Abstract:As the volume of model data increases, traditional machine learning is not able to train models efficiently, so distributed machine learning is gradually used in large-scale data training. Currently, commonly used distributed machine learning algorithms are based on data parallelism, and often use an overall synchronous parallel strategy when passing data, but using this strategy makes the overall training speed limited by the computation speed of the slower workers in the cluster. While the asynchronous paral… Show more
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