Randy Zwitch • 2/1/2019

PyData NYC 2018: End-to-End Data Science Without Leaving the GPU

Read Original

This article revisits a 2018 PyData NYC talk demonstrating a complete GPU-accelerated data science pipeline. It covers the OmniSci database and GPU dataframes (now cudf), explaining core concepts that remain valid. It includes updated setup instructions using conda to install pymapd and cudf, encouraging readers to use the provided Jupyter notebook for hands-on learning.

0 comments
#Data Science #Gpu Computing #Rapids
PyData NYC 2018: End-to-End Data Science Without Leaving the GPU

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
The Beautiful Web
Jens Oliver Meiert • 2 votes
2
When your coding agent doesn’t understand your project, you’ll get junk
Benjamin Cane • 1 votes
3
LLM Use in the Python Source Code
Miguel Grinberg • 1 votes
4
Wagon’s algorithm in Python
John D. Cook • 1 votes
5
An example conversation with Claude Code
Dumm Zeuch • 1 votes