This builds a Docker image containing Jupyter applications and interactive computing tools based on Python 3.8 for ARMv7 architectures, like the popular RaspberryPi 3 or XU4. Some of the packages included are Pandas, Numpy, Scipy, and matplotlib. These compute intensive packages are also provided as precompiled wheels for faster building.

Find it on:

• https://github.com/andresvidal/jupyter-armv7l
• https://hub.docker.com/r/andresvidal/jupyter-armv7l

Example 1: This command pulls the andresvidal/jupyter-armv7l image from Docker Hub if it is not already present on the local host. It then starts a container running a Jupyter Notebook server and exposes the server on host port 8888. The server logs appear in the terminal. Visiting http://<hostname>:8888/?token=<token> in a browser loads the Jupyter Notebook dashboard page, where hostname is the name of the computer running docker and token is the secret token printed in the console. The container remains intact for restart after the notebook server exits.

docker run -p 8888:8888 andresvidal/jupyter-armv7l

Example 3: This command pulls the andresvidal/jupyter-armv7l image from Docker Hub if it is not already present on the local host. It then starts an ephemeral container running a Jupyter Notebook server and exposes the server on host port 10000. The command mounts the current working directory on the host as /notebooks in the container. The server logs appear in the terminal. Visiting http://<hostname>:10000/?token=<token> in a browser loads Jupyter, where hostname is the name of the computer running docker and token is the secret token printed in the console. Docker destroys the container after notebook server exit, but any files written to /notebooks in the container remain intact on the host.

docker run --rm -p 10000:8888 -v "$PWD":/notebooks andresvidal/jupyter-armv7l

Example 4: This command pulls the andresvidal/jupyter-armv7l image from Docker Hub if it is not already present on the local host. It then starts a daemon container running a Jupyter Notebook server and exposes the server on host port 8888. The command mounts the current working directory on the host as /notebooks in the container.

The server logs won't appear in the terminal. Token and password will be disabled by the additional parameter, --NotebookApp.token='' Visiting http://<hostname>:8888 in a browser loads Jupyter, where hostname is the name of the computer running docker. Docker will attempt to restart the container if it fails with the parameter --restart unless-stopped. Any files written to /notebooks in the container remain intact on the host.

docker run -d \
--name jupyter \
--restart unless-stopped \
-p 8888:8888 \
-v "$PWD":/notebooks \
andresvidal/jupyter-armv7l \
--NotebookApp.token=''

Precompiled wheels for compute intense packages:

wheelhouse/kiwisolver-1.1.0-cp38-cp38-linux_armv7l.whl
wheelhouse/matplotlib-3.1.1-cp38-cp38-linux_armv7l.whl
wheelhouse/numpy-1.17.4-cp38-cp38-linux_armv7l.whl
wheelhouse/pandas-0.25.3-cp38-cp38-linux_armv7l.whl
wheelhouse/pyzmq-18.1.1-cp38-cp38-linux_armv7l.whl
wheelhouse/scipy-1.3.2-cp38-cp38-linux_armv7l.whl

The latest pip-supported Jupyter is installed:

$ jupyter --version
jupyter core     : 4.6.1
jupyter-notebook : 6.0.2
qtconsole        : 4.6.0
ipython          : 7.9.0
ipykernel        : 5.1.3
jupyter client   : 5.3.4
jupyter lab      : not installed
nbconvert        : 5.6.1
ipywidgets       : 7.5.1
nbformat         : 4.4.0
traitlets        : 4.3.3

Build on armv7l architectures.

git clone https://github.com/andresvidal/jupyter-armv7l.git
cd jupyter-armv7l
docker build -t <your hub>/jupyter-armv7l .