The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It 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 serialisation costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

Features

• Hassle-Free Integration
  Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
• Top Model Accuracy
  Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
• Reduced Training Time
  Drastically improve your productivity with near-interactive data science.
• Open Source
  Customisable, extensible, interoperable – the open-source software is supported by NVIDIA and built on Apache Arrow.

For more information on RAPIDS please contact XENON, visit our RAPIDS Workshop page or https://rapids.ai/.

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