---
title: LitLogger
description: >-
  LitLogger enables seamless experiment tracking, logging, and artifact
  management on the Lightning.ai platform.
canonical: 'https://lightning.ai/docs/fabric/stable/reference/guide/loggers/litlogger'
---
# LitLogger {#litlogger}

[LitLogger](https://pypi.org/project/litlogger/) enables seamless experiment tracking, logging, and artifact management on the [Lightning.ai](https://lightning.ai) platform.

It integrates with your Fabric training loop to log metrics, hyperparameters, and model checkpoints automatically to the Lightning Experiments dashboard.
View your experiments at [lightning.ai](https://lightning.ai) with real-time charts, compare runs, and share results with your team.

## Set Up LitLogger {#set-up-litlogger}

First, install the `litlogger` package:

```bash
pip install litlogger
```

That's it! LitLogger automatically detects your Lightning.ai credentials when running in a Lightning Studio or when logged in via the CLI.

## Track Metrics {#track-metrics}

To start tracking metrics in your training loop, import the LitLogger and configure it with your settings:

```python
from lightning.fabric import Fabric
from lightning.pytorch.loggers import LitLogger

# 1. Configure the logger
logger = LitLogger(name="my-experiment")

# 2. Pass it to Fabric
fabric = Fabric(loggers=logger)
```

Next, add `log` calls in your code:

```python
value = 0.5  # Python scalar or tensor scalar
fabric.log("some_value", value)
```

To log multiple metrics at once, use `log_dict`:

```python
loss, acc, other = 0.1, 0.95, 0.5
values = {"loss": loss, "acc": acc, "other": other}
fabric.log_dict(values)
```

## Log Hyperparameters {#log-hyperparameters}

Log your model's hyperparameters to keep track of your experiment configuration:

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

logger = LitLogger(name="my-experiment")
logger.log_hyperparams({
    "learning_rate": 0.001,
    "batch_size": 32,
    "model": "resnet50",
})
```

You can also pass metadata directly when creating the logger:

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

logger = LitLogger(
    name="my-experiment",
    metadata={"learning_rate": "0.001", "batch_size": "32"},
)
```

## Log Checkpoints {#log-checkpoints}

Enable automatic checkpoint logging with the `log_model` parameter:

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

logger = LitLogger(name="my-experiment", log_model=True)
```

Checkpoints will be automatically uploaded to the Lightning platform when saved.

You can also manually log model artifacts:

```python
# Log a model checkpoint file
logger.log_model_artifact("/path/to/checkpoint.ckpt")

# Log a model object directly
logger.log_model(model)
```

## Log Files {#log-files}

Log any file as an artifact:

```python
# Log a configuration file
logger.log_file("config.yaml")
```

## Capture Terminal Output {#capture-terminal-output}

Enable terminal log capture to save your script's output:

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

logger = LitLogger(name="my-experiment", save_logs=True)
```

Your terminal output will be captured and available in the Lightning Experiments dashboard.

## View Your Experiments {#view-your-experiments}

After running your training script, view your experiments at [lightning.ai](https://lightning.ai).
The dashboard provides:

- Real-time metric charts
- Hyperparameter comparison
- Artifact management
- Team collaboration features

Access your experiment URL programmatically:

```python
print(logger.url)
```

---

**Navigation**

- Parent: [Loggers](/docs/fabric/2.6.6/reference/guide/loggers.md)