Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks

Title: Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks
Authors: Frank Schneider, Felix Dangel, Philipp Hennig
Published: 12th February 2021 (Friday) @ 16:28:49
Link: http://arxiv.org/abs/2102.06604v2

Abstract

When engineers train deep learning models, they are very much ‘flying blind’. Commonly used methods for real-time training diagnostics, such as monitoring the train/test loss, are limited. Assessing a network’s training process solely through these performance indicators is akin to debugging software without access to internal states through a debugger. To address this, we present Cockpit, a collection of instruments that enable a closer look into the inner workings of a learning machine, and a more informative and meaningful status report for practitioners. It facilitates the identification of learning phases and failure modes, like ill-chosen hyperparameters. These instruments leverage novel higher-order information about the gradient distribution and curvature, which has only recently become efficiently accessible. We believe that such a debugging tool, which we open-source for PyTorch, is a valuable help in troubleshooting the training process. By revealing new insights, it also more generally contributes to explainability and interpretability of deep nets.

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