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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