Sebastian Berg
Sebastian has been a NumPy developer for about 10 years now. After a PhD in phsyics he worked at as a postdoc at the Berkeley Institute for Datascience on NumPy as grants byt the Alfred P. Sloan Foundation and the Gordon and Betty Moore Foundation. Since 2022 he has been a software engineer at NVIDIA where he continues to contribute to NumPy.
NVIDIA
Sessions
Python code that works with Numpy, Cupy and PyTorch arrays? Use a GPU when possible, but fallback to using a CPU if there is none? We will show you how you can write Python code that can do all of the above. The not so secret ingredient to do this is the Array API. In this workshop you will learn what the Array API is and how to use it to write programs that can take any compatible array as input.
Open-source projects are essential for scientific programming. They provide many tools and resources that can be customized for different scientific needs. However, sometimes the existing tools in a package don't meet all the requirements of a project. This is when contributing to open-source packages becomes important. By contributing, you can implement new functionalities, improve the software and help keep the open-source community strong.
This workshop will make contributing to open-source projects easier to understand. It will guide participants from just using the software to actively contributing to it. The workshop will address technical challenges such as interacting with web-based hosting services (like GitHub and GitLab), branching, and opening pull requests. Additionally, it will cover how to contribute documentation and ensure the correctness of the code.
We will use the following repository during the workshop: https://github.com/Nikoleta-v3/HitchCos.
You can find a checklist of prerequisites and installation notes here: https://github.com/Nikoleta-v3/HitchCos/wiki/Prerequisites.
NumPy 2 had some significant changes in its API and required many downstream libraries and users to adapt.
One of the larger new features is that the new DType API is now public. This C-API allows more powerful user defined DTypes, for which the new StringDType is an example. In the first part, I will give a brief overview of this API.
Since many downstream projects needed to adapt and publish new versions, in the second part I recap the current and past difficulties in transitioning to NumPy 2. This part of the session will be a forum for open discussion to gauge the challenges faced by users in making this transition.
Scientific python libraries struggle with the existence of several array and dataframe providers. Many important libraries currently mainly support NumPy arrays or pandas dataframes.
However, as library authors we wish to allow users to smoothly use other array provides and simplify for example the use of GPUs without the need for explicit use of cuda enabled libraries.
This session will be split into three related discussions around efforts to tackle this situation:
* Dispatching and backend selection discussion
* Array API adoption progress and discussion
* Dataframe compatibility layer discussion