Productive and Efficient Data Science with Python: With Modularizing, Memory profiles, and Parallel/GPU Processing

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022

T. SarkarProductive and Efficient Data Science with Pythonhttps://doi.org/10.1007/978-1-4842-8121-5_11

11. GPU-Based Data Science for High Productivity

Tirthajyoti Sarkar¹  

(1)

Fremont, CA, USA

 

In the last two chapters, you learned about various tools and frameworks for doing out-of-core and distributed/parallelized data science. The central goal has always been the same: enhancing the productivity of the data science pipeline. Productivity is often directly related to the speed of execution of various DS tasks including numerical processing, data wrangling, and feature engineering. When it goes to the advanced machine learning stage, depending on the modeling ...