List of videos

Carol Willing Thomas Wouters Brett Cannon Pablo Galindo Salgado Barry Warsaw Elected as prescribed in PEP 8016, the Python Steering Council is a 5-person committee that assumes a mandate to maintain the quality and stability of the Python language and CPython interpreter, improve the contributor experience, formalize and maintain a relationship between the Python core team and the PSF, establish decision making processes for Python Enhancement Proposals, seek consensus among contributors and the Python core team, and resolve decisions and disputes in decision making among the language. This keynote will update the community on current and future initiatives. Additionally, the Steering Council will address community questions collected prior to the conference.

Ewa Jodlowska, Executive Director addresses the community and announces the Community Service Award recipients

Over the years, testing has become one of the main focus areas in development teams, a good feature is a well tested one. In the field of AI this is many times a real struggle. Since eventually most advanced AI models are stochastic - we can’t manually define all their possible edge cases. This led us to use the hypothesis library which does a lot of that for you, while you can focus on defining the properties and specifications of your system. In this talk, I will cover shortly the theory of property-based testing and then jump into use cases and examples to demonstrate how we used the hypothesis library to generate random examples of plausible edge cases of our AI model.

Workflow-oriented systems have many uses, including data processing and analysis, ETL, CI/CD, and more. But creating a programmatic interface to a workflow system is a delicate balancing act: we want the API to be flexible enough to support useful work, but also constrained enough that tasks run cooperatively within the larger system. We faced this challenge when designing the task API for the Pants build system. We needed to allow custom task code to enjoy the benefits of complex features like caching, concurrency and remote execution, without every task author having to reason about them. In this talk we'll show how we found the right balance through unconventional use of Python's type annotations, coroutines, and dataclasses. Combining these seemingly disparate features in the context of a workflow engine allows you to build elegant extensibility APIs with just the right amount of flexibility. Slides: https://docs.google.com/presentation/d/1aWZjk3tZUp37RDmZZxy0j8OjbatzkuWLfuSh84Lzwfk/edit?usp=sharing

The game engine, Ren'Py, is an open source engine used to make countless interactive fiction games, also known as visual novels (VNs). These include commercial hits with VN elements such as Persona 5, to viral works such as Doki Doki Literature Club (2mil+ downloads as of Jan 2018). I learned to program in Python using this engine, and have released my commercial game with it after working for a few years on it during weekends, selling 6K+ copies in less than half a year. In the daytime, I work as a principal data scientist in fintech. Anyhow, the talk will dig into the source code of the engine, https://github.com/renpy/renpy, such as: How it takes care of OS level stuff for game developers, memory optimization, cross platform game saves, and all that cool stuff. Outcome: The audience will understand the independent gaming industry and how they can use Python to break into the industry, as I share my journey. There will also be components of source code walkthrough, but will be more of an overview than a step by step tutorial due to the scope of the talk.

When you hear about beautiful notebook automation, your first thought usually goes to what Netflix is doing with Jupyter Notebooks. Their work is heavily inspired by nteract's papermill, which allows for the parameterization, execution, and analysis of Jupyter notebooks. Notebook operationalization opens many doors for team's troubleshooting pipelines, and we wanted to learn more from the open source community for how we can work together to empower developers who are on-call. Join us for this code-focused session to hear about our journey of listening and learning to the open source community, and how we used Python to evolve notebook parameterization.

NumPy has grown to be a vital part of the data science workflow for everyone from astrophysicists to zoologists. This talk is about how PyTorch approaches being “NumPy-compatible,” and why the PyTorch community thinks that’s important, why it can be challenging, and why sometimes it’s necessary to be divergent from NumPy’s behavior. Slides: https://www.slideshare.net/MikeRuberry1/from-numpy-to-pytorch

If you're building something for developers, you want it to get used. This means your potential users need to find your library, framework, or API. They need to work out whether it's useful for them, learn how to use it, and solve problems they encounter along the way. All these things depend on your developer docs! Docs aren't just docs: They're your UI, your marketing, and they - not your code - define what your product is. This talk talk about important functions of your docs that you might not think about, and then some particular pitfalls of documenting things for developers. Slides: https://drive.google.com/file/d/1K93TsQ4s39X70vvzckPpdke7srzMTXZ6/view

Code produced by data scientists is under attack! There are a growing series of conference talks, Medium blog posts and business stakeholders telling a story of how changing business objectives are driving interest in production-level code. Production-level code is considered time-consuming to produce and limiting for the experimentation process needed to create amazing models. You're going to follow a workflow that deconstructs your experimentation workflow in a Jupyter notebook and helps you create production-ready ML pipelines. The talk is focused on an open source Python framework, called kedro that emphasises creating reproducible, maintainable and modular data science code. Documentation: https://kedro.readthedocs.io/en/stable/ GitHub Repository: https://github.com/quantumblacklabs/kedro Slides: https://speakerdeck.com/yetudada/reproducible-and-maintainable-data-science-code-with-kedro?slide=18