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Accelerate Data Science Workflows with Zero Code Changes

Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.

Learning Objectives

By participating in this course, you will:

  • Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks
  • Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes
  • Experience the significant reduction in processing time when workflows are GPU-accelerated

Course Details


  • Basic understanding of data processing and knowledge of a standard data science workflow on tabular data
  • Experience using common Python libraries for data analytics

Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX