This session, “RAPIDS: The Platform Inside and Outside,” presented by Joshua Patterson, Data Science Director at NVIDIA, for ODSC East 2019, looks at the programming platform RAPIDS.
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Python has seen terrific progress as the data science language of choice. With the introduction of Pandas, users could interact with data in Python in a way that fells intuitive. In addition, open-source packages such as Scikit-Learn have democratized and accelerated data science.
RAPIDS seeks to have a similar impact on the Python data science community by accelerating data science with GPUs.
It’s an open-source suite of tools for GPU data science. Launched in October, RAPIDS includes cuDF, a library for reading data to the GPU and interacting with it in an Pandas’s like way; cuML, a library for machine learning that follows the Scikit-Learn API; and cuGraph, a graph analytics library similar to NetworkX.
The RAPIDS libraries use Dask and Numba to scale to multiple GPUs across nodes and JIT compile User Defined Functions (UDF) respectively to allow users to do large scale data science problems while leveraging Python as a high-performance language running on the GPU. In addition, RAPIDS has interoperability with numerous other libraries and Deep Learning frameworks to simplify end to end data science workflows.
This video will walk through the libraries in RAPIDS, as well as show examples of how simple it is to use in various machine learning and deep learning workflows. In addition, we will show how to install RAPIDS in various ways (container, pip, conda, and from source) on cloud or local environments.
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Finally, we will talk about the evolution of the libraries, new functionality coming soon, and the long term direction of RAPIDS.
Watch the video here.