RAPIDS: Open GPU Data Science
The RAPIDS suite of open source 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.
Features of RAPIDS
Hassle Free Integration
Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
Scaling Out on a 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.
Reduced Training Time
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.
The New GPU Data Science Pipeline
This is a columnar, in-memory data structure that delivers efficient and fast data interchange with flexibility to support complex data models.
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.
This is a framework and collection of graph analytics libraries that seamlessly integrate into the RAPIDS data science platform.
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.
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.
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.
End-to-End Faster Speeds on RAPIDS
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. Download 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.
Exxact Deep Learning GPU Solutions
Our deep learning GPU solutions are powered by the leading hardware, software, and systems engineering. Each system comes with our pre-installed deep learning software stack and are fully turnkey to run right out of the box.