  One of the most significant bottleneck in training large scale machine
learning models on parameter server (PS) is the communication overhead, because
it needs to frequently exchange the model gradients between the workers and
servers during the training iterations. Gradient quantization has been proposed
as an effective approach to reducing the communication volume. One key issue in
gradient quantization is setting the number of bits for quantizing the
gradients. Small number of bits can significantly reduce the communication
overhead while hurts the gradient accuracies, and vise versa. An ideal
quantization method would dynamically balance the communication overhead and
model accuracy, through adjusting the number bits according to the knowledge
learned from the immediate past training iterations. Existing methods, however,
quantize the gradients either with fixed number of bits, or with predefined
heuristic rules. In this paper we propose a novel adaptive quantization method
within the framework of reinforcement learning. The method, referred to as
MQGrad, formalizes the selection of quantization bits as actions in a Markov
decision process (MDP) where the MDP states records the information collected
from the past optimization iterations (e.g., the sequence of the loss function
values). During the training iterations of a machine learning algorithm, MQGrad
continuously updates the MDP state according to the changes of the loss
function. Based on the information, MDP learns to select the optimal actions
(number of bits) to quantize the gradients. Experimental results based on a
benchmark dataset showed that MQGrad can accelerate the learning of a large
scale deep neural network while keeping its prediction accuracies.

Machine Learning;Machine Learning