Docker Image for SciPy with Jupyter Notebook.
This Docker image is for SciPy with Jupyter Notebook. This images inherits compdatasci/base.
Running Jupyter Notebook
To install Docker for your platform (Windows, MacOS, Linux, cloud platforms, etc.), follow the instructions at docker.com.
Once you have Docker installed, you can start Jupyter Notebook using the following command in the directory that contains your notebook (e.g., scipy-intro.ipynb):
docker-jupyter -i scipy-jupyter scipy-intro.ipynb
docker-jupyter script can be downloaded at https://github.com/compdatasci/dockerfiles/raw/master/docker-jupyter.
Running Jupyter Notebook with Docker Toolbox
If your version of Windows does not support Docker, you may need to install Docker Toolbox instead. After you have installed Docker Toolbox, start it and run the following command in a Docker Toolbox terminal in your work directory:
docker run --rm -w /home/compdatasci/shared -v $(pwd):/home/compdatasci/shared -d -p \ $(docker-machine ip $(docker-machine active)):8088:8088 compdatasci/scipy-jupyter \ 'jupyter-notebook --no-browser --ip=0.0.0.0 --port=8088'
If successful, you will see some screen out such as:
... Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://0.0.0.0:8088/?token=2634a8f67ed91c582929e1a1137b8b3b400385b35afab19e
Copy and paste the URL into a web browser (such as Google Chrome). If port
8088 is in use, you can change it to a different port (say
8099) by replacing
8099 in the
docker run command.
When you have finished using Jupyter Notebook, use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
Running as Linux environment
You can also run the image as a Linux environment for SciPy. You can run the image using the following command:
docker run --rm -ti -w/home/compdatasci/shared -v $(pwd):/home/compdatasci/shared \ compdatasci/scipy-jupyter:latest
which would share your current working directory into the container as
~/shared. Note that you should only save files under the shared directory because all other files will be lost when the process ends.