Welcome to the RAPIDS ID Card Office Online. Select an option below to update your CAC, manage sponsor or family member ID card information, or find a RAPIDS ID Card Office.

Purpose: ID Card Office Online is a self-service application that allows Service Members, DoD Civilians, Contractors, and Retirees to accomplish personnel functions online to minimize in-person at a RAPIDS ID Card Office.

The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA CUDA primitives and high-bandwidth GPU memory under the hood.

RAPIDS, which stands for Real-time Automated Personnel Identification System, is a program within the Department of Defense that is managed by the Defense Manpower Data Center (DMDC).

RAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries. Built on state-of-the-art foundations like NVIDIA CUDA and Apache Arrow, it unlocks the speed of GPUs with code you already know.

Rapids are sections of a river where the river bed has a relatively steep gradient, causing an increase in water velocity and turbulence. Flow, gradient, constriction, and obstacles are four factors that are needed for a rapid to be created.

After installing the RAPIDS libraries, the best place to get started is our User Guide. Our RAPIDS.ai home page also provides a great deal of information, as does our Blog Page and the NVIDIA Developer Blog.

The Real-Time Automated Personnel Identification System (RAPIDS) is a United States Department of Defense (DoD) system used to issue the definitive credential within DoD.

This site serves to unify the documentation for RAPIDS. Whether you’re new to RAPIDS, looking to contribute, or are a part of the RAPIDS team, the docs here will help guide you.

RAPIDS supports end-to-end data science workflows, from data loading and preprocessing to machine learning, graph analytics, and visualization. It’s a fully functional Python stack that scales to enterprise big-data use cases.