress_bar_pulse='#6206E0', batch_progress='white', time='grey54', processing_speed='grey70', metrics='white', metrics_text_delimiter=' ', metrics_format='.3f'), console_kwargs=None)[source]¶
Bases:
ProgressBar
Create a progress bar with rich text formatting.
Install it with pip:
pip install rich
from lightning.pytorch import Trainer from lightning.pytorch.callbacks import RichProgressBar trainer = Trainer(callbacks=RichProgressBar())
- Parameters:
refresh_rate¶ (
int
) – Determines at which rate (in number of batches) the progress bars get updated. Set it to0
to disable the display.leave¶ (
bool
) – Leaves the finished progress bar in the terminal at the end of the epoch. Default: Falsetheme¶ (
RichProgressBarTheme
) – Contains styles used to stylize the progress bar.console_kwargs¶ (
Optional
[Dict
[str
,Any
]]) – Args for constructing a Console
- Raises:
ModuleNotFoundError – If required rich package is not installed on the device.
Note
PyCharm users will need to enable “emulate terminal” in output console option in run/debug configuration to see styled output. Reference: https://rich.readthedocs.io/en/latest/introduction.html#requirements
- enable()[source]¶
You should provide a way to enable the progress bar.
The
Trainer
will call this in e.g. pre-training routines like the learning rate finder. to temporarily enable and disable the training progress bar.- Return type:
- on_exception(trainer, pl_module, exception)[source]¶
Called when any trainer execution is interrupted by an exception.
- Return type:
- on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the predict batch ends.
- Return type:
- on_predict_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the predict batch begins.
- Return type:
- on_sanity_check_end(trainer, pl_module)[source]¶
Called when the validation sanity check ends.
- Return type:
- on_sanity_check_start(trainer, pl_module)[source]¶
Called when the validation sanity check starts.
- Return type:
- on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the test batch ends.
- Return type:
- on_test_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the test batch begins.
- Return type:
- on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)[source]¶
Called when the train batch ends. :rtype:
None
Note
The value
outputs["loss"]
here will be the normalized value w.r.taccumulate_grad_batches
of the loss returned fromtraining_step
.
- on_train_epoch_end(trainer, pl_module)[source]¶
Called when the train epoch ends.
To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the
lightning.pytorch.core.LightningModule
and access them in this hook:class MyLightningModule(L.LightningModule): def __init__(self): super().__init__() self.training_step_outputs = [] def training_step(self): loss = ... self.training_step_outputs.append(loss) return loss class MyCallback(L.Callback): def on_train_epoch_end(self, trainer, pl_module): # do something with all training_step outputs, for example: epoch_mean = torch.stack(pl_module.training_step_outputs).mean() pl_module.log("training_epoch_mean", epoch_mean) # free up the memory pl_module.training_step_outputs.clear()
- Return type:
- on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the validation batch ends.
- Return type:
- on_validation_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx=0)[source]¶
Called when the validation batch begins.
- Return type: