---
title: Track and Visualize Experiments (expert)
description: >-
  Audience: Users who want to make their own progress bars or integrate new
  experiment managers.
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/visualize/logging_expert'
---
# Track and Visualize Experiments (expert) {#track-and-visualize-experiments-expert}

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

## Change the progress bar {#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 {#use-the-tqdmprogressbar}

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

```python
from lightning.pytorch.callbacks import TQDMProgressBar

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

### Use the RichProgressBar {#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

```bash
pip install rich
```

Then pass the callback into the callbacks `Trainer` argument:

```python
from lightning.pytorch.callbacks import RichProgressBar

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

The rich progress bar can also have custom themes

```python
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 {#customize-a-progress-bar}

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

```python
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 {#build-your-own-progress-bar}

To build your own progress bar, subclass `ProgressBar`

```python
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.flush()
        sys.stdout.write(f"{percent:.01f} percent complete \r")

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

## Integrate an experiment manager {#integrate-an-experiment-manager}

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

```python
from lightning.pytorch.loggers import Logger

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

    @property
    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)
```

---

**Navigation**

- Parent: [Visualize](/docs/pytorch/2.6.6/reference/visualize.md)