Title: Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train,
Abstract: For the past 5 years, the ILSVRC competition and the ImageNet dataset have
attracted a lot of interest from the Computer Vision community, allowing for
state-of-the-art accuracy to grow tremendously. This should be credited to the
use of deep artificial neural network designs. As these became more complex,
the storage, bandwidth, and compute requirements increased. This means that
with a non-distributed approach, even when using the most high-density server
available, the training process may take weeks, making it prohibitive.
Furthermore, as datasets grow, the representation learning potential of deep
networks grows as well by using more complex models. This synchronicity
triggers a sharp increase in the computational requirements and motivates us to
explore the scaling behaviour on petaflop scale supercomputers. In this paper
we will describe the challenges and novel solutions needed in order to train
ResNet-50 in this large scale environment. We demonstrate above 90\% scaling
efficiency and a training time of 28 minutes using up to 104K x86 cores. This
is supported by software tools from Intel's ecosystem. Moreover, we show that
with regular 90 - 120 epoch train runs we can achieve a top-1 accuracy as high
as 77\% for the unmodified ResNet-50 topology. We also introduce the novel
Collapsed Ensemble (CE) technique that allows us to obtain a 77.5\% top-1
accuracy, similar to that of a ResNet-152, while training a unmodified
ResNet-50 topology for the same fixed training budget. All ResNet-50 models as
well as the scripts needed to replicate them will be posted shortly.