  In recent years, with the popularization of deep learning frameworks and
large datasets, researchers have started parallelizing their models in order to
train faster. This is crucially important, because they typically explore many
hyperparameters in order to find the best ones for their applications. This
process is time consuming and, consequently, speeding up training improves
productivity. One approach to parallelize deep learning models followed by many
researchers is based on weak scaling. The minibatches increase in size as new
GPUs are added to the system. In addition, new learning rates schedules have
been proposed to fix optimization issues that occur with large minibatch sizes.
In this paper, however, we show that the recommendations provided by recent
work do not apply to models that lack large datasets. In fact, we argument in
favor of using strong scaling for achieving reliable performance in such cases.
We evaluated our approach with up to 32 GPUs and show that weak scaling not
only does not have the same accuracy as the sequential model, it also fails to
converge most of time. Meanwhile, strong scaling has good scalability while
having exactly the same accuracy of a sequential implementation.
