What Is NVIDIA RAPIDS?

The RAPIDS suite of 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 GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

NVIDIA RAPIDS includes XGBoost, cuDF for Data Frames, cuML for Machine Learning, cuGraph for Graph processing, RMM for memory management, and spatiotemporal operations.

What Is NVIDIA RAPIDS Production Branch May 2024?

The NVIDIA RAPIDS Production Branch, exclusively available with NVIDIA AI Enterprise, is a 9-month supported, API-stable branch that includes monthly fixes for high and critical software vulnerabilities. This branch provides a stable and secure environment for building your mission-critical AI applications. The NVIDIA RAPIDS production branch releases every six months with a three-month overlap in between two releases.

Getting started with NVIDIA RAPIDS Production Branch

Before you start, ensure that your environment is set up by following one of the deployment guides available in the NVIDIA AI Enterprise Documentation.

For more information on RAPIDS, visit the following links:

• RAPIDS.ai website

• RAPIDS User Guides

• RAPIDS Documentation

Compatible Infrastructure Software Versions

For the optimized performance, it is highly recommended to deploy the supported NVIDIA AI Enterprise Infrastructure software in conjunction with your AI software.

Production Branch - May 2024 (24h1) is compatible with NVIDIA AI Enterprise Infrastructure 5.

Known Issues

There is a bug in RAPIDS whereby attempting to serialize any cudf dataframe whose column names are numpy integers will result in a TypeError similar to TypeError: can not serialize 'numpy.int64' object. A fix will be provided in the next Production Branch October 2024 (PB24h2) release. As a workaround, users should rewrite the dataframe column names by getting the underlying int/float value from the numpy type and reassigning that value as the column name.

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