Fundamentals of Accelerated Data Science (FADS) – Outline

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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