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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:20260715T021114Z
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/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-scipy-2026-MEKP9F@pretalx.com
DTSTART;TZID=CST:20260716T112500
DTEND;TZID=CST:20260716T115500
DESCRIPTION:After 3 years\, Pixi is widely adopted in the scientific Python
  ecosystem. At SciPy 2026\, we want to show why.\n\nScientific Python has 
 specific challenges that Pixi can solve well\; a lot of our beloved packag
 es contain C\, C++\, Rust\, CUDA or even Fortran code. With Pixi\, a singl
 e tool can install the compilers\, different Python versions and other bui
 ld tools in one go\, thanks to piggy backing on the years of development t
 hat the Conda ecosystem has seen.\n\nThanks to Pixi’s task system and na
 tive multi-platform capabilities\, the contributor experience is also enha
 nced. Daunting tasks like running CMake\, installing the correct Rust vers
 ion or C++ compilers are all hidden away behind a magical: `pixi run fooba
 r`.\n\nAre you interested to see how you could improve your own workflow a
 nd learn from what these big open-source projects are doing? Then you shou
 ld join this talk! You'll be amazed by what is possible these days.
DTSTAMP:20260715T021114Z
LOCATION:Memorial Hall
SUMMARY:Scipy\, Numpy\, Xarray and Python all have a pixi.toml. Why? - Rube
 n Arts
URL:https://pretalx.com/scipy-2026/talk/MEKP9F/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-scipy-2026-WFXBKQ@pretalx.com
DTSTART;TZID=CST:20260717T174500
DTEND;TZID=CST:20260717T184000
DESCRIPTION:Until very recently\, producing and using reproducible scientif
 ic software environments required advanced knowledge and a strict adherenc
 e to best practices (e.g. DOI: 10.25080/majora-212e5952-028). Now\, with t
 he advent of modern tooling with lockfile-first workflows (i.e. Pixi and u
 v)\, and the emergence of lockfile standards across scientific open source
 \, applications can be made reproducible at the digest level through tooli
 ng decisions. As this technology and practices become increasingly common 
 there is an opportunity to define common best practices around lockfile ba
 sed software development that can further reduce developer overhead and ma
 intenance burden. This Birds of a Feather panel will focus on how experien
 ced developers are leveraging lockfiles across software development\, appl
 ications\, and deployment while providing best practices and practical rec
 ommendations\, while also highlighting continuing challenges and opportuni
 ties for improvement.
DTSTAMP:20260715T021114Z
LOCATION:Johnson Great Room
SUMMARY:Lockfile-based development and applications - Naty Clementi\, Matth
 ew Feickert\, Ruben Arts\, Gil Forsyth\, Henry Schreiner
URL:https://pretalx.com/scipy-2026/talk/WFXBKQ/
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