Course Outline


  • Overview of RAPIDS features and components
  • GPU computing concepts

Getting Started

  • Installing RAPIDS
  • cuDF, cUML, and Dask
  • Primitives, algorithms, and APIs

Managing and Training Data

  • Data preparation and ETL
  • Creating a training set using XGBoost
  • Testing the training model
  • Working with CuPy array
  • Using Apache Arrow data frames

Visualizing and Deploying Models

  • Graph analysis with cuGraph
  • Implementing Multi-GPU with Dask
  • Creating an interactive dashboard with cuXfilter
  • Inference and prediction examples


Summary and Next Steps


  • Familiarity with CUDA
  • Python programming experience


  • Data scientists
  • Developers
 14 Hours

Testimonials (5)

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