When engineers train deep learning models, they are very much “flying blind”. Commonly used approaches for realtime 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 hyper parameters.

2021: Frank Schneider, Felix Dangel, Philipp Hennig

https://arxiv.org/pdf/2102.06604v2.pdf