> ## Documentation Index
> Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Run Jupyter Notebook on a Cloud GPU

> Deploy a GPU instance, install JupyterLab, and access it from your browser with port forwarding.

export const RuncrateStyles = () => {
  if (typeof document !== 'undefined' && !document.getElementById('runcrate-overrides')) {
    const s = document.createElement('style');
    s.id = 'runcrate-overrides';
    s.textContent = `
      /* Match Runcrate's rounding scale (--radius: 0.75rem) */
      .rounded-sm { border-radius: 0.5rem !important; }   /* 8px */
      .rounded-md { border-radius: 0.625rem !important; } /* 10px */
      .rounded-lg { border-radius: 0.75rem !important; }  /* 12px */
      .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; }
      .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; }
      .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; }
      .rounded-r-md { border-top-right-radius: 0.625rem !important; border-bottom-right-radius: 0.625rem !important; }
      .rounded-l-lg { border-top-left-radius: 0.75rem !important; border-bottom-left-radius: 0.75rem !important; }
      .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; }

      /* Cards: never pure white in light mode */
      .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; }
      html.dark .card { background-color: #141414 !important; }

      /* Docs hero box */
      .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; }
      html.dark .rc-hero { background-color: #141414; border-color: #242424; }
      html.dark .rc-hero h1 { color: #f5f5f5; }

      /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */
      ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; }
      ::-webkit-scrollbar-track { background-color: transparent; }
      ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; }
      ::-webkit-scrollbar-thumb:hover { background-color: rgba(155, 155, 155, 0.7); }
      *:hover::-webkit-scrollbar-thumb,
      *:focus::-webkit-scrollbar-thumb,
      *:active::-webkit-scrollbar-thumb { opacity: 1; }
      * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; }
    `;
    document.head.appendChild(s);
  }
  return null;
};

<RuncrateStyles />

Run JupyterLab on a dedicated cloud GPU. Train models, explore datasets, and prototype backed by an RTX 4090, A100, or H100 — without installing CUDA locally.

## 1. Deploy a devbox

```bash theme={"theme":"github-dark"}
runcrate instances create --name jupyter --gpu RTX4090 --template ubuntu-devbox
runcrate instances status jupyter
```

## 2. Install and start JupyterLab

```bash theme={"theme":"github-dark"}
runcrate ssh jupyter -- "pip install jupyterlab ipywidgets"

runcrate ssh jupyter -- "nohup jupyter lab \
  --ip=0.0.0.0 --port=8888 --no-browser --allow-root \
  --NotebookApp.token='' --NotebookApp.password='' \
  > /root/jupyter.log 2>&1 &"
```

## 3. Connect via browser

```bash theme={"theme":"github-dark"}
runcrate instances info jupyter
```

Open `http://<INSTANCE_IP>:8888` in your browser.

## 4. Verify GPU access

In a notebook cell:

```python theme={"theme":"github-dark"}
import torch
print(f"CUDA: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")
print(f"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
```

## 5. Persist notebooks with a volume

```bash theme={"theme":"github-dark"}
runcrate volumes create --name notebooks --size 50
runcrate instances create --name jupyter --gpu RTX4090 --template ubuntu-devbox --storage notebooks
```

Notebooks saved to `/workspace/` persist across deploys.

## Upload data

```bash theme={"theme":"github-dark"}
runcrate cp ./dataset.csv jupyter:/workspace/
runcrate cp ./my_notebook.ipynb jupyter:/workspace/
```

## Install additional packages

```bash theme={"theme":"github-dark"}
runcrate ssh jupyter -- "pip install pandas scikit-learn matplotlib transformers"
```

## Tips

* Use the `ubuntu-devbox` template — it includes CUDA, cuDNN, and Python.
* Save notebooks to `/workspace/` when using a volume for persistence.
* For long training runs, use `nohup` in a terminal tab so the job survives browser disconnects.
* For password-protected access, run `jupyter lab password` and restart without the token flags.

## Cleanup

```bash theme={"theme":"github-dark"}
runcrate instances delete jupyter
```