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SUMMARY:GPU Python for the Real World: Practical GPU-Accelerated Python wi
 th RAPIDS - Jacob Tomlinson
DTSTART;TZID=Europe/Warsaw:20250819T133000
DTEND;TZID=Europe/Warsaw:20250819T150000
DTSTAMP:20260912T143014Z
UID:pretalx-euroscipy-2025-CBPMEK@pretalx.com
DESCRIPTION:NVIDIA GPUs offer unmatched speed and efficiency for data proc
 essing and model training\, significantly reducing the time and cost assoc
 iated with these tasks. Using GPUs is even more tempting when you use zero
 -code-change plugins and libraries. You can use PyData libraries including
  pandas\, polars and networkx without needing to rewrite your code to get 
 the benefits of GPU acceleration. We can also mix in GPU native libraries 
 like Numba\, CuPy and pytorch to accelerate our workflows from end-to-end.
 \n\nHowever\, integrating GPUs into our workflow can be a new challenge wh
 ere we need to learn about installation\, dependency management\, and depl
 oyment in the Python ecosystem. When writing code\, we also need to monito
 r performance\, leverage hardware effectively\, and debug when things go w
 rong\n\nThis is where RAPIDS and its tooling ecosystem comes to the rescue
 . RAPIDS\, is a collection of open source software libraries to execute en
 d-to-end data pipelines on NVIDIA GPUs using familiar PyData APIs.
LOCATION:Room 1.38 (Ground Floor)
URL:https://pretalx.com/euroscipy-2025/talk/CBPMEK/
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SUMMARY:EffVer: Versioning code by the effort required to upgrade - Jacob 
 Tomlinson
DTSTART;TZID=Europe/Warsaw:20250821T114000
DTEND;TZID=Europe/Warsaw:20250821T120000
DTSTAMP:20260912T143014Z
UID:pretalx-euroscipy-2025-7SLETG@pretalx.com
DESCRIPTION:Version numbers are hard to get right. Maintainers want to com
 municate to users what the impact of adopting a new version will be\, but 
 poor communication can lead to a lot of frustration. There are a few popul
 ar version schemes in use today including Semantic Versioning (SemVer) and
  Calendar Versioning (CalVer). However\, projects in the Python community 
 often don’t strictly conform to these standards which leads to confusion
 . \n\nIn this talk we will discuss Intended Effort Versioning (EffVer)\, a
  new scheme that captures the reality of what many Python projects do toda
 y. This formalisation has been officially adopted by projects including Ju
 pyter Hub\, Matplotlib\, JAX and many more.
LOCATION:Room 1.20 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2025/talk/7SLETG/
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