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Last pushed: 2 years ago
Short Description
jupyteR docker pymol
Full Description


Travis CI:

Container-based installation of PyMol, with interaction through the browser via ipymol and Jupyter notebook (based on jupyter/notebook). A convenient and portable way to render pretty pictures of molecules, and much more.

The installation also contains numpy and scipy among other things, so it can be used for a variety of scientific Python tasks.

Instructions (setup)

First, note down the IP address of the currently running Docker machine (which is called dev in this example) with

docker-machine ip dev
  1. Download the image from Docker hub :

     docker pull ocramz/jupyter-docker-pymol

    You can see the list of locally available Docker images with the command docker images

  1. Run the image :

     docker run --rm -it -p 8888:8888 ocramz/jupyter-docker-pymol
  1. Point your browser to the IP address of the Docker machine found initially, and port 8888, i.e.


    where <docker-machine-ip> usually starts with 192.168.

A Jupyter session should appear in the browser at this point. Up and running !

Instructions (use)

  1. Within Jupyter, start a Python 3 document (or just start by modifying the provided example notebook)
  1. Declare inline figure rendering within Jupyter notebooks and setup the connection to PyMol:

     %pylab inline 
     from ipymol import viewer as pm
  1. Run your PyMol tasks, e.g. :'fetch 3odu; as cartoon; bg white;')
     f1 =



This project uses PyMol and Python 3


At present, this setup is intended for local use only (i.e. the Docker image, along with all the computational payload i.e. PyMol and the Python interpreter, is running on the same host that runs the browser).

There is NO authentication to the notebooks and the Jupyter user is root.


the PyMol project contributors ,

the Jupyter and iPython project contributors ,

Carlos Hernandez for ipymol ,

Saulo Alves ( for helping merge with the BioDocker project (

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