RAPIDS is NVIDIA’s new Python-based framework for accelerating end-to-end data science and machine learning pipelines on their GPUs. In this webinar, we’ll provide an overview of this new framework and how you can incorporate it in your own research.
In this webinar we will show how to use RAPIDS to accelerate your data science applications utilizing libraries like cuDF (GPU-enabled Pandas-like dataframes) and cuML (GPU-accelerated machine learning algorithms). We will demonstrate how to use Jupyter Notebooks on Comet to accelerate Data Science workflows with RAPIDS using interactive computing methods. In particular, if you use pandas and/or scikit-learn extensively, you’ll definitely want to attend this webinar.
About the Instructor:
Marty Kandes, Ph.D. is a Computational and Data Science Research Specialist in the High-Performance Computing User Services Group at San Diego Super Computer Center. He currently helps manage user support for Comet — SDSC’s largest supercomputer. Marty obtained his Ph.D. in Computational Science in 2015 from the Computational Science Research Center at San Diego State University, where his research focused on studying quantum systems in rotating frames of reference through the use of numerical simulation. He also holds an M.S. in Physics from San Diego State University and B.S. degrees in both Applied Mathematics and Physics from the University of Michigan, Ann Arbor. His current research interests include problems in Bayesian statistics, combinatorial optimization, nonlinear dynamical systems, and numerical partial differential equations.