  Increasing the batch size of a deep learning model is a challenging task.
Although it might help in utilizing full available system memory during
training phase of a model, it results in significant loss of test accuracy most
often. LARS solved this issue by introducing an adaptive learning rate for each
layer of a deep learning model. However, there are doubts on how popular
distributed machine learning systems such as SystemML or MLlib will perform
with this optimizer. In this work, we apply LARS optimizer to a deep learning
model implemented using SystemML.We perform experiments with various batch
sizes and compare the performance of LARS optimizer with \textit{Stochastic
Gradient Descent}. Our experimental results show that LARS optimizer performs
significantly better than Stochastic Gradient Descent for large batch sizes
even with the distributed machine learning framework, SystemML.
