Reproducible Deep Learning Research with GPU-Jupyter: A Demo Project

GPU-Jupyter is a lightweight, containerized framework designed to enable fully reproducible deep learning experiments with GPU support — reproducible with just one command.

This repository serves as a demonstration project for reproducible research using the GPU-Jupyter framework. It includes all components — code, data, and environment definition — needed to run and reproduce a deep learning experiment for land-use classification.

Please find the blog-article for this repository at 🧪 Reproducible Deep Learning Experiments with One Command: GPU-Jupyter and the publication at ResearchGate.

Overview

Reproducibility is a fundamental requirement of the scientific method. However, many meta-studies highlight a reproducibility crisis across scientific disciplines. GPU-Jupyter is a flexible and efficient framework for reproducible deep learning experiments by encapsulating the entire experimental setup into an isolated, GPU-supported containerized environment. This approach mitigates version conflicts, ensures a well-defined setup, and streamlines the sharing and collaboration of research.

GPU-Jupyter provides a flexible yet scalable environment for deep learning research, enabling researchers to run experiments on NVIDIA GPUs using Docker containers. It supports various deep learning frameworks like PyTorch, TensorFlow, and Keras, allowing for easy setup and execution of complex models.

🚀 One Command Reproducibility

If the authors have built and published a customized GPU-Jupyter image to a container registry (e.g., DockerHub), the entire deep learning experiment can be reproduced with a single command without any further dependencies.:

bash docker run --gpus all --rm -it -p 8888:8888 cschranz/reproducible-research-with-gpu-jupyter:v1.0

Then open your browser at http://localhost:8888 and use the provided token to access the pre-configured JupyterLab interface.

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Documentation

Requirements

To use GPU-Jupyter, the following components must be installed on the host system:

• NVIDIA GPU: A compatible NVIDIA GPU is required to accelerate deep learning computations.
• NVIDIA CUDA Drivers: The CUDA toolkit provides the necessary drivers and runtime libraries that enable GPU acceleration for deep learning frameworks like PyTorch and TensorFlow. GPU-Jupyter is built on NVIDIA’s official CUDA Docker images to ensure compatibility. Install the latest version from NVIDIA CUDA Toolkit.
• Docker Engine: Docker ensures a reproducible execution environment by isolating deep learning experiments from the host system, preventing dependency conflicts and facilitating portability.
• NVIDIA Container Toolkit: This toolkit allows Docker containers to access GPU resources on the host system, ensuring full hardware acceleration inside the containerized deep learning environment. Official installation instructions are available on NVIDIA’s GitHub repository.

For the setup on the host server, please follow the installation steps in this article Medium.com/Set up Your own GPU-based Jupyter easily using Docker.

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Reproducing Existing Work, Published with or without GPU-Jupyter

To reproduce a deep learning experiment, follow these steps:

1. Start a GPU-Jupyter container

Run the following command, replacing $(pwd) with the absolute path to your project directory:

bash cd path/to/project docker run --gpus all -it -p 8888:8888 \ -v $(pwd):/home/jovyan/work \ -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes \ -e NB_UID=$(id -u) -e NB_GID=$(id -g) \ --user root cschranz/gpu-jupyter:v1.9_cuda-12.6_ubuntu-24.04

Note the following Docker parameters:

• --gpus all: Grants the container access to all available GPUs on the host system, enabling GPU acceleration for deep learning workloads.
• -it: Runs the container in interactive mode, allowing direct user interaction via a terminal (useful for executing commands inside the container).
• -p 8888:8888: Maps port 8888 of the container to port 8888 on the host, enabling access to JupyterLab through http://localhost:8888.
• -v $(pwd):/home/jovyan/work: Mounts the current directory ($(pwd)) on the host to /home/jovyan/work inside the container, ensuring persistent access to files and code across sessions.
• -e GRANT_SUDO=yes: Grants the Jupyter user (jovyan) sudo privileges inside the container, allowing administrative commands if needed.
• -e JUPYTER_ENABLE_LAB=yes: Ensures JupyterLab (instead of the classic Jupyter Notebook interface) is enabled when the container starts.
• -e NB_UID=$(id -u) -e NB_GID=$(id -g): Sets the user ID (UID) and group ID (GID) inside the container to match the host system’s user, preventing permission issues when accessing mounted files.
• --user root: This is the default configuration for running Jupyter within containers, allowing unrestricted access to system configurations and software installations in the isolated environment.

2. Open JupyterLab

Access JupyterLab at http://localhost:8888 (or the ip/hostname of the server instead of localhost with the specified port) and enter the access token printed in the Docker output:

```bash [C 2025-02-17 12:25:57.988 ServerApp]

To access the server, open this file in a browser:
    file:///home/jovyan/.local/share/jupyter/runtime/jpserver-22-open.html
Or copy and paste one of these URLs:
    http://127.0.0.1:8888/lab?token=ba872bc692eb7d749bbfaf7ef1a48ce5a8ff3658f2d49b14

```

Then navigate in JuperLab's file explorer into work/reproducible-research-with-gpu-jupyter/ and check out the project directory.

When mounting a directory into a container, inconsistent user permissions can lead to files and directories being hidden in Jupyter's file explorer. To ensure proper ownership, open a terminal in JupyterLab and run:

bash ll # check user permissions sudo chown -R jovyan.users work/

3. Clone the Experiment Repository

Inside JupyterLab, open a terminal and execute:

bash cd work git clone https://github.com/iot-salzburg/reproducible-research-with-gpu-jupyter cd reproducible-research-with-gpu-jupyter/ pip install -r requirements.txt

This recreates the original deep learning environment, ensuring that all dependencies match the original research setup.

Then, reproduce the experiment by running the provided Jupyter Notebook under src/modelling/train_ResNet.ipynb. Randomness is controlled by providing random seeds for functions in numpy, PyTorch, and PyTorch-CUDA.

Alternative Setup Using Docker Compose for Configuration Management

Instead of specifying all parameters manually in a docker run command, Docker Compose allows for a structured, version-controlled setup by defining configurations in the local docker-compose.yml file. Using a docker-compose.yml is particularly useful for version-controlled setup configuration in Git, reusable setup across different systems, and simplified container management to organize multiple services. Please find an example for this repository in docker-compose.yml, which constraints hardware resources (as often used for shared physical setups).

Basic docker-compose Commands:

1. Start GPU-Jupyter (in the background): bash docker-compose up -d

   • Builds (if necessary) and starts the container based on docker-compose.yml.
   • The -d flag runs it in detached mode, meaning the container continues running in the background.

2. Stop and remove the container: bash docker-compose down

3. View logs in real time: bash docker-compose logs -f

Make Your Own Research Reproducible in a Single Command

GPU-Jupyter enables researchers to publish experiments with full computational reproducibility.

Preparation Steps

1. Develop deep learning models in JupyterLab inside a GPU-Jupyter container.
2. Share the code and dataset in a public Git repository and/or data repository.
3. Specify the exact GPU-Jupyter image.
4. Explain all additional steps required for the setup and reproduction of the experiment.

Example Open Science statement for your publication:

The authors ensure the reproducibility of their work by providing all code, data, and environment details at https://github.com/iot-salzburg/reproducible-research-with-gpu-jupyter. All experiments were conducted with <CPU/RAM> and <GPU model name/number> using the image cschranz/gpu-jupyter:v1.9_cuda-12.6_ubuntu-24.04 as available on DockerHub.

Publishing in a Customized GPU-Jupyter Image for single-command Reproducibility

For the reproducibility of your deep learning research in only one single command, a customized GPU-Jupyter image can be built and published that includes the entire experiment.

Step 1: Define the Dockerfile

Build a Dockerfile that installs all required dependencies. Use an appropriate tagged GPU-Jupyter image as base image and declare the full computational setup of your experiment from there, such as installations in package managers like pip. Use the local Dockerfile as an example. It is encouraged that datasets are externally referred (open data repos) or reasonably sized (compressed, no redundancy, etc.), as otherwise, the data size of the resulting image may be huge.

Step 2: Log in to Docker Hub

sh docker login - Enter your Docker Hub username and password when prompted. - If using Access Tokens, generate one from Docker Hub and use it as the password.

Step 3: Build the Docker Image

sh docker build -t your-dockerhub-username/image-name:tag .

• -t specifies the tag. Specify your own username and repository name for your-dockerhub-username/image-name:tag.
• . assumes the Dockerfile to build is in the current directory.

Verify the image is built using:

sh docker images

Step 4: Push the Image to DockerHub

sh docker push your-dockerhub-username/image-name:tag

• The image will now be available on Docker Hub at: https://hub.docker.com/r/your-dockerhub-username/image-name

Step 5: Verify if it worked: Pull and Run the Image

Optimally, test on another machine by pulling and running your image using:

sh docker run --gpus all --rm -it -p 8888:8888 your-dockerhub-username/image-name:tag

To reproduce the experiment within the repository, run:

sh docker run --gpus all --rm -it -p 8888:8888 cschranz/reproducible-research-with-gpu-jupyter:v1.0 Please make sure to describe which scripts or Notebooks have to be executed to reproduce your research. In the case of this example repository, reproduce the experiment by running the provided Jupyter Notebook under src/modelling/train_ResNet.ipynb and check if the identical results are yielded

Include the single-command execution for reproducing your deep learning experiment in the research paper to ensure future researchers can reproduce your work effortlessly without additional setup steps.

Ensure that the data within the repository is reasonably sized or downloaded during the execution of the experiment. Otherwise, a big dataset is included in the image stored on DockerHub thus being redundant.

More information

Customizing the Docker Container for Your Project

GPU-Jupyter allows flexible configuration for various deep learning workflows:

• Run detached containers using -d to keep them running in the background. Find more Docker commands in Docker-Docs.
• Set a static access token for persistent authentication, as described among other configurations in github.com/GPU-Jupyter.
• Enable sudo privileges within the container using -e GRANT_SUDO=yes.

• Adapt file ownership and permissions of mounted directories, e.g., ensure proper ownership:

  bash sudo chown -R jovyan.users work/ # set ownership sudo chmod -R 660 work/ # set read and write permissions for the owning group sudo chmod -R g+X work/ # set the executable permission

Calkit Integration

This work is also compatible with calkit. A demonstration of calkit can be found in the branch calkit-integration with the calkit README.md.

Learn More About CodeMeta

For more information on how to use CodeMeta to describe your code, please refer to the CodeMeta documentation.

Learn More About JupyterLab

Here is a tutorial on how to use JupyterLab:

Video: Introductory tutorial on the use of JupyterLab.  Created by Van Yang. Source: official channel of Jupyter on YouTube

Cite This Work

When you are using GPU-Jupyter for the development of your academic work and its reproduction, please cite the framework in your publication as:

markdown Schranz, C., Pilosov, M., Beeking, M. (2025). *GPU-Jupyter: A Framework for Reproducible Deep Learning Research.* [Accepted in] Interdisciplinary Data Science Conference. Preprint-DOI: [10.13140/RG.2.2.15549.99040](https://www.researchgate.net/publication/393662057)

apa-style @inproceedings{GPU-Jupyter2025, author = {Schranz, C. and Pilosov, M. and Beeking, M.}, title = {GPU-Jupyter: A Framework for Reproducible Deep Learning Research}, booktitle = {Interdisciplinary Data Science Conference}, year = {2025}, publisher = {ResearchGate}, doi = {10.13140/RG.2.2.15549.99040}, url = {https://www.researchgate.net/publication/393662057} }

By streamlining environment management, GPU-Jupyter reduces barriers to reproducibility, fostering an open and trustworthy research culture.

Happy reproducible research with GPU-Jupyter! If you have any questions or suggestions, feel free to open an issue in the GitHub repository.