ART: Actually Robust Training

Some guidelines have been proposed, yet currently, they lack practical implementations. Furthermore, neural network training often takes on the form of trial and error, lacking a structured and thoughtful process. To alleviate these issues, in this paper, we introduce Art, a Python library designed to help automatically impose rules and standards while developing deep learning pipelines. Art divides model development into a series of smaller steps of increasing complexity, each concluded with a validation check improving the interpretability and robustness of the process. The current version of Art comes equipped with nine predefined steps inspired by Andrej Karpathy's Recipe for Training Neural Networks, a visualization dashboard, and integration with loggers such as Neptune.