The IPython notebook project is one the preferred tools of data scientists,
and it is nowadays the bastion for Reproducible Research.
In fact, notebooks are now used as in-browser IDE (Integrated Development Environment) to implement the whole data analysis process, along with the corresponding documentation.
However, since this kind of processes usually include heavy-weight computations, it may likely happen that execution results get lost if something wrong happens, e.g. the connection to the notebook server hangs or an accidental page refresh is issued.
To this end, [%]%async_run notebook line/cell magic to the rescue.
In this talk, I would like to talk about some of the technologies I played with since I decided to develop
this extension.
These technologies include asynchronous I/O libraries (e.g. asyncio, tornado.websocket),
multiprocessing, along with IPython kernels and notebooks.
During the talk, I would like to discuss pitfalls, failures, and adopted solutions (e.g. namespace management among processes) , aiming at getting as many feedbacks as possible from the community.
A general introduction to the actual state-of-the-art of the Jupyter projects (an libraries) will be presented as well, in order to help those who are willing to know some more details about the internals of IPython.
