How a Notebook changed the world of Science!

Tech talk on Semut.io

Notebooks are disrupting science, finance and every other sector. Learn more about Jupyter Notebooks on this tech talk with Shreyas Bapat, B.Tech. IIT Mandi, who is spearheading efficiency initiatives at Semut.io.
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Transcript

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3. Who am I?
   -> Software Engineer @ Semut.io
   -> Electrical Engineer from IIT Mandi
   -> Lead Developer @ EinsteinPy
   -> Managing Member @ PSF
   -> Love being in Mountains of
   Himachal Pradesh (India)
   

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4. Let’s start with what went wrong?
   

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5. What is it?
   -> Published in Non Peer Reviewed Issue of American
   Economic Review.
   -> Cited by politicians worldwide in debates to prove
   effectiveness of austerity in fiscal policies for debt burdened
   economies.
   -> When “Gross external debt reaches 60 percent of GDP", a
   country's annual growth declined by two percent, and "for
   levels of external debt in excess of 90 percent" GDP growth
   was "roughly cut in half.”
   -> Proved Wrong!
   

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7. Catch?
   Science needs explanation. Papers can be hard to
   understand. Excel is not made for this. You can’t
   document code, logic and results in excel!
   

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8. More Issues with using Excel for Science
   

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10. Is there a better way?
    -> Is there a better way to have the text, analysis,
    results, code, plots, comments in one place?
    -> Is it possible to write it sequentially such that
    anyone going through the code/paper/book could
    not just understand it, but run it, reproduce results,
    find flaws and suggest enhancements 1000X faster?
    -> Is it possible to distribute the results properly?
    

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11. What would be a better way?
    -> Excel users are people avoiding hardcore
    programming languages.
    -> Easy interface to write logic
    -> Low Cognitive Complexity
    -> Easy Syntax (Preferably like English) [Think BQL
    (Bloomberg Query Language)]
    

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12. Python stands out!
    -> Easy to grab!
    -> Easy interface to write logic
    -> Low Cognitive Complexity
    -> Mostly like writing english.
    -> Out of the box support, extremely friendly
    community.
    -> Democratic
    

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13. Results?
    

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14. Rise of
    iPython
    

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15. iPython?
    -> First version in 2001 (Started with just 259 lines)
    -> Inbuilt support for parallelization came in 2004
    -> Support for running the code in remote cluster from the
    shell.
    -> Inspired by Mathematica
    -> Boon for scientific computing
    

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17. Rise to
    fame!
    

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18. What are notebooks?
    -> Notebook is a rather old concept.
    -> Sequentially written logic
    -> Interactive
    -> Saves State (like a paper notebook does)
    

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20. Benefits of using iPython
    -> obj.[tab] , obj? , obj??
    -> files = !ls , !wget $url
    -> iPython Magic:
    - %run script.py (-p -> profile, -t -> time)
    - %debug (jump in after an exception)
    - %lsmagic (See the rest of magics)
    

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21. Some myths
    -> There’s no support
    -> Won’t be free forever
    -> Free software has bugs
    

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22. Some myths
    -> There’s no support
    Enthought, Continuum Analytics
    -> Won’t be free forever
    -> Free software has bugs
    

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23. Some myths
    -> There’s no support
    Enthought, Continuum Analytics
    -> Won’t be free forever
    Free software Belongs to the community
    -> Free software has bugs
    

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24. Some myths
    -> There’s no support
    Enthought, Continuum Analytics
    -> Won’t be free forever
    Free software Belongs to the community
    -> Free software has bugs
    Naturally!
    

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25. And it happened
    

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26. Jupyter
    or
    Jupiter?
    

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27. What is Jupyter?
    

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28. Jupyter : An Ecosystem
    -> JupyterLab
    -> Slides/Documents
    -> Write Books (O’Reilly) - jupyterbook
    -> JupyterHub
    -> Collab Notebooks, binder
    

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29. Jupyter : A way to teach in classrooms!
    -> Easy to create interactive tutorials which lets
    students play around with the code.
    -> Perform Live Coding, share lecture notes and
    materials
    -> Grade homeworks
    -> No need to run every script supplied by
    students
    

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30. Project Jupyter
    -> Separation of the language agnostic
    components
    - Jupyter : protocol, format, multi-user server
    - iPython : Jupyter Kernel, interactive python
    -> Jupyter Kernels: Languages which can be
    used in notebook: ~ 100 programming
    languages.
    

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31. Notebook Extensions
    -> Add ons to extend functionality. Much like VS
    Code extensions.
    -> VS Code supports Jupyter
    -> Written in JavaScript, send browser
    notifications, autoformat code. There are
    immense possibilities.
    

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32. Widgets
    -> Interact with the code output!
    -> Have slide bars, text boxes, inputs
    -> Like mini-GUIs
    -> Very helpful when working with hyper
    parameters.
    -> You can write one for yourself!
    

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33. Widgets
    

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34. Widgets
    

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35. Version Control in Notebooks
    -> The notebooks have extension .ipynb but are
    plain text files and are represented in JSON.
    -> The diffs used to be pretty large earlier even
    when nothing changed. Storing output is another
    issue.
    -> It’s getting better. With nbdime, it’s much easier
    to merge and compare now.
    

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36. Using Notebooks in Production!
    

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37. Issues with Jupyter Notebooks
    -> Hidden states in the Notebooks
    

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38. Issues with Jupyter Notebooks
    -> Hidden states in the Notebooks
    

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39. Issues with Jupyter Notebooks
    Notebooks are great for iterative development
    BUT
    Notebooks are *very* dangerous unless you run
    each cell only ONCE in CORRECT ORDER.
    

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40. A way to solve this...
    The %history magic!
    But you shouldn’t have to run a magic to find the
    state!
    

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41. Now take this!
    

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42. Problem number 2
    The ability to run a code in a non sequential arbitrary
    order is counter intuitive to most programmers out
    there.
    Can be daunting for beginners!
    

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43. Notebooks in Cloud
    -> Notebooks as a service is a cool new thing!
    -> Azure Notebooks, Collab Notebooks and what
    not.
    -> You can create your own notebook service!
    

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44. How does a Jupyter Notebook Work?
    Credits: Carol Willing
    

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45. What does JupyterHub do?
    -> Manages Authentication
    -> Spawns single-user notebook servers on demand
    -> Gives a user their complete notebook server!
    -> Hub and server are different entities.
    

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46. JupyterHub
    

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47. Parts of JupyterHub
    -> The Hub: User Database, Auth, and Spawner
    -> Users and their individual notebook servers
    -> Configurable HTTP Proxy
    The auth supports OAuth, pem etc.
    Deploy: https://github.com/jupyterhub/jupyterhub-deploy-docker
    

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48. Real Time Collaboration
    Jupyter RTC: https://jupyter-rtc.readthedocs.io/
    This works directly with JupyterLab
    

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49. Summary
    -> Jupyter / iPython is a useful tool, not only for
    coding but also for teaching, sharing, documenting,
    publishing!
    -> We don’t have to throw away previous work in
    different languages, now we can integrate them.
    -> Jupyter is gaining relevance in Open Science,
    Finance, Music, Teaching. We must go further!
    

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50. Questions?
    Semut Twitter: @semut_io
    GitHub: @shreyasbapat
    Twitter: @shreyasb94
    

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