Accelerating Data Science Workflows with RAPIDS
TimeTuesday, July 301:30pm - 5pm
DescriptionThe 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.