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Distributed deep learning is becoming increasingly popular due to theexpanding demand for computing resources for deep learning models with a largeramount of parameters. Different from traditional training approaches,data-parallel training allows multiple compute nodes to train large deeplearning models simultaneously in order to boost the training efficiency. Inthis paper, we present and compare six strategies for data-parallel trainingusing PyTorch on the language model GPT-2 with 100M parameters using aqualitative approach. These strategies are Single GPU, Single Parameter Server,Distributed Parameter Server, Horovod, Distributed Parameter Server with Apexmixed-precision strategy, and Horovod with Apex mixed-precision strategy. Wealso analyze the quantitative experiment results from each strategy. In theend, we draw the conclusion that the Distributed Parameter Server with Apexmixedprecision strategy has the best performance on single node training, whileHorovod with Apex is the most robust approach to use when we have single ormultiple nodes.

Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models