Matthew Feickert
Matthew is a research scientist in experimental high energy physics and data science at the University of Wisconsin-Madison Data Science Institute (a "data physicist"). He works as a member of the ATLAS collaboration on searches for physics beyond the standard model with experiments performed at CERN's Large Hadron Collider (LHC) in Geneva, Switzerland. He also serves on the executive board of the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) where he is a researcher and the Analysis Systems Area lead. He is also a topical editor for physics and data science for the Journal of Open Source Software.
Matthew has served on the SciPy Organizing Committee since 2020, with roles as co-chair of the Physics and Astronomy specialized track and co-chair of the Program Committee.
Sessions
Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms. Modern open source tools, like pixi, provide automatic reproducibility solutions for all dependencies while providing a high level interface well suited for researchers.
This tutorial will provide a practical introduction to using pixi to easily create scientific and AI/ML environments that benefit from hardware acceleration, across multiple machines and platforms. The focus will be on applications using the PyTorch and JAX Python machine learning libraries with CUDA enabled, as well as deploying these environments to production settings in Linux container images.