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# Tracking & Monitoring
This guide explains how to monitor your experiments using Weights & Biases (WandB) and TensorBoard.
## Weights & Biases (WandB)
WandB is used for online synchronization and visualization of training metrics.
### Authorization
Export your API key in your terminal to enable WandB synchronization:
```bash
export WANDB_API_KEY=your_copied_api_key_here
```
Alternatively, you can log in using the CLI:
```bash
uv run wandb login
```
### Enabling Tracking
To enable online sync during a training run, set `logging.track=true` on the command line:
```bash
uv run python scripts/train.py logging.track=true
```
You can also configure your project and entity:
```bash
uv run python scripts/train.py \
logging.track=true \
logging.wandb_project_name="MyProject" \
logging.wandb_entity="my-team"
```
These can also be set in your configuration YAML file under the `logging` key.
## Local Monitoring with TensorBoard
All runs are recorded locally in the `runs/` directory (or the directory specified in `experiment.base_run_dir`). You can view scalars and other metrics with TensorBoard:
```bash
tensorboard --logdir runs/
```
Access the interface at `http://localhost:6006`.
### CLI Exploration Tool
For quick diagnostics or to export data to CSV without launching the full TensorBoard UI, you can use the `explore_tensorboard.py` script:
```bash
uv run python scripts/analysis/explore_tensorboard.py runs/your_run_name/
```
See the detailed description in [`/scripts/analysis/README.md`](../../scripts/analysis/README.md).