A method for updating a convolutional neural network by using a GPU cluster, the GPU cluster including a first GPU and several other GPUs, wherein the convolutional neural network comprises at least one convolutional layer, CONV, a batch normalization layer and another layer in a front-end network and at least one fully connected layer, FC, in a back-end network, the first GPU having parameters of each layer of front-end network of the convolutional neural network, wherein parameters of each layer of the back-end network of the convolutional neural network are distributed among the first GPU and the other GPUs, and the method being performed by the first GPU and comprising: obtaining a first sample with a classification label; performing a first operation corresponding to each layer of front-end network on the first sample based on the parameters of each layer of front-end network, to obtain a first operation result of the first sample, which includes: performing a convolution operation on the first sample based on parameters of the convolutional layer, to obtain a first intermediate result; obtaining second intermediate results that are acquired by the other GPUs performing a convolution operation on other samples obtained by the other GPUs based on parameters of the convolutional layer that the other GPUs have; performing normalization processing on the first intermediate result based on the second intermediate results of the other GPUs; and performing another operation on the first sample based on parameters of the another layer and the first intermediate result obtained after normalization processing, to obtain the first operation result of the first sample; performing a corresponding second operation on the first sample based on the first operation result and the parameters of each layer of back-end network that the first GPU has, to obtain a second operation result; separately sending the first operation result to the other GPUs, so that each other GPU performs a corresponding third operation on the first sample based on their parameters of each layer of back-end network and the first operation result; receiving a third operation result obtained after each other GPU performs the corresponding third operation; combining the second operation result and the third operation result to obtain a classification result of the first sample; determining a prediction error based on the classification result and the classification label of the first sample; and updating the convolutional neural network based on the prediction error. obtaining a first sample with a classification label; performing a first operation corresponding to each layer of front-end network on the first sample based on the parameters of each layer of front-end network, to obtain a first operation result of the first sample, which includes: performing a convolution operation on the first sample based on parameters of the convolutional layer, to obtain a first intermediate result; obtaining second intermediate results that are acquired by the other GPUs performing a convolution operation on other samples obtained by the other GPUs based on parameters of the convolutional layer that the other GPUs have; performing normalization processing on the first intermediate result based on the second intermediate results of the other GPUs; and performing another operation on the first sample based on parameters of the another layer and the first intermediate result obtained after normalization processing, to obtain the first operation result of the first sample; performing a convolution operation on the first sample based on parameters of the convolutional layer, to obtain a first intermediate result; obtaining second intermediate results that are acquired by the other GPUs performing a convolution operation on other samples obtained by the other GPUs based on parameters of the convolutional layer that the other GPUs have; performing normalization processing on the first intermediate result based on the second intermediate results of the other GPUs; and performing another operation on the first sample based on parameters of the another layer and the first intermediate result obtained after normalization processing, to obtain the first operation result of the first sample; performing a corresponding second operation on the first sample based on the first operation result and the parameters of each layer of back-end network that the first GPU has, to obtain a second operation result; separately sending the first operation result to the other GPUs, so that each other GPU performs a corresponding third operation on the first sample based on their parameters of each layer of back-end network and the first operation result; receiving a third operation result obtained after each other GPU performs the corresponding third operation; combining the second operation result and the third operation result to obtain a classification result of the first sample; determining a prediction error based on the classification result and the classification label of the first sample; and updating the convolutional neural network based on the prediction error.