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

# Jupyter Notebooks

> Execute Jupyter Notebooks on Thunder Compute cloud GPUs. Configure remote kernels and process intensive data workloads in a notebook environment.

## Prerequisites for a Jupyter Notebook with Cloud GPU

* A supported editor installed: VSCode, Cursor, or Windsurf
* The Thunder Compute extension installed in that editor
* The Jupyter extension installed in that editor

## Steps to Launch Your Notebook

### 1. Connect to a Thunder Compute cloud GPU in VSCode

Follow the instructions in our [quickstart](/vscode/quickstart) guide to set and connect to a remote instance in VSCode.

### 2. Install the Jupyter extension in your cloud workspace

Open the Extensions panel and install the Jupyter extension inside your Thunder Compute instance.

### 3. Verify GPU availability inside the notebook

Create a Jupyter Notebook, which is now connected to a Thunder Compute instance with GPU capabilities. To confirm that the GPU is accessible, run the following in a notebook cell:

```
import torch
print(torch.cuda.is_available())
```

If everything is set up correctly, the output should be:

```
True
```

You now have a Jupyter Notebook running on a Thunder Compute cloud GPU, a fast and low-cost alternative to Colab for indie developers, researchers, and data scientists.