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コース概要
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
要求
- Familiarity with CUDA
- Python programming experience
Audience
- Data scientists
- Developers
14 時間
お客様の声 (5)
私たちの分野に完璧に適合した例/演習
Luc - CS Group
コース - Scaling Data Analysis with Python and Dask
Machine Translated
トレーナーはどんな質問にも答えてくれた。
Caterina - Stamtech
コース - Developing APIs with Python and FastAPI
Machine Translated
トレーナーの実践的な知識と経験の移転。
Rumel Mateusz - Pojazdy Szynowe PESA Bydgoszcz SA
Machine Translated
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
コース - Build REST APIs with Python and Flask
As I was the only participant the training could be adapted to my needs.
