An end-to-end data science ecosystem, open source RAPIDS gives you Python dataframes, graphs, and machine learning on Nvidia GPU hardware Building machine learning models is a repetitive process.

rapids-build-backend is a wrapper around standard backends like scikit-build-core and setuptools that handles some of the standard issues that we face for RAPIDS packages (CUDA versioning, alpha ...

In this video from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing. We will introduce Numba and RAPIDS for GPU programming in Python.

This PySpark-compatible API leverages the RAPIDS cuML python API to provide GPU-accelerated implementations of many common ML algorithms. These implementations are adapted to use PySpark for ...

Nvidia has expanded its support of NetworkX graph analytic algorithms in RAPIDS, its open source library for accelerated computing. The expansion means data scientists can run 40-plus NetworkX ...