The Augmentor software package is a versatile tool for data augmentation. It provides a stochastic, pipeline-based approach to data augmentation with a number of features that are relevant to various fields, e.g. biomedical imaging, such as z-stack augmentation and randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. There are two versions available, one in Python and one in Julia. The outstanding features of Augmentor include size-preserving rotations, size-preserving shearing, and cropping, which is more suitable for deep learning than traditional methodologies. Due to the success of the Augmentor within the international community we will expand in 2020 and offer some opportunities of new students.