Cluster packet synchronization optimization method and system for distributed deep neutral network
A technology of deep neural network and group synchronization, which is applied in the field of distributed optimization of deep neural network, can solve the problems of increased number of parameter server requests, twists and turns in parameter update direction, poor model convergence effect, etc., to achieve high resource utilization and increase Affects the effect of weights, reducing the impact of stale gradients
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 In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
 The following first explains and describes the technical terms involved in the present invention:
 Training data: also known as input data, that is, the processing objects that are input to the network model when training the neural network, such as images, audio, text, etc.;
 Model parameters: the weight of the interconnected neurons in the neural network model and the bias bias on ...
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