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UID:pretalx-scipy-2026-9FQMMN@pretalx.com
DTSTART;TZID=CST:20260713T133000
DTEND;TZID=CST:20260713T173000
DESCRIPTION:Scientific researchers need reproducible software environments 
 for complex applications that can run across heterogeneous computing platf
 orms. Modern open source tools\, like [Pixi](https://pixi.sh/)\, provide a
 utomatic reproducibility solutions for all dependencies while providing a 
 high level interface well suited for researchers.\n\nThis tutorial will pr
 ovide a practical introduction to using Pixi to easily create scientific a
 nd AI/ML environments that benefit from hardware acceleration\, across mul
 tiple machines and platforms. The focus will be on CUDA applications\, suc
 h as machine learning frameworks and use of CUDA Tile\, as well as using p
 ixi-build to construct bespoke CUDA enabled conda packages.\n\nInstallatio
 n Instructions: https://matthewfeickert-talks.github.io/reproducible-cuda-
 workflows-with-pixi-scipy-2026/setup/
DTSTAMP:20260715T023004Z
LOCATION:Accelerated Computing
SUMMARY:Reproducible CUDA Accelerated Workflows for Scientists with Pixi (R
 oom HSEC 2-138) - Matthew Feickert\, Ruben Arts\, Katrina Riehl
URL:https://pretalx.com/scipy-2026/talk/9FQMMN/
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