  Hyper-parameter tuning (HPT) is crucial for many machine learning (ML)
algorithms. But due to the large searching space, HPT is usually time-consuming
and resource-intensive. Nowadays, many researchers use public cloud resources
to train machine learning models, convenient yet expensive. How to speed up the
HPT process while at the same time reduce cost is very important for cloud ML
users. In this paper, we propose SpotTune, an approach that exploits transient
revocable resources in the public cloud with some tailored strategies to do HPT
in a parallel and cost-efficient manner. Orchestrating the HPT process upon
transient servers, SpotTune uses two main techniques, fine-grained cost-aware
resource provisioning, and ML training trend predicting, to reduce the monetary
cost and runtime of HPT processes. Our evaluations show that SpotTune can
reduce the cost by up to 90% and achieve a 16.61x performance-cost rate
improvement.
