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Training deep neural networks consumes increasing computational resourceshares in many compute centers. Often, a brute force approach to obtainhyperparameter values is employed. Our goal is (1) to enhance this by enablingsecond-order optimization methods with fewer hyperparameters for large-scaleneural networks and (2) to perform a survey of the performance optimizers forspecific tasks to suggest users the best one for their problem. We introduce anovel second-order optimization method that requires the effect of the Hessianon a vector only and avoids the huge cost of explicitly setting up the Hessianfor large-scale networks.  We compare the proposed second-order method with two state-of-the-artoptimizers on five representative neural network problems, including regressionand very deep networks from computer vision or variational autoencoders. Forthe largest setup, we efficiently parallelized the optimizers with Horovod andapplied it to a 8 GPU NVIDIA P100 (DGX-1) machine.

Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs