Distributed model training method and device, storage medium and computer equipment

Abstract

The embodiment of the invention discloses a distributed model training method and device, a storage medium and computer equipment. The invention is related to the field of machine learning of artificial intelligence; the method comprises the steps of obtaining a model structure and sample data of a target model; sending a model message to each graphics processor, and triggering each graphics processor to train a target model; based on the preset sample number of the sample labels and the number of the graphics processors, performing multiple times of data sampling on the sample data to obtain multiple groups of training sample data; sending the training sample data to a corresponding graphics processor, triggering the graphics processor to train a target model based on the training sample data, and updating training parameters of the target model among the graphics processors to obtain updated training parameters of the trained target model; and when the updated training parameters meet a preset training termination condition, outputting the trained target model. The speed of model training can be effectively improved.

Description

technical field [0001] The present application relates to the field of artificial intelligence, in particular to a distributed model training method, device, storage medium and computer equipment. Background technique [0002] With the rapid development of artificial intelligence science, more and more problems can be solved by neural network models in daily life. Before applying neural network models to solve problems, training neural network models is a key step. In the prior art , model training usually requires a large amount of sample data. Before training, the sample data is transmitted to the training machine, and distributed data sampling is performed through the training machine, and the sampled training sample data is input into the neural network model for training, so that the neural network model The network parameters are updated. [0003] During the research and practice of the prior art, the inventors of the present application found that the sample data com...

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Problems solved by technology

[0003] During the research and practice of the prior art, the inventors of the present application found that the sample data comes in various forms. When the sample data is an image, the training sample data obtained through the prior art cannot match the demand data for neural network model training. The matching degree is not high, which makes the whole model training process take a long time

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