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cs.DC2025
Deadline-Aware Online Scheduling for LLM Fine-Tuning with Spot Market Predictions
Linggao Kong, Yuedong Xu, Lei Jiao +1
As foundation models grow in size, fine-tuning them becomes increasingly expensive. While GPU spot instances offer a low-cost alternative to on-demand resources, their volatile pri…
cs.DC2020
Dynamic backup workers for parallel machine learning
Chuan Xu, Giovanni Neglia, Nicola Sebastianelli
The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of workers, which iteratively…