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SUMMARY:Reproducible Machine Learning Workflows for Scientists with pixi -
  John Kirkham\, Matthew Feickert\, Ruben Arts
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DTSTAMP:20261007T044045Z
UID:pretalx-scipy2025-GDN8PN@cfp.scipy.org
DESCRIPTION:Scientific researchers need reproducible software environments
  for complex applications that can run across heterogeneous computing plat
 forms. Modern open source tools\, like [`pixi`](https://pixi.sh/)\, provid
 e automatic reproducibility solutions for all dependencies while providing
  a high level interface well suited for researchers.\n\nThis tutorial will
  provide a practical introduction to using `pixi` to easily create scienti
 fic and AI/ML environments that benefit from hardware acceleration\, acros
 s 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 c
 ontainer images.
LOCATION:Ballroom C
URL:https://cfp.scipy.org/scipy2025/talk/GDN8PN/
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