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2019 GTC San Jose
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DLIT937 - Accelerating Data Science Workflows with RAPIDS

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Session Description

Prerequisites: Data science experience including intermediate Python competency

How To Prepare: 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 http://courses.nvidia.com/join before you arrive.

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.


 


Additional Information
Autonomous Machines, IoT, Robotics & Drones
HPC and AI, Supercomputing
General
Instructor-Led Training
2 Hours
Session Schedule
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