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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:20260715T021115Z
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
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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:20260715T021115Z
LOCATION:Thomas Swain Room
SUMMARY:Accelerating Geospatial Analysis with GPUs - Jaya Venkatesh\, Jacob
  Tomlinson\, Naty Clementi
URL:https://pretalx.com/scipy-2026/talk/97QQ8D/
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