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pyRat

Docker Container for Jupyter notebooks with R kernel and R Cell Magic

Example Notebooks


To Use

  • pull image from Dockerhub and run default
docker run --rm -it -p 8888:8888 cfljam/pyrat

Pull the image from Dockerhub and run a notebook server locally on port 8888 with Documents dir on host mounted

docker run --rm -it -p 8888:8888 \
-v ~/Documents/:/Documents cfljam/socker \
sh -c " ipython notebook --ip=0.0.0.0 --port=8888 \
--no-browser --notebook-dir=/Documents"

We invoke the notebook servers in a sh call to avoid kernel instability in OSX and Windows VM hosts ( See ipython/ipython#7062)

To Build Docker Image

git clone https://github.com/cfljam/pyRat
cd pyRat
  • if you are behind a proxy , insert lines at the head of the dockerfile
ENV http_proxy  http://my.proxy.url:my_proxy_port
ENV https_proxy  https://my.proxy.url:my_proxy_port
  • to avoid running as root you might want to add a user with a
RUN useradd <my_login>
  • and make this the default with a USER line
USER <my_login>
  • build the image (you can call it anything you want-but in this case repo name, no other tags)
docker build -t cfljam/pyrat .

To run the image

  • ensure that there is a port forwarding rule for port 8888 on the virtual host (on Virtualbox in this case) Virtualbox port forward rule
  • run the container, sharing default Virtualbox shared directory mapping /Users
docker run --rm -p 8888:8888 -v /Users/:/Users -it cfljam/pyrat
  • point your browser to localhost:8888 . You should see something like the screen below. Navigate the links to where you want to work and select new to create content. Jupyter Screen

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Jupyter notebooks plus R definitions for Docker

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