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RAPIDS: Open Source GPU Data Science


See how RAPIDS and the open source ecosystem are advancing data science. In this session, we will explore RAPIDS, the open source data science platform from NVIDIA. Come learn how to get started leveraging these open-source libraries for faster performance and easier development on GPUs. See the latest engineering work and new release features, including benchmarks and software development roadmap

The RAPIDS suite of open source software libraries gives the data scientist the freedom to execute end-to-end data science and analytics pipelines on GPUs. RAPIDS is incubatedby NVIDIA based on years of accelerated analytics experience. RAPIDS relies on NVIDIA CUDA primitives for low-level compute optimization and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. Through a familiar DataFrame API that integrates with a variety of machine learning algorithms, RAPIDS facilitates common data preparations tasks while removing typical serialization costs. RAPIDS includes support for multi-GPU deployments, enabling vastly accelerated processing and training on large dataset sizes.

Join NVIDIA’s engineers as they walk through a collection of data science problems that introduce components and features of RAPIDS, including: feature engineering, data manipulation, statistical tasks, machine learning, and graph analysis.


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