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RAPIDS: Open GPU Data Science | Scipy 2019 Tutorial | Scopatz, Becker, Kraus, Gama Dessavre

Nick Becker
Data Science Products at NVIDIA
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SciPy 2019
July 9 2019, Texas, United States
SciPy 2019
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RAPIDS: Open GPU Data Science | Scipy 2019 Tutorial | Scopatz, Becker, Kraus, Gama Dessavre
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About speakers

I'm a data scientist with experience conducting research, leading technical teams, and building products. Currently, I'm focused on applications of deep learning, machine learning, and accelerated computing at NVIDIA.

Over the past few years, I've participated in and led projects with DataKind, a data-science non-profit. DataKind brings together data scientists and engineers with leading social change organizations to collaborate on projects to maximize social impact.

Please feel free to visit my personal website (https://beckernick.github.io/) for more information or send me an email at nickb500@gmail.com if you want to chat.
Anthony Scopatz is a computational scientist and long time Python developer, Anthony holds his BS in Physics from UC Santa Barbara and Ph.D. in Mechanical / Nuclear Engineering from UT Austin. A former Enthought employee, he spent his post-doctoral studies at the FLASH Center at the University of Chicago in the Astrophysics Department. He then became a Staff Scientist at the University of Wisconsin-Madison in Engineering Physics. He is currently an Assistant Professor of Nuclear Engineering in the Mechanical Engineering at the University of South Carolina. Anthony’s research interests revolve around essential physics modeling of the nuclear fuel cycle, and information theory & entropy. Anthony has published and spoken at numerous conferences on a variety of science & software development topics.

About the talk

The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science pipelines entirely on GPUs. RAPIDS is incubated by NVIDIA® based on years of accelerated data science experience. RAPIDS relies on NVIDIA CUDA® primitives for low-level compute optimization, GPU parallelism, and high-bandwidth memory speed through user-friendly Python interfaces. This tutorial will teach you how to use the RAPIDS software stack from Python, including cuDF (a DataFrame library interoperable with Pandas), dask-cudf (for distributing DataFrame work over many GPUs), and cuML (a machine learning library that provides GPU-accelerated versions of the algorithms in scikit-learn).

See tutorial materials here: https://www.scipy2019.scipy.org/tutorial-participant-instructions

See the full SciPy 2019 playlist at https://www.youtube.com/playlist?list=PLYx7XA2nY5GcDQblpQ_M1V3PQPoLWiDAC

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