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Course Outline
Introduction
- Overview of RAPIDS features and components
- Concepts of GPU computing
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 trained model
- Working with CuPy arrays
- Using Apache Arrow data frames
Visualising and Deploying Models
- Graph analysis with cuGraph
- Implementing Multi-GPU solutions with Dask
- Creating an interactive dashboard with cuXfilter
- Examples of inference and prediction
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with CUDA
- Experience in Python programming
Audience
- Data scientists
- Developers
14 Hours
