RAPIDS AI

Introducing node-RAPIDS

New Features Added to RAPIDS cuXfilter GPU-Accelerated Cross Filtering Library

Together, Dask and RAPIDS allow you to scale up and scale out production Python workloads at the same time.

RAPIDS Versioning for Continuous Improvement

Learn how we’re fuzz testing IO readers and writers with randomly generated data and parameters to catch bugs and improve reliability

RAPIDS 0.19 Released at GTC Spring 2021

RAPIDS cuDF release 0.19 introduces improved support for nested types such as lists and structs.

Use Prometheus and Grafana to instrument your RAPIDS deployment and monitor how your workflows are performing on your system.

Checkout the latest features and updates.

A tutorial to run RAPIDS and your favorite Linux software, including NVIDIA CUDA, on Windows.

See why you should push the filtering operation down to the read calls whenever possible.

This release is focused on improvements in three main areas: performance, functionality, and user experience.

Simply sprinkling RAPIDS cuDF scalars into your workflow can result in nontrivial speedup without any additional code changes.

Scale RAPIDS from a local machine to a multi-GPU cluster on GCP with Dask. Explore w/ affordable cloud resources and scale when ready.

TL;DR — With the release of XGBoost 1.3 comes an exciting new feature for model interpretability — GPU accelerated SHAP values. SHAP…

Covers setup of remote repositories on Artifactory 7.x and RAPIDS conda installation

Get an 11x speed-up by switching your streaming inference pipeline to RAPIDS cuStreamz and FIL.

All the components required to train, record, and register GPU accelerated machine learning models on GKE

Checkout the latest features and integrations in the RAPIDS ecosystem

A highly-efficient multi-GPU backend for scaling Recommender Pipelines

A distributed multi-node, multi-GPU implementation now available

New Accelerated Force Atlas 2 API processes and visualizes graphs with millions of vertices and edges in seconds

Perform every step of complex ML pipelines on the GPU, resulting in a significant speedup for feature engineering tasks

Achieve double the streaming throughput, process 2x the data in the same time, using less than half number of processes/Dask workers.