Kursplan
Introduktion
- Översikt över RAPIDS funktioner och komponenter
- GPU datorkoncept
Komma igång
- Installerar RAPIDS
- cuDF, cUML och Dask
- Primitiver, algoritmer och API:er
Hantering och utbildning av data
- Databeredning och ETL
- Skapa ett träningsset med XGBoost
- Testar träningsmodellen
- Arbeta med CuPy array
- Använder Apache Arrow dataramar
Visualisera och distribuera modeller
- Grafanalys med cuGraph
- Implementering av Multi-GPU med Dask
- Skapa en interaktiv instrumentpanel med cuXfilter
- Exempel på slutledningar och förutsägelser
Felsökning
Sammanfattning och nästa steg
Krav
- Kännedom om CUDA
- Python programmeringserfarenhet
Publik
- Dataforskare
- Utvecklare
Vittnesmål (5)
Det faktum att vi har mer praktiska övningar med mer liknande data som vi använder i våra projekt (satellitbilder i rasterformat)
Matthieu - CS Group
Kurs - Scaling Data Analysis with Python and Dask
Machine Translated
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.
Kurs - 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
Kurs - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Kurs - Data Analysis in Python using Pandas and Numpy
As I was the only participant the training could be adapted to my needs.