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Course Outline
Introduction
- 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
Troubleshooting
Summary and Next Steps
Requirements
- Familiarity with CUDA
- Python programming experience
Audience
- Data scientists
- Developers
14 Hours
Testimonials (4)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Transfer of practical knowledge and experience of the trainer.
Rumel Mateusz - Pojazdy Szynowe PESA Bydgoszcz SA
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