Torch + Jupyter

Deploy JupyterLab with PyTorch preinstalled as a template on a GPU instance. Aquanode provisions and starts it for you behind a private, password-protected URL.

The Torch + Jupyter template gives you a ready-to-use JupyterLab environment with PyTorch preinstalled, running on a GPU instance. Pick it when you deploy and it starts automatically on boot, served behind a password-protected URL. No setup on the box required.

What you can do

Get a GPU-backed JupyterLab with PyTorch already installed
Run notebooks for training, fine-tuning, inference, and experiments
Access it from your browser over a private URL with an auto-generated password

Deploy Torch + Jupyter

Pick a GPU

Go to the Marketplace and choose a GPU offer sized for your workload.

Select the Torch + Jupyter template

In the deploy configuration panel, open the Template dropdown and choose Torch + Jupyter. Set a deployment name and resources, then deploy.

The service starts on boot

Aquanode provisions the box, sets up JupyterLab with PyTorch, and starts it automatically once the VM is ready. You don't install anything yourself.

Open JupyterLab

When the deployment reaches Ready, open it from Deployments and go to the Overview tab. The Jupyter section shows:

An Open button (and the URL, with a copy button)
A Password to sign in

Click Open, enter the password, and JupyterLab loads in your browser. PyTorch is already available, so you can import torch and start working immediately.

Access details

JupyterLab is served on port 8888. Authentication uses a single password (there is no separate username), generated automatically per deployment and shown on the Overview tab.
The service status (running / starting) is shown alongside the password.

The instance bills until you close it

A Jupyter deployment is a running GPU instance. It keeps billing until you close the deployment from the console. Close it when you're done.