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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
GPU-Jupyter: Your GPU-accelerated JupyterLab with a rich data science toolstack, TensorFlow and PyTorch for your reproducible deep learning experiments.
| Date | Stars |
|---|---|
| 2026-07-24 | 769 |
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# GPU-Jupyter
<img src="https://raw.githubusercontent.com/iot-salzburg/gpu-jupyter/master/extra/logo_gupyter.png"
alt="GPU-Jupyter"
width=661/>
#### GPU-Jupyter: Your GPU-accelerated JupyterLab with PyTorch, TensorFlow, and a rich data science toolstack for your reproducible deep learning experiments.
[](https://github.com/iot-salzburg/gpu-jupyter/graphs/commit-activity)
[](https://github.com/iot-salzburg/gpu-jupyter/pulls?q=is%3Aclosed)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/commit/)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/stargazers/)
[](https://hub.docker.com/r/cschranz/gpu-jupyter)
[](https://hub.docker.com/r/cschranz/gpu-jupyter)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/network/)
### TL;DR
```bash
mkdir -p data
docker run --gpus all -it -p 8848:8888 -v "$(pwd)/data:/home/jovyan/work" --user root cschranz/gpu-jupyter:v1.11_cuda-13.0_ubuntu-24.04_python-only
```
Open [http://localhost:8848](http://localhost:8848) using the token printed in the terminal. See [Quickstart](#quickstart) below for requirements and all configuration options.
Welcome to this project, which provides a **GPU-capable environment** based on NVIDIA's official CUDA Docker image and the popular [Jupyter's Docker Stacks](https://github.com/jupyter/docker-stacks).
By utilizing version control for the source code, tagged data spaces, seeds for the random functions within isolated Docker containers, our solution **empowers researchers to conduct fully reproducible and sharable machine-learning experiments**.
Architecture of the GPU-Jupyter Docker image on top of NVIDIA and Docker.
Please find an example of how to **use GPU-Jupyter to make your deep learning research reproducible with one single command on [github.com/iot-salzburg/reproducible-research-with-gpu-jupyter](https://github.com/iot-salzburg/reproducible-research-with-gpu-jupyter)**.
## Contents
1. [Quickstart](#quickstart)
2. [Configuration](#configuration)
- [Docker parameters](#docker-parameters)
- [Available GPU-Jupyter Images](#available-gpu-jupyter-images)
- [Set a Static Token](#set-a-static-token)
- [Deploy with Docker Compose](#deploy-with-docker-compose)
- [Adaptions for using Tensorboard](#adaptions-for-using-tensorboard)
- [Customized installations](#customized-installations)
- [Share your customized Dockerfile](#share-your-customized-dockerfile)
3. [Build Your Image](#build-your-image)
- [Configuration of the Dockerfile-Generation](#configuration-of-the-dockerfile-generation)
- [Set NVIDIA CUDA Base Image](#set-nvidia-cuda-base-image)
- [Specify Jupyter Docker Stacks Version](#specify-jupyter-docker-stacks-version)
4. [Issues and Contributing](#issues-and-contributing)
- [Frequent Issues](#frequent-issues)
- [Contribution](#contribution)
5. [Cite This Work](#cite-this-work)
## Quickstart
1. **Requirements:**
- **NVIDIA GPU with drivers**
- **CUDA**
- **Docker**
- **NVIDIA Container Toolkit**
You can confirm that all requirements are matched if the Docker command below returns a result similar to this one:
```bash
docker run --rm --gpus all nvidia/cuda:13.0.3-cudnn-runtime-ubuntu24Excerpt of 30,860 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bfd54346eb1df3bf, topic:pytorch, topic:tensorflow