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
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.
Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
Seamless scale from GPU workstations to multi-GPU servers and multi-node clusters.
Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
Drastically improve your productivity with near-interactive data science.
The open-source software is customizable, extensible, interoperable--supported by NVIDIA and built on Apache Arrow.
RAPIDS is for everyone--users, adopters, and contributors. If you’re a data scientist, researcher, engineer, or developer using Pandas, Dask, Scikit-Learn, or Spark on CPUs and looking for 50X end-to-end pipeline speedups at scale, look no further. Downloads RAPIDS and give us a run. RAPIDS is open sourced under the Apache 2.0 open source license and intended to be built upon and hardened in the community. While significant time and effort has been invested into making the platform usable and relevant, we need active contributors to help improve it and build its future.JOIN NOW