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SUMMARY:Deploying and debugging GPU accelerated Python workloads - Jacob T
 omlinson
DTSTART;TZID=Poland:20260723T140000
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DTSTAMP:20260911T015713Z
UID:pretalx-euroscipy-2026-LL8N7L@pretalx.com
DESCRIPTION:Leveraging GPU acceleration is now a common necessity for scal
 ing Python projects. NVIDIA GPUs offer unmatched speed and efficiency for 
 data processing and model training\, significantly reducing the time and c
 ost associated with these tasks. GPU acceleration is already baked into ma
 ny projects\, or available via plugins. You can use PyData libraries inclu
 ding pandas\, polars and networkx without needing to rewrite your code to 
 get the benefits of GPU acceleration. \n\nHowever\, integrating GPUs into 
 our workflow can be a new challenge where we need to learn about installat
 ion\, dependency management\, and deployment in the Python ecosystem. When
  writing code\, we also need to monitor performance\, leverage hardware ef
 fectively\, and debug when things go wrong\n\nThis is where RAPIDS and its
  tooling ecosystem comes to the rescue. RAPIDS\, is a collection of open s
 ource software libraries to execute end-to-end data pipelines on NVIDIA GP
 Us using familiar PyData APIs.\n\nIn this tutorial we will cover:\n- Answe
 rs to questions like: “Where do I get a GPU?”\, “How do I run a cont
 ainer on a VM with a GPU?”\, “How do I install GPU packages into an ex
 isting environment?”\, “What if I use uv pip?”\, “What about conda
 ? ”as well as follow along examples to get a GPU up and running.\n- Trou
 bleshooting and monitoring:  Examples of performance analysis\, diagnostic
 s\, and debugging. Showcasing of diagnostic tools like nvdashboard\, nvtop
 \, nsys\, pynvml\, etc.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/LL8N7L/
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