List of videos

Presented by: Steven Lott Since PEP 484 in 2014, type hints and the mypy tool have become an important part of the Python ecosystem. Type hints provide an incremental way to add information to the Python source, which can be checked by external tools like mypy and some IDE environments like PyCharm. Type hint checking can be part of a CI/CD pipeline to provide confidence that the code is likely to behave properly at run-time. Python’s Duck-Typing approach can lead to working code that is hard to describe with available type hints. This tutorial expects hands-on participation with a large number of small examples. It will start with some simple cases of type hints. We’ll look at complex data structures and ways to factor the complexity out of the type hints. We’ll look at circular type references, common on problems where graphs are represented. The techniques for “debugging” type hints will focus on exposing the mypy reasons about the source code. We’ll look at two very easy and useful ways to incorporate type hints into class definitions using the typing.NamedTuple class and the @dataclass decorator. We’ll also look at situations where the type: ignore comment can be appropriate. Another important topic will touch on how we can write a stub for a library where the code doesn’t have type hints.

Presented by: Casey Faist In this workshop, we will take a Django application from development to ready for deployment on Heroku. We’ll talk about 12 Factor Django apps and why you’d want one, then work through the configuration updates you’ll need to make your application robust and Heroku ready. We’ll take a quick tour of your Heroku Dashboard. We’ll add some human complexity and talk about how to collaborate with others and how to control and share access on Heroku, and then some debugging tips along the way. You’ll leave with everything you need to hit the ground running with Django on Heroku. heroku.com/python Prerequisites: https://signup.heroku.com/ https://devcenter.heroku.com/articles/heroku-cli#download-and-install https://github.com/heroku-python/PyCon2020

Presented by: Patrick Lysaght Imagine if we could combine the benefits of Raspberry PI microprocessors, Arduino microcontrollers and the programmable logic of a field programmable gate array (FPGA) in a single development platform? What if we could connect all of these components together with high speed interconnect and add lots of programmable, high-speed IO? That would be quite the embedded development dream machine! Things would be even more exciting if we could integrate all of the parts into a single programmable system-on-chip. But wait … hardware, without good software, is just scrap metal! We need to program the machine with a user-friendly programming language with hundreds of thousands of libraries, so that we would not have to re-write code for problems that we know have already been solved. And since this is a wish-list for our dream machine, we should make sure to include libraries for machine learning and data science because these are some of the hottest areas that we want to explore. This workshop will introduce PYNQ, based on Xilinx programmable System-on-Chips. PYNQ serves up a browser-based, integrated development environment (IDE) for Python, so that new users can experience the power of Xilinx platforms without having to install new software, or create their own FPGA hardware designs. Meanwhile, PYNQ makes experienced developers more productive and helps them to create, document, and distribute their designs, more effectively. Website: pynq.io

Presented by: Keith Kraus, Bartley Richardson As data volumes and computational complexity of data analysis techniques have increased, so has the need for acceleration of these workloads to allow the data scientist to quickly iterate on models. One of the key ways to achieve this has been through GPU acceleration. Traditionally, GPU acceleration has required specialized knowledge of low-level C++ GPGPU programming. However, the open-source RAPIDS data science libraries allow data scientists to easily make use of GPU acceleration in common ETL, machine learning, and graph analytics workloads using familiar Python APIs (e.g. pandas and scikit-learn). This workshop will introduce RAPIDS, walk through its component libraries, and will show participants how these libraries allow them to easily introduce GPU acceleration into their workflows to speed up compute times and increase iteration on their models. We will demonstrate common data ETL (cuDF), machine learning (cuML), graph analytics (cuGraph), signal processing (cuSignal), spatial analytics (cuSpatial), and InfoSec (cyber log accelerator) workloads that RAPIDS accelerates. We will also discuss how users can integrate RAPIDS and the broader open-source GPU data science ecosystem to solve their specific use cases. An understanding of basic data science concepts will be helpful, but is not required. No experience with GPU programming is required!

Full Title: How Python Powers Salesforce.org's Unique Open Source Model Salesforce.org’s core products are the most installed open source packages run on top of Salesforce’s enterprise platform. The processes and open source tools we’ve built using Python to streamline and scale our internal development of those packages are also used by our community to build shared solutions on top of our products. Inspired by our community’s desire to share, we created the Salesforce.org Open Source Commons, a program that incubates and fosters sustainable, community run open source projects for the nonprofit and education sectors. In this workshop, we’ll show how the open source tooling we built using Python and some of the best open source projects from the Python ecosystem have helped us get closer to our vision of fostering open source collaboration beyond just developers. You’ll learn how you can apply your knowledge of Python to better understand the Salesforce platform and build Salesforce integration into your own Python web apps. With this knowledge, you’ll be empowered to volunteer with the 30k+ nonprofit and education organizations already using Salesforce and learn how Python application developers can easily build repeatable, scalable Salesforce solutions to integrate your Python applications with the most popular enterprise CRM in the world.

ActiveState builds Python from source for every runtime we distribute. We build every package from source too. For Linux and MacOS that’s a pretty straightforward task but for Windows… it’s another story. In this workshop, Shaun Lowry walks us through what it takes to build Python from source for Linux, MacOS, and Windows, deep dives into the challenges involved, and illustrates why that effort is imperative to our users. Join us for an insider’s view of some of our toughest problems, how both the strict constraints of enterprise and the needs of our users necessitates them, and get a peek at our plans for contributing our work upstream for the Python community at large. Audience: This workshop is geared toward a technical audience and will probably resonate most with developers who’ve ever had to build software from source.

Presented by: Eric J. Ma Have you ever wondered what goes on behind the scenes of a deep learning framework? Or what is going on behind that pre-trained model that you took from Kaggle? Then this tutorial is for you! In this tutorial, we will demystify the internals of deep learning frameworks - in the process equipping us with foundational knowledge that lets us understand what is going on when we train and fit a deep learning model. By learning the foundations without a deep learning framework as a pedagogical crutch, you will walk away with foundational knowledge that will give you the confidence to implement any model you want in any framework you choose.

Presented by: Matt Harrison Are you new to Python? Or do you feel like you grok the syntax, but would like to understand new idioms or where to use them? Want to watch an experienced Python developer create code from nothing? Instead of just covering the syntax, we will introduce most of Python as we build code together. Bring your laptop, and we will program a predictive text engine from scratch together. Follow along as we start with IDLE (or your favorite editor) and a blank file and end with a tested, idiomatic Python module that will learn from any text we pass into it, and predict characters or words for us.

Presented by: Andrew Knight When unit tests aren’t enough, how can we write reliable automated tests for Web apps in live browsers? It’s easy with Python! Let’s build a test project from the ground up using pytest and selenium to test DuckDuckGo searches. We’ll take a top-down approach and get our hands dirty with automation code at each layer. Learn everything from switching browsers to avoiding race conditions!