<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DRA on Jerry Yang's Blog</title><link>https://blog.yangjerry.tw/en/tags/dra/</link><description>Recent content in DRA on Jerry Yang's Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>youremail@example.com (ChengHao Yang)</managingEditor><webMaster>youremail@example.com (ChengHao Yang)</webMaster><copyright>© 2018–2025 ChengHao Yang. Unless otherwise stated, all articles are licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)</copyright><lastBuildDate>Sun, 10 May 2026 12:56:48 +0800</lastBuildDate><atom:link href="https://blog.yangjerry.tw/en/tags/dra/atom.xml" rel="self" type="application/rss+xml"/><item><title>A First Look at Kubernetes DRA — Using NVIDIA DRA Driver GPU as an Example</title><link>https://blog.yangjerry.tw/en/posts/dra-nvidia-gpu-overview/</link><pubDate>Sun, 10 May 2026 12:56:48 +0800</pubDate><author>youremail@example.com (ChengHao Yang)</author><guid>https://blog.yangjerry.tw/en/posts/dra-nvidia-gpu-overview/</guid><description>Dynamic Resource Allocation (DRA) recently reached GA in Kubernetes v1.35, and I believe many of us are eager to give it a try. Adding to the momentum, NVIDIA has moved dra-driver-nvidia-gpu into Kubernetes SIGs, with the documentation dropping the Beta label — a sign that the technology and its standards are gradually maturing.
For this post, I borrowed all the NVIDIA GPUs currently available at CNTUG Infra Labs to learn how to elegantly allocate devices and resources with DRA.</description></item></channel></rss>