@techreport{oai:ipsj.ixsq.nii.ac.jp:00190710,
 author = {Duo, Zhang and Yusuke, Tanimura and Hidemoto, Nakada and Duo, Zhang and Yusuke, Tanimura and Hidemoto, Nakada},
 issue = {28},
 month = {Jul},
 note = {For modern machine learning systems, including deep learning systems, parallelization is inevitable since they are required to process massive amount of training data. One of the hot topic of this area is the data parallel learning where multiple nodes cooperate each other exchanging parameter / gradient periodically. In order to efficiently implement data-parallel machine learning in a collection of computers with a relatively sparse network, it is indispensable to asynchronously update model parameters through gradients, but the effect of the learning model through asynchronous analysis has not yet been fully understood. In this paper, we propose a software test-bed for analyzing gradient staleness effect on prediction performance, using deep learning framework TensorFlow and distributed computing framework Ray. We report the architecture of the test-bed and initial evaluation results., For modern machine learning systems, including deep learning systems, parallelization is inevitable since they are required to process massive amount of training data. One of the hot topic of this area is the data parallel learning where multiple nodes cooperate each other exchanging parameter / gradient periodically. In order to efficiently implement data-parallel machine learning in a collection of computers with a relatively sparse network, it is indispensable to asynchronously update model parameters through gradients, but the effect of the learning model through asynchronous analysis has not yet been fully understood. In this paper, we propose a software test-bed for analyzing gradient staleness effect on prediction performance, using deep learning framework TensorFlow and distributed computing framework Ray. We report the architecture of the test-bed and initial evaluation results.},
 title = {Asynchronous Deep Learning Test-bed to Analyze Gradient Staleness Effect},
 year = {2018}
}