Accelerating Your Prototypes with NVIDIA RAPIDS and Friends
, Technical Marketing Engineer, NVIDIA
, Principal Product Architect, NVIDIA
Highly Rated
NVIDIA RAPIDS accelerates Python data science workflows from visualization and discovery to production inference. Its tremendous coverage across a wide range of important algorithms gives data scientists amazing superpowers. However, superheroes sometimes prefer to chart their own courses, prototyping novel techniques in Python and NumPy in order to evaluate them on real data. Unfortunately, combining convenience and acceleration might seem like Kryptonite. Learn how to implement new algorithms without giving up accelerated computing or convenience. We’ll see how to use RAPIDS and ecosystem projects to accelerate novel techniques without leaving the comforts of Python. You’ll leave this hands-on session with a deeper understanding of how accelerated data science works under the hood, a catalog of solutions to challenges you might encounter, and a head start on confidently expanding your accelerated data science superpowers.
Prerequisite(s):
Some data science expertise and proficiency with Python are required; some familiarity with RAPIDS and numerical computation will be useful but is not necessary.
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