Self-host Jupyter GPU Notebook with Docker
JupyterLab with a CUDA-ready data science stack for training and experimenting with models.
This page describes the Levelrail template for Jupyter GPU Notebook (AI). It deploys the services below as one Docker Compose app on your own server.
Project site: https://github.com/iot-salzburg/gpu-jupyter.Recommended memory: about 4096 MiB.
This template reserves an NVIDIA GPU, so it needs a node with a GPU and the NVIDIA container runtime.
Services, ports and volumes
| Service | Image | Container ports | Volumes |
|---|---|---|---|
jupyter | cschranz/gpu-jupyter:v1.11_cuda-13.0_ubuntu-24.04_python-only | 8888 | jupyter_work -> /home/jovyan/work |
Environment variables
| Service | Variable | Value |
|---|---|---|
jupyter | JUPYTER_TOKEN | Generated at deploy |
Passwords and keys marked as generated are created for you when the app is deployed and stored as secrets. Values are not shown here.
Deploy Jupyter GPU Notebook with Levelrail
In the dashboard, open Apps, choose New app, then Browse templates, and select Jupyter GPU Notebook. Review the Compose body and deploy.
With the CLI:
levelrail-cli templates deploy jupyter-gpu --name my-jupyter-gpuSee Service template catalog for how templates work, and Getting started if you have not installed Levelrail yet.
More AI templates
All templates are listed in the self-host gallery.