One Docker command to stop fighting with CUDA drivers

What it does GPU-Jupyter is a Docker image built on top of NVIDIA’s official CUDA base and Jupyter’s Docker Stacks. It packages JupyterLab with PyTorch, TensorFlow, and a full data-science toolstack into a container that sees your host GPU via the NVIDIA Container Toolkit. You pull a tagged image, run one command, and get a working GPU notebook server on localhost.

The interesting bit The project treats reproducibility as a first-class feature: version-tagged images, isolated containers, and pinned seeds are presented as the mechanism for “fully reproducible and sharable machine-learning experiments.” This is essentially environment-as-a-guarantee rather than environment-as-hope.

Key highlights

• Pre-built images on Docker Hub with explicit CUDA/Ubuntu version tags (e.g., v1.10_cuda-12.9_ubuntu-24.04)
• Three image variants per release: full (Python + Julia + R), python-only, and slim
• Includes sample GPU starter code in extra/Getting_Started
• Companion repo demonstrates reproducible research workflow with one command
• Supports sudo inside container for custom package installation

Caveats

• Requires full NVIDIA stack on host: GPU, drivers, CUDA, Docker, and NVIDIA Container Toolkit
• README notes a GitHub workflow failure due to “No space left on device” — CI appears strained by image size
• Image tags in the “Available Images” section have copy-paste errors (three entries claim the same tag with different descriptions)

Verdict Worth a look if you’re tired of maintaining CUDA environments or need to hand a reproducible GPU notebook setup to collaborators. Skip it if you’re already happy with your current container workflow or don’t have an NVIDIA GPU.