---
title: Track and Visualize Experiments (basic)
description: 'Audience: Users who want to visualize and monitor their model development'
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/visualize/logging_basic'
---
# Track and Visualize Experiments (basic) {#track-and-visualize-experiments-basic}

**Audience:** Users who want to visualize and monitor their model development

## Why do I need to track metrics? {#why-do-i-need-to-track-metrics}

In model development, we track values of interest such as the *validation_loss* to visualize the learning process for our models. Model development is like driving a car without windows, charts and logs provide the *windows* to know where to drive the car.

With Lightning, you can visualize virtually anything you can think of: numbers, text, images, audio. Your creativity and imagination are the only limiting factor.

## Track metrics {#track-metrics}

Metric visualization is the most basic but powerful way of understanding how your model is doing throughout the model development process.

To track a metric, simply use the *self.log* method available inside the *LightningModule*

```python
class LitModel(L.LightningModule):
    def training_step(self, batch, batch_idx):
        value = ...
        self.log("some_value", value)
```

To log multiple metrics at once, use *self.log_dict*

```python
values = {"loss": loss, "acc": acc, "metric_n": metric_n}  # add more items if needed
self.log_dict(values)
```

### View in the commandline {#view-in-the-commandline}

To view metrics in the commandline progress bar, set the *prog_bar* argument to True.

```python
self.log(..., prog_bar=True)
```

```bash
Epoch 3:  33%|███▉        | 307/938 [00:01<00:02, 289.04it/s, loss=0.198, v_num=51, acc=0.211, metric_n=0.937]
```

### View in the browser {#view-in-the-browser}

To view metrics in the browser you need to use an *experiment manager* with these capabilities.

By Default, Lightning uses Tensorboard (if available) and a simple CSV logger otherwise.

```python
# every trainer already has tensorboard enabled by default (if the dependency is available)
trainer = Trainer()
```

To launch the tensorboard dashboard run the following command on the commandline.

```bash
tensorboard --logdir=lightning_logs/
```

If you're using a notebook environment such as *colab* or *kaggle* or *jupyter*, launch Tensorboard with this command

```bash
%reload_ext tensorboard
%tensorboard --logdir=lightning_logs/
```

### Accumulate a metric {#accumulate-a-metric}

When *self.log* is called inside the *training_step*, it generates a timeseries showing how the metric behaves over time.

![TensorBoard chart of a metric logged with self.log](https://pl-public-data.s3.amazonaws.com/assets_lightning/logging_basic/visualize_logging_basic_tensorboard_chart.png)

However, For the validation and test sets we are not generally interested in plotting the metric values per batch of data. Instead, we want to compute a summary statistic (such as average, min or max) across the full split of data.

When you call self.log inside the *validation_step* and *test_step*, Lightning automatically accumulates the metric and averages it once it's gone through the whole split (*epoch*).

```python
def validation_step(self, batch, batch_idx):
    value = batch_idx + 1
    self.log("average_value", value)
```

![TensorBoard chart of a metric logged with self.log](https://pl-public-data.s3.amazonaws.com/assets_lightning/logging_basic/visualize_logging_basic_tensorboard_point.png)

If you don't want to average you can also choose from `{min,max,sum}` by passing the *reduce_fx* argument.

```python
# default function
self.log(..., reduce_fx="mean")
```

For other reductions, we recommend logging a `torchmetrics.Metric` instance instead.

## Configure the saving directory {#configure-the-saving-directory}

By default, anything that is logged is saved to the current working directory. To use a different directory, set the *default_root_dir* argument in the Trainer.

```python
Trainer(default_root_dir="/your/custom/path")
```

---

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