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Course Outline
Introduction
- Overview of RAPIDS features and components for government
- GPU computing concepts
Getting Started
- Installing RAPIDS for government use
- cuDF, cUML, and Dask integration
- Understanding primitives, algorithms, and APIs
Managing and Training Data
- Data preparation and ETL processes for government applications
- Creating a training set using XGBoost for government datasets
- Testing the training model for accuracy and performance
- Working with CuPy arrays for efficient data manipulation
- Utilizing Apache Arrow data frames for optimized data handling
Visualizing and Deploying Models
- Conducting graph analysis with cuGraph for government insights
- Implementing Multi-GPU configurations with Dask for enhanced processing
- Creating an interactive dashboard with cuXfilter for data exploration
- Examples of inference and prediction in government scenarios
Troubleshooting
Summary and Next Steps
Requirements
- Proficiency with CUDA
- Experience in Python programming
Audience
- Data scientists for government
- Developers
14 Hours
