NVIDIA RAPIDS

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 serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

End-to-End Faster Speeds on RAPIDS

The New GPU Data Science Pipeline


	Apache Arrow This is a columnar, in-memory data structure that delivers efficient and fast data interchange with flexibility to support complex data models.	cuGRAPH This is a framework and collection of graph analytics libraries that seamlessly integrate into the RAPIDS data science platform.
	cuDF The RAPIDS cuDF library is a DataFrame manipulation library based on Apache Arrow that accelerates loading, filtering, and manipulation of data for model training data preparation. The Python bindings of the core-accelerated CUDA DataFrame manipulation primitives mirror the pandas interface for seamless onboarding of pandas users.	Deep Learning Libraries RAPIDS provides native array_interface support. This means data stored in Apache Arrow can be seamlessly pushed to deep learning frameworks that accept array_interface such as PyTorch and Chainer.
	cuML RAPIDS cuML is a collection of GPU-accelerated machine learning libraries that will provide GPU versions of all machine learning algorithms available in scikit-learn.	Visualization Libraries Coming Soon RAPIDS will include tightly integrated data visualization libraries based on Apache Arrow. Native GPU in-memory data format provides high-performance, high-FPS data visualization, even with very large datasets.

Features of RAPIDS

Hassle-Free Intefgration
Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

Scaling Out on Any GPU
Seamless scale from GPU workstations to multi-GPU servers and multi-node clusters.

Top Model Accuracy
Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.