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The exponential growth in use of large deep neural networks has acceleratedthe need for training these deep neural networks in hours or even minutes. Thiscan only be achieved through scalable and efficient distributed training, sincea single node/card cannot satisfy the compute, memory, and I/O requirements oftoday's state-of-the-art deep neural networks. However, scaling synchronousStochastic Gradient Descent (SGD) is still a challenging problem and requirescontinued research/development. This entails innovations spanning algorithms,frameworks, communication libraries, and system design. In this paper, wedescribe the philosophy, design, and implementation of Intel Machine LearningScalability Library (MLSL) and present proof-points demonstrating scaling DLtraining on 100s to 1000s of nodes across Cloud and HPC systems.

On Scale-out Deep Learning Training for Cloud and HPC