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With machine learning models especially Deep Neural Network (DNN) models becoming an integral part of the new intelligent software, new tools to support their engineering process are in high demand. Existing DNN debugging tools are either post-training which wastes a lot of time training a buggy model and requires expertises, or limited on collecting training logs without analyzing the problem not even fixing them. In this paper, we propose Au t o Tr a in e r , a DNN training monitoring and automatic repairing tool which supports detecting and autorepairing five commonly seen training problems. During training, it periodically checks the training status and detects potential problems. Once a problem is found, Au t o Tr a in e r tries to fix it by using built-in state-of-the-art solutions. It supports various model structures and input data types, such as Convolutional Neural Networks (CNNs) for image and Recurrent Neural Networks (RNNs) for texts. Our evaluation on 6 datasets, 495 models show that Au t o Tr a in e r can effectively detect all potential problems with 100% detection rate and no false positives. Among all models with problems, it can fix 97.33% of them, increasing the accuracy by 47.08% on average. Index Terms-software engineering, software tools, deep learning training I. In t r o d u c t io n

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