Skip to main content

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.

​
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 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.