The RAPIDS suite of open source software libraries (https://rapids.ai/) allow you to run data science and analytics pipelines entirely on GPUs, but following familiar Python APIs including Numpy, Pandas and SciKit Learn.
RAPIDS 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.