Pandas DataFrame Tutorial - Beginner's Guide to GPU Accelerated DataFrames in Python

Originally published at: Pandas DataFrame Tutorial - Beginner’s Guide to GPU Accelerated DataFrames in Python | NVIDIA Developer Blog

This post is the first installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process geospatial, signal, and system log data, or use…

Thanks for this post, it’s great. I don’t understand if Rapids is a library that relies on CUDA, why can’t a computer like AGX Xavier run Rapids by just installing the library?
I’ve tried numerous incarnations of attempts by people trying to do just that. The repository GitHub - rapidsai-community/rapids-l4t: Feedstock-like set of scripts for building RAPIDS components for NVIDIA Jetson tried as well, but running the latest JetPack 4.6.xx has no success.
Couldn’t Nvidia allocate some resources to making an l4t compatible Rapids library to support the Xavier AGX and NX?