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RAPIDS: Open GPU Data Science

07/09/2019
8:00-12:00
Anthony Scopatz, Quansight, LLC
Nick Becker, NVIDIA
0d6f1e55-5853-4491-acd5-ce329efb843e
Dante Gama Dessavre, NVIDIA
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).

Prerequisites:

NumPy, SciKit Learn, Pandas