Progress Estimation for End-to-End Training of Deep Learning Models With Online Data Preprocessing

Abstract

Deep learning is the best machine learning algorithm for numerous analytical tasks. On a large data set, training a deep learning model frequently lasts several days to several months. Throughout this long period, it would be helpful to show a progress indicator, which continually projects the percentage of model training work accomplished as well as the outstanding model training time. We formerly invented the first method to support this function while allowing early stopping. This method assumes that the input data to the model have been preprocessed before model training starts. This is a limitation. In practice, online data preprocessing is often integrated into the model and done as part of the end-to-end model training. Ignoring online data preprocessing costs can cause our former method to produce inaccurate estimates. To overcome this limitation, this paper presents a new progress estimation method that explicitly considers online data preprocessing. We did a coding implementation of our new method in TensorFlow. Our tests unveil that for various deep learning models that integrate online data preprocessing and in comparison with our former method, our proposed new method produces more stable progress estimates for model training and on average lowers the error of the predicted outstanding model training time by 16.0%.

Keywords

• Deep learning
• online data preprocessing
• TensorFlow
• progress indicator
• model training