<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Slurm | Yoonmee Hwang</title><link>https://devotto.github.io/tags/slurm/</link><atom:link href="https://devotto.github.io/tags/slurm/index.xml" rel="self" type="application/rss+xml"/><description>Slurm</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 11 May 2026 00:00:00 +0000</lastBuildDate><image><url>https://devotto.github.io/media/icon_hu_1c0e9cb08cfb822a.png</url><title>Slurm</title><link>https://devotto.github.io/tags/slurm/</link></image><item><title>ML Experimentation Platform for Time-Series Anomaly Detection</title><link>https://devotto.github.io/projects/ml-experimentation/</link><pubDate>Mon, 11 May 2026 00:00:00 +0000</pubDate><guid>https://devotto.github.io/projects/ml-experimentation/</guid><description>&lt;p&gt;Independent study. Reproducible PyTorch experiment framework evaluating 5 model architectures across 200+ time-series datasets on a Slurm HPC cluster. Fail-fast screening cut a 17 GPU-hour benchmark to 1.3 GPU-hours, with 600+ runs tracked in Weights &amp;amp; Biases.&lt;/p&gt;</description></item></channel></rss>