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Can you describe how nbdev is implemented?.How does nbdev compare to or complement those other tools?.What are some of the other projects that are working to simplify or improve the experience of using notebooks?.What do you see as the primary challenges of building and collaborating on projects written in notebooks?.How does that focus influence the features and design of nbdev?.Who is the target audience for the nbdev project?.What is the story behind how and why it got started?.

Can you start by describing what nbdev is and the goals of the project?.

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.

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Hello and welcome to Podcast._init_, the podcast about Python and the people who make it great.Go to /linode today and get a $100 credit to launch a new cluster, run a server, upload some data, or… And don’t forget to thank them for being a long time supporter of Podcast._init_! With the combined power of the leading container orchestrator and the speed and reliability of Linode’s object storage, node balancers, block storage, and dedicated CPU or GPU instances, you’ve got everything you need to scale up. 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.ĭo you want to try out some of the tools and applications that you heard about on Podcast._init_? Do you have a side project that you want to share with the world? With Linode’s managed Kubernetes platform it’s now even easier to get started with the latest in cloud technologies. 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. 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. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. In order to weave all of these concerns together there have been many efforts to create a literate programming environment.

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The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked.

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Creating well designed software is largely a problem of context and understanding.