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Nvidia workshop: Accelerating Data Science Workflows with RAPIDS
September 24 @ 9:00 am - 11:30 am
(This event will be limited to 40 registrants)
Please visit Nvidia’s website to create a free account ahead of the workshop; you will need this to access course materials. The course code will be provided the day of.
Learn to build a GPU-accelerated, end-to-end data science workflow using RAPIDS open-source libraries for massive performance gains. RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. We will discuss how to ingest and prepare datasets using cuDF (GPU-accelerated dataframes) and train machine learning models using cuML (GPU-accelerated machine learning).
Learning Objectives
RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. Learn to GPU-accelerate end-to-end data science workflows by:
- Using cuDF to injest and manipulate massive datasets directly on the GPU
- Utilizing a wide variety of GPU-accelerated machine learning algorithms including XGBoost and several other cuML algorithms to perform data analysis
- Performing end to end analysis tasks utilizing several realistic data sets
Upon completion, you’ll be able to perform a wide variety of end-to-end data science tasks using multiple datasets.
Course Details
Prerequisites: Experience with Python, ideally including Pandas and NumPy.
Technologies: RAPIDS, cuDF, cuML, XGBoost, CuPy
