Led by May Casterline (Senior Solutions Architect, NVIDIA)

May CasterlinePrerequisite(s): Data science experience including intermediate Python competency

The open source RAPIDS project allows data scientists to GPU-accelerate their data science and data analytics applications from beginning to end, creating possibilities for drastic performance gains and techniques not available through traditional CPU-only workflows. Learn how to GPU-accelerate your data science applications by:

  • Utilizing key RAPIDS libraries like cuDF (GPU-enabled Pandas-like dataframes) and cuML (GPU-accelerated machine learning algorithms)
  • Learning techniques and approaches to end-to-end data science, made possible by rapid iteration cycles created by GPU acceleration
  • Understanding key differences between CPU-driven and GPU-driven data science, including API specifics and best practices for refactoring

Upon completion, you'll be able to refactor existing CPU-only data science workloads to run much faster on GPUs and write accelerated data science workflows from scratch.

All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at before you arrive.