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Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. TaaS must satisfy a wide range of customers who have no experience and/or resources to tune DL hyper-parameters (e.g., mini-batch size and learning rate), and meticulous tuning for each user's dataset is prohibitively expensive. Therefore, TaaS hyper-parameters must be fixed with values that are applicable...
Deep learning with a large number of parametersrequires distributed training, where model accuracy and runtimeare two important factors to be considered. However, there hasbeen no systematic study of the tradeoff between these two factorsduring the model training process. This paper presents Rudra, aparameter server based distributed computing framework tunedfor training large-scale deep neural networks...
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