Course Overview
Learn how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS™, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Prerequisites
Experience with Python, ideally including pandas and NumPy.
Suggested resources to satisfy prerequisites: Kaggle's pandas Tutorials, Kaggle's Intro to Machine Learning, Accelerating Data Science Workflows with RAPIDS
Course Objectives
- 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
Follow On Courses
Outline: Fundamentals of Accelerated Data Science (FADS)
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
GPU-Accelerated Data Manipulation
- Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop:
- Read data directly to single and multiple GPUs with cuDF and Dask cuDF.
- Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.
GPU-Accelerated Machine Learning
- Apply several essential machine learning techniques to the data that was prepared in the first section:
- Use supervised and unsupervised GPU-accelerated algorithms with cuML.
- Train XGBoost models with Dask on multiple GPUs.
- Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK
- Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the entire UK population:
- Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
- Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
Assessment and Q&A