Nvidia Workshop 2021: DLI Accelerated Data Science with RAPIDS

Traditional CPU-driven data science workflows can be cumbersome, but with the power of GPUs, researchers can make sense of data quickly to drive business decisions. Nvidia, Research Computing, and the UF Informatics Institute are offering a Deep Learning Institute (DLI) workshop. Using the RAPIDS accelerated data science libraries, developers will apply a wide variety of GPU-accelerated machine learning algorithms.

June 8, 2021
9:00 am – 4:30 pm
Fee: $10
Register here.

GPU-accelerated machine learning algorithms include XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale. Developers will learn how to build and execute end-to-end GPU accelerated data science workflows that enable them to quickly explore, iterate, and get their work into production.

All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLI-certified instructor, and the opportunity to network with other developers, data scientists, and researchers. Attendees can earn a certificate to prove subject matter competency and support professional growth.

 Learning Objectives:

In this workshop, developers will learn how to:

• Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
• Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
• Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
• Rapidly achieve massive-scale graph analytics using cuGraph routines 
  

Prerequisites

• Experience with Python, ideally including pandas and NumPy.
• To gain experience with pandas, we suggest this pandas course on Kaggle.
• To gain experience with data science using Python, we suggest this machine learning course on Kaggle.
• To get experience accelerating data science workflows, we suggest the Accelerating Data Science Workflows with RAPIDS course with DLI.
  

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

For more info: https://www.nvidia.com/content/dam/en-zz/Solutions/deep-learning/deep-learning-education/DLI-Workshop-Fundamentals-of-Accelerated-Data-Science-with-RAPIDS.pdf

Any questions, please contact Ying Zhang at yingz@ufl.edu.