Tim Head
I am a scikit-learn core maintainer and work at NVIDIA.
Before working on scikit-learn I helped build mybinder.org and worked on JupyterHub.
Many years ago I was a particle physicist at CERN in Geneva.
NVIDIA
Homepage – Twitter handle –@betatim
Git*hub|lab –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.
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