Talk: RAPIDS - Open GPU Data Science
Last week I was fortunate to speak at the PyData Cardiff meetup.
I presented an overview of RAPIDS, a suite of open source software libraries which give you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. Much can change between releases of RAPIDS so the latest overview of libraries, benchmarks, and updates is consolidated in a release deck. The deck I presented at PyData Cardiff was a variation of the 0.11 deck which I remixed to appeal to the audience of the meetup, which was mainly data scientists, individual researchers and students.
The RAPIDS suite of open source software libraries (https://rapids.ai/) allow you to run data science and analytics pipelines entirely on GPUs, but following familiar Python APIs including Numpy, Pandas and SciKit Learn.
RAPIDS relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.
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