***

title: Running Jupyter Notebooks
description: Run JupyterLab on your NVIDIA Brev GPU instance with port forwarding.
----------------------------------------------------------------------------------

Access JupyterLab on your GPU instance with port forwarding for interactive development with GPU acceleration.

## Start JupyterLab

Connect to your instance and start JupyterLab:

```bash
# Connect to your instance
brev shell my-instance

# Start JupyterLab (no browser on remote)
jupyter lab --no-browser --port=8888
```

Copy the URL with token from the output (looks like `http://localhost:8888/lab?token=...`).

## Forward the Port

In a new terminal window on your local machine:

```bash
brev port-forward my-instance --port 8888:8888
```

Now open the Jupyter URL (with token) in your local browser: `http://localhost:8888`

## Verify GPU Access

In a notebook cell, verify the GPU is available:

```python
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")
```

## Best Practices

* Use `jupyter lab --ip 0.0.0.0` if you need to access from external hosts
* Run Jupyter in a `tmux` session to keep it running after disconnecting
* Forward TensorBoard on port 6006 alongside Jupyter for experiment tracking

## What's Next

<CardGroup cols={2}>
  <Card title="Custom Containers" icon="server" href="/guides/development-tools/custom-containers">
    Run Docker workloads with GPU access.
  </Card>

  <Card title="NGC Developer Guide" icon="book" href="/guides/inference-deployment/ngc-guide">
    Access NVIDIA's GPU-optimized containers and models.
  </Card>
</CardGroup>