https://dasya.itu.dk/tags/scheduling/ Recent content in scheduling on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-scheduling/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-scheduling/ Workload collocation has been shown as an effective method to reduce the hardware requirements for certain deep learning (DL) training tasks. On the other hand, there hasn’t been many robust open-source implementations of schedulers that incorporate workload collocation on GPUs for DL. BLOX is a framework that aims at standardizing the way we implement deep learning schedulers. In this project, our goal is to investigate ways to integrate a collocation-aware scheduler in this framework. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-checkpointing/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-checkpointing/ Deep learning training can run for hours or days, causing long queue times and poor quality of service (QoS) on shared clusters. Since schedulers can’t accurately predict training durations, they often wait for jobs to finish or time out, worsening the issue. Frameworks like TensorFlow and PyTorch support model checkpointing, but frequent checkpoints can slow training, while infrequent ones reduce effectiveness. Automating checkpointing could improve scheduler decisions and fairness. This project aims to (1) benchmark checkpointing overhead in TensorFlow and PyTorch, (2) identify optimal checkpointing frequencies, and (3) develop an automatic checkpointing module.