CUDA.md

CUDA-based JupyterLab MAX docker stack

GPU accelerated, multi-arch (linux/amd64, linux/arm64/v8) docker images:

• glcr.b-data.ch/jupyterlab/cuda/max/base
• glcr.b-data.ch/jupyterlab/cuda/max/scipy

Images available for MAX versions ≥ 24.6.0.

Build chain

The same as the JupyterLab MAX/Mojo docker stack.

Features

The same as the JupyterLab MAX/Mojo docker stack plus the CUDA runtime.

👉 See the CUDA Version Matrix for detailed information.

Subtags

The same as the JupyterLab MAX/Mojo docker stack.

Table of Contents

• Prerequisites
• Install
• Usage
• Similar project

Prerequisites

The same as the JupyterLab MAX/Mojo docker stack plus

• NVIDIA GPU
• NVIDIA Linux driver
• NVIDIA Container Toolkit

ℹ️ The host running the GPU accelerated images only requires the NVIDIA driver, the CUDA toolkit does not have to be installed.

Install

To install the NVIDIA Container Toolkit, follow the instructions for your platform:

• Installation Guide — NVIDIA Cloud Native Technologies documentation

Usage

Build image (base)

latest:

cd base
docker build \
  --build-arg BASE_IMAGE=ubuntu \
  --build-arg BASE_IMAGE_TAG=22.04 \
  --build-arg BUILD_ON_IMAGE=glcr.b-data.ch/cuda/python/ver \
  --build-arg MOJO_VERSION=25.1.0 \
  --build-arg PYTHON_VERSION=3.12.9 \
  --build-arg CUDA_IMAGE_FLAVOR=base \
  --build-arg INSTALL_MAX=1 \
  -t jupyterlab/cuda/max/base \
  -f latest.Dockerfile .

version:

cd base
docker build \
  --build-arg BASE_IMAGE=ubuntu \
  --build-arg BASE_IMAGE_TAG=22.04 \
  --build-arg BUILD_ON_IMAGE=glcr.b-data.ch/cuda/python/ver \
  --build-arg CUDA_IMAGE_FLAVOR=base \
  --build-arg INSTALL_MAX=1 \
  -t jupyterlab/cuda/max/base:MAJOR.MINOR.PATCH \
  -f MAJOR.MINOR.PATCH.Dockerfile .

For MAJOR.MINOR.PATCH ≥ 24.6.0.

Create home directory

Create an empty directory using docker:

docker run --rm \
  -v "${PWD}/jupyterlab-jovyan":/dummy \
  alpine chown 1000:100 /dummy

It will be bind mounted as the JupyterLab user's home directory and automatically populated.

Run container

self built:

docker run -it --rm \
  --gpus '"device=all"' \
  -p 8888:8888 \
  -u root \
  -v "${PWD}/jupyterlab-jovyan":/home/jovyan \
  -e NB_UID=$(id -u) \
  -e NB_GID=$(id -g) \
  jupyterlab/cuda/max/base[:MAJOR.MINOR.PATCH]

from the project's GitLab Container Registries:

docker run -it --rm \
  --gpus '"device=all"' \
  -p 8888:8888 \
  -u root \
  -v "${PWD}/jupyterlab-jovyan":/home/jovyan \
  -e NB_UID=$(id -u) \
  -e NB_GID=$(id -g) \
  IMAGE[:MAJOR[.MINOR[.PATCH]]]

IMAGE being one of

• glcr.b-data.ch/jupyterlab/cuda/max/base
• glcr.b-data.ch/jupyterlab/cuda/max/scipy

The use of the -v flag in the command mounts the empty directory on the host (${PWD}/jupyterlab-jovyan in the command) as /home/jovyan in the container.

-e NB_UID=$(id -u) -e NB_GID=$(id -g) instructs the startup script to switch the user ID and the primary group ID of ${NB_USER} to the user and group ID of the one executing the command.

The server logs appear in the terminal.

Using Podman (rootless mode)

Create an empty home directory:

mkdir "${PWD}/jupyterlab-root"

Use the following command to run the container as root:

podman run -it --rm \
  --device 'nvidia.com/gpu=all' \
  -p 8888:8888 \
  -u root \
  -v "${PWD}/jupyterlab-root":/home/root \
  -e NB_USER=root \
  -e NB_UID=0 \
  -e NB_GID=0 \
  -e NOTEBOOK_ARGS="--allow-root" \
  IMAGE[:MAJOR[.MINOR[.PATCH]]]

Using Docker Desktop

Creating a home directory might not be required. Also

docker run -it --rm \
  --gpus '"device=all"' \
  -p 8888:8888 \
  -v "${PWD}/jupyterlab-jovyan":/home/jovyan \
  IMAGE[:MAJOR[.MINOR[.PATCH]]]

might be sufficient.

Similar project

• MAX container

What makes this project different:

1. Derived from nvidia/cuda:12.8.0-base-ubuntu22.04
2. IDE: code-server next to JupyterLab
3. Just Python – no Conda / Mamba

The CUDA-based JupyterLab MAX docker stack is derived from the CUDA-based Python docker stack.
ℹ️ See also Python docker stack > Notes on CUDA.