  The parameter server architecture is prevalently used for distributed deep
learning. Each worker machine in a parameter server system trains the complete
model, which leads to a hefty amount of network data transfer between workers
and servers. We empirically observe that the data transfer has a non-negligible
impact on training time.
  To tackle the problem, we design a new distributed training system called
Stanza. Stanza exploits the fact that in many models such as convolution neural
networks, most data exchange is attributed to the fully connected layers, while
most computation is carried out in convolutional layers. Thus, we propose layer
separation in distributed training: the majority of the nodes just train the
convolutional layers, and the rest train the fully connected layers only.
Gradients and parameters of the fully connected layers no longer need to be
exchanged across the cluster, thereby substantially reducing the data transfer
volume. We implement Stanza on PyTorch and evaluate its performance on Azure
and EC2. Results show that Stanza accelerates training significantly over
current parameter server systems: on EC2 instances with Tesla V100 GPU and 10Gb
bandwidth for example, Stanza is 1.34x--13.9x faster for common deep learning
models.
