Exploring Literate Programming For Python Projects With nbdev

Summary

Creating well designed software is largely a problem of context and understanding. The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked. In order to weave all of these concerns together there have been many efforts to create a literate programming environment. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. The explain how it allows you to weave together documentation, code, and tests in the same context so that it is more natural to explore and build understanding when working on a project. It is built on top of the Jupyter environment, allowing you to take advantage of the other great elements of that ecosystem, and it provides a number of excellent out of the box features to reduce the friction in adopting good project hygiene, including continuous integration and well designed documentation sites. Regardless of whether you have been programming for 5 days, 5 years, or 5 decades you should take a look at nbdev to experience a different way of looking at your code.

Announcements

• Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
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• Your host as usual is Tobias Macey and today I’m interviewing Jeremy Howard and Hamel Husain about nbdev, a library for turning Jupyter notebooks into Python libraries.

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by describing what nbdev is and the goals of the project?
  • What is the story behind how and why it got started?
• Who is the target audience for the nbdev project?
  • How does that focus influence the features and design of nbdev?
• What do you see as the primary challenges of building and collaborating on projects written in notebooks?
• What are some of the other projects that are working to simplify or improve the experience of using notebooks?
  • How does nbdev compare to or complement those other tools?
• Can you describe how nbdev is implemented?
  • How has the design and goals of the project evolved since it was first started?
• What is the workflow of someone who is using nbdev?
  • At what point in the lifecycle of a notebook oriented project should someone start integrating nbdev?
• How does nbdev scale when working on a project that spans multiple notebooks/modules?
• How does working in a notebook environment change your approach to software development and project design?
• What are the most interesting, innovative, or unexpected ways that you have seen nbdev used?
• What are the most interesting, unexpected, or challenging lessons that you have learned from working on nbdev?
• When is nbdev the wrong choice?
• What do you have planned for the future of the project?

Keep In Touch

• Jeremy
  • LinkedIn
  • @jeremyphoward on Twitter
  • jph00 on GitHub
• Hamel
  • hamelsmu on GitHub
  • Website
  • @HamelHusain on Twitter
  • LinkedIn

Picks

• Tobias
  • Rivals! Frenemies Who Changed The World
• Jeremy
  • Chess
• Hamel
  • Moonwalking With Einstein by Joshua Foer (affiliate link)

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

• nbdev
• fast.ai
• GitHub
• Perl
• Fastmail
• R Studio
• R Markdown
• Literate Programming
• fastcore
• JupyterLab
• nteract
• Jupyter Voilà
• GitHub Actions
• Sphinx
• Google Colab
• Working In Public by Nadia Eghbal (affiliate link)
• Jekyll
• Hugo
• Cython
  • Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA