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
Testimonials (5)
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
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
As I was the only participant the training could be adapted to my needs.