deep learning training

Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to …

We present UniSched, a unified scheduler to optimize different types of scheduling objectives (e.g., guaranteeing the deadlines of SLO jobs, minimizing the latency of best-effort jobs).

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and …

Hyperparameter tuning is an essential step in deep learning model development that provides better model performance at the cost of …

With the proliferation of deep learning, there exists a strong need to efficiently operate GPU clusters for deep learning production in …

We design a new and practical GPU scheduler, ASTRAEA, to enforce the desired fairness among tenants and jobs for deep learning training clusters.

We present Chronus, an end-to-end scheduling system to provide deadline guarantee for SLO jobs and maximize the performance of best-effort jobs for deep learning training jobs.