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UID:pretalx-euroscipy-2026-LL8N7L@pretalx.com
DTSTART;TZID=CET:20260723T140000
DTEND;TZID=CET:20260723T153000
DESCRIPTION:Leveraging GPU acceleration is now a common necessity for scali
 ng Python projects. NVIDIA GPUs offer unmatched speed and efficiency for d
 ata processing and model training\, significantly reducing the time and co
 st associated with these tasks. GPU acceleration is already baked into man
 y projects\, or available via plugins. You can use PyData libraries includ
 ing pandas\, polars and networkx without needing to rewrite your code to g
 et the benefits of GPU acceleration. \n\nHowever\, integrating GPUs into o
 ur workflow can be a new challenge where we need to learn about installati
 on\, dependency management\, and deployment in the Python ecosystem. When 
 writing code\, we also need to monitor performance\, leverage hardware eff
 ectively\, 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 so
 urce software libraries to execute end-to-end data pipelines on NVIDIA GPU
 s using familiar PyData APIs.\n\nIn this tutorial we will cover:\n- Answer
 s to questions like: “Where do I get a GPU?”\, “How do I run a conta
 iner on a VM with a GPU?”\, “How do I install GPU packages into an exi
 sting environment?”\, “What if I use uv pip?”\, “What about conda?
  ”as well as follow along examples to get a GPU up and running.\n- Troub
 leshooting and monitoring:  Examples of performance analysis\, diagnostics
 \, and debugging. Showcasing of diagnostic tools like nvdashboard\, nvtop\
 , nsys\, pynvml\, etc.
DTSTAMP:20260603T201710Z
LOCATION:Room 1.19 (Ground Floor\, Shannon)
SUMMARY:Deploying and debugging GPU accelerated Python workloads - Jacob To
 mlinson
URL:https://pretalx.com/euroscipy-2026/talk/LL8N7L/
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