Kursplan
Introduksjon
- Oversikt over RAPIDS funksjoner og komponenter GPU-databehandlingskonsepter
Starter
- Installere RAPIDS cuDF, cUML og Dask Primitives, algoritmer og APIer
Administrering og opplæring av data
- Dataforberedelse og ETL Opprette et treningssett ved hjelp av XGBoost Teste treningsmodellen Arbeide med CuPy array Bruke Apache Arrow datarammer
Visualisere og distribuere modeller
- Grafanalyse med cuGraph Implementering av Multi-GPU med Dask Opprette et interaktivt dashbord med cuXfilter Inferens og prediksjonseksempler
Feilsøking
Sammendrag og neste trinn
Krav
- Kjennskap til CUDA
- Python programmeringserfaring
Publikum
- Dataforskere
- Utviklere
Testimonials (5)
Det faktum å ha mer praktiske øvelser som bruker mer lignende data til det vi bruker i våre prosjekter (satellittbilder 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.