  As deep learning techniques advance more than ever, hyper-parameter
optimization is the new major workload in deep learning clusters. Although
hyper-parameter optimization is crucial in training deep learning models for
high model performance, effectively executing such a computation-heavy workload
still remains a challenge. We observe that numerous trials issued from existing
hyper-parameter optimization algorithms share common hyper-parameter sequence
prefixes, which implies that there are redundant computations from training the
same hyper-parameter sequence multiple times. We propose a stage-based
execution strategy for efficient execution of hyper-parameter optimization
algorithms. Our strategy removes redundancy in the training process by
splitting the hyper-parameter sequences of trials into homogeneous stages, and
generating a tree of stages by merging the common prefixes. Our preliminary
experiment results show that applying stage-based execution to hyper-parameter
optimization algorithms outperforms the original trial-based method, saving
required GPU-hours and end-to-end training time by up to 6.60 times and 4.13
times, respectively.
