RichProgressBar

class deeplay.callbacks.progress.RichProgressBar(refresh_rate: int = 1, leave: bool = False, theme: RichProgressBarTheme = RichProgressBarTheme(description='', progress_bar='#6206E0', progress_bar_finished='#6206E0', progress_bar_pulse='#6206E0', batch_progress='', time='dim', processing_speed='dim underline', metrics='italic', metrics_text_delimiter=' ', metrics_format='.3g'), console_kwargs=None)

Bases: RichProgressBar

A progress bar for displaying training progress with Rich.

This class enhances the standard Lightning RichProgressBar by supporting customizable themes and console options. It includes an environment-specific adjustment to prevent potential crashes on platforms like Colab and Kaggle.

Parameters

refresh_rateint, optional

The refresh rate of the progress bar, by default 1.

leavebool, optional

Whether to leave the progress bar on the screen after completion, by default False.

themeRichProgressBarTheme, optional

The theme used for the Rich progress bar, by default RichProgressBarTheme(metrics_format=”.3g”).

console_kwargsdict, optional

Additional keyword arguments for configuring the Rich console, by default None.

Example

This example demosntrate the use of the standard TQDM progress bar:

```python import deeplay as dl import torch

# Create training dataset. num_samples = 10 ** 4 data = torch.randn(num_samples, 2) labels = (data.sum(dim=1) > 0).long()

dataset = torch.utils.data.TensorDataset(data, labels) dataloader = dl.DataLoader(dataset, batch_size=16, shuffle=True)

# Create neural network and classifier application. mlp = dl.MediumMLP(in_features=2, out_features=2) classifier = dl.Classifier(mlp, optimizer=dl.Adam(), num_classes=2).build()

# Train neural network with progress bar. rich_bar = dl.callbacks.RichProgressBar(refresh_rate=100) trainer = dl.Trainer(max_epochs=100, callbacks=[rich_bar]) trainer.fit(classifier, dataloader) ```