Embrace Literate Programming without Compromise: nbdev for Collaborative Project

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Topic: Embracing Literate Programming without Compromise: nbdev for Collaborative Projects

Speaker: Zachary Mueller, Deep Learning Software Engineer at Novetta

Bio:
Zachary Mueller is focusing on Natural Language Processing Applications. He will be graduating soon with a degree in Senior Software Design and Development Major and with minors in Computer Science and Environmental Science. An expert in the fastai framework, much of his outside work is contributing to the fastai libraries and open source communities.

Abstract:
Data scientists also typically work out of Jupyter Notebooks, or a similar Python-sandbox environment where code can be run from an interactive GUI. As a result, data scientists find themselves bouncing between notebooks and production code, and they generally write their documentation only once development is complete. The ideal workflow for data scientists would keep this sandbox environment while retaining the test-driven development pipeline used by software developers.

This is what the library “nbdev” aims to achieve.
A potential barrier to the adoption of “nbdev” is that converting existing projects over to the nbdev workflow takes far too long. For this reason, many organizations and projects are reluctant to make the jump into the “exploratory programming” workflow that nbdev offers. Novetta developed a library, lib2nbdev, that successfully bridges this hurdle by providing a one-time conversion of existing Python libraries into nbdev workflows. Lib2nbdev eases adoption of nbdev, increasing the productivity and quality of programming projects by putting good software practices within reach in a familiar Jupyter environment.

In this talk, attendees will get an overview of nbdev and the concept of exploratory programming, then delve into lib2nbdev, exploring use cases and benefits of each technology.

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