  Testing the implementation of deep learning systems and their training
routines is crucial to maintain a reliable code base. Modern software
development employs processes, such as Continuous Integration, in which changes
to the software are frequently integrated and tested. However, testing the
training routines requires running them and fully training a deep learning
model can be resource-intensive, when using the full data set. Using only a
subset of the training data can improve test run time, but can also reduce its
effectiveness. We evaluate different ways for training set reduction and their
ability to mimic the characteristics of model training with the original full
data set. Our results underline the usefulness of training set reduction,
especially in resource-constrained environments.
