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UID:pretalx-scipy-2026-XSWVVE@pretalx.com
DTSTART;TZID=CST:20260714T080000
DTEND;TZID=CST:20260714T120000
DESCRIPTION:As GPU acceleration becomes essential for scaling Python worklo
 ads\, many developers face new challenges: understanding installation\, ma
 naging dependencies\, and deploying GPU-enabled environments. Even experie
 nced Python users can struggle to integrate GPUs effectively or troublesho
 ot performance issues.\n\nThis tutorial addresses those barriers by walkin
 g participants step-by-step through the process of getting started with GP
 Us. Using NVIDIA’s RAPIDS ecosystem and familiar python tools\, we’ll 
 demonstrate how to set up\, monitor\, optimize and debug GPU-powered workf
 lows—turning what often feels like complex infrastructure work into an a
 pproachable\, reproducible process.\n\nInstallation Instructions: https://
 developer.nvidia.com/nsight-systems/get-started
DTSTAMP:20260715T021041Z
LOCATION:Accelerated Computing
SUMMARY:Deploying and debugging GPU accelerated Python workloads (Room HSEC
  2-110) - Naty Clementi\, Jacob Tomlinson\, Jaya Venkatesh
URL:https://pretalx.com/scipy-2026/talk/XSWVVE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-scipy-2026-97QQ8D@pretalx.com
DTSTART;TZID=CST:20260715T135500
DTEND;TZID=CST:20260715T142500
DESCRIPTION:Geospatial analysis relies on raster data — n-dimensional arr
 ays where each cell holds a spatial measurement. The scale of modern remot
 e sensing data makes CPU-based workflows impractical\, but raster operatio
 ns are naturally parallelizable and well suited for GPU acceleration. This
  talk walks through a GPU-accelerated end-to-end workflow to classify sate
 llite imagery into land cover types\, covering data access via [STAC](http
 s://stacspec.org/en)\, preprocessing (cloud masking\, compositing\, spectr
 al index computation)\, training a Random Forest classifier on millions of
  pixels\, and running inference on unseen tiles. The pipeline uses familia
 r APIs from Xarray\, Dask\, pandas\, and scikit-learn\, accelerated with R
 APIDS. No prior geospatial or GPU experience is required.
DTSTAMP:20260715T021041Z
LOCATION:Thomas Swain Room
SUMMARY:Accelerating Geospatial Analysis with GPUs - Jaya Venkatesh\, Jacob
  Tomlinson\, Naty Clementi
URL:https://pretalx.com/scipy-2026/talk/97QQ8D/
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:20260715T021041Z
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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