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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:20260715T022617Z
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/
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