RAPIDS AI

Expanded accelerated vector search and zero-code change data science, Dask query planning, and support for Python 3.11 and pandas 2

Zero code-change NetworkX, RAPIDS on Databricks with Dask, and more!

RAPIDS Goes CPU/GPU (zero code change!), XGBoost 2.0, Improved Vector Search, and more

This is the second chapter in our blog series on GPU-accelerated graph analysis using RAPIDS cuGraph. In the first chapter, we introduced…

Using cuGraph-DGL to accelerate GNNs with GPUs

GPU accelerated large-scale text processing, vector search, spatial analytics and much more!

CUDA 12 support for pip wheels, cuSpatial DE-9IM support, performance improvements across the board, timezone support, and more!

GeoSeries, Intersection, Spatial Predicates, and more!

Accelerate GNN training with the power of cuGraph and PyG

RAPIDS 23.04 is out! We are excited about this release, which includes major updates to cuSpatial, continued improvements to graph neural…

First Release of the Year

Authors (alphabetical): Nick Becker (NVIDIA), Dante Gama Dessavre (NVIDIA), and Corey Nolet (NVIDIA)

Authors: Rick Zamora and Ben Zaitlen

Authors (alphabetical): Nick Becker (NVIDIA), Dante Gama Dessavre (NVIDIA), Maarten Grootendorst (BERTopic), and Corey Nolet (NVIDIA)

GPU-accelerated machine learning is producing fascinating results across a wide range of fields on a seemingly daily basis. While neural…

Authors: Thomson Comer, Michael Wang and Mark Harris

Making sure your holiday season is full of presents.

By: Vibhu Jawa and Randy Gelhausen

by Michael Wang, Thomson Comer, Mark Harris, Ben Jarmak

Ever wish you could apply your own custom functions to string data columns in cuDF? Well, now you can! Recently cuDF introduced support for…

Serving cake, pip, and Windows at our fourth birthday party!

Numba is the Just-In-Time compiler used in RAPIDS cuDF to implement high-performance User-Defined Functions (UDFs) by turning user-supplied…

We’re excited to announce General Availability of Dask-SQL on GPUs! This matters to you because it means more interoperability between data…