End-to-End Data Science on GPUs with RAPIDS

John Zedlewski, NVIDIA

Abstract: 

In this talk, we'll discuss the impact of GPUs on the complete workflow supporting data science, and how the open source RAPIDS stack unifies ETL, analytics, and model building all within GPU memory without round trips to CPU. We'll emphasize modern networking technologies, like Dask, UCX, and Infiniband that allow this stack to scale out.

John Zedlewski, NVIDIA

John Zedlewski is the director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.

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BibTeX
@conference {256674,
author = {John Zedlewski},
title = {End-to-End Data Science on GPUs with {RAPIDS}},
year = {2020},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Video 
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