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Course Outline
Introduction
- Overview of RAPIDS features and components for government use
- Fundamentals of GPU computing concepts for government applications
Getting Started
- Installation process for RAPIDS in a government environment
- Utilization of cuDF, cUML, and Dask for government data processing
- Introduction to primitives, algorithms, and APIs relevant to government operations
Managing and Training Data
- Techniques for data preparation and ETL in a government context
- Development of a training set using XGBoost for government datasets
- Methods for testing the training model to ensure accuracy and reliability for government applications
- Utilization of CuPy arrays in government data processing tasks
- Integration of Apache Arrow data frames for efficient data handling in government systems
Visualizing and Deploying Models
- Conducting graph analysis with cuGraph to support government decision-making
- Implementation of Multi-GPU configurations using Dask for enhanced performance in government operations
- Creation of an interactive dashboard with cuXfilter to facilitate data visualization for government stakeholders
- Examples of inference and prediction models tailored for government use cases
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with CUDA for government applications
- Python programming experience
Audience
- Data scientists
- Developers
14 Hours
