Abstract: A model optimization method is provided. The method includes: receiving optimization request information entered by a user or sent by an artificial intelligence AI application, where the optimization request information includes a first model file, the first model file includes M operators, each operator is used to perform one matrix multiplication calculation, each operator corresponds to one kernel function, and M is a positive integer; generating a second model file based on the first model file, where the second model file includes N fused operators, each fused operator is used to perform at least two matrix multiplication calculations, each fused operator corresponds to one kernel function, N is a positive integer, and N<M; and providing the second model file for the user or sending the second model file to the AI application.

Abstract: A method for training a neural network, and a related device are provided. The method may be applied to a scenario in which a neural network is trained in the field of artificial intelligence. When an Nth round of training of the neural network is being performed, a first computational graph may be obtained; and after it is determined that a first compiled code corresponding to the first computational graph has been stored in a system, the first compiled code may be directly executed, where the first compiled code is generated during execution of an Mth round of training of the neural network, and M is less than N. Because there is no need to perform an operation of converting the first computational graph into an intermediate representation, and obtaining a compiled code based on the intermediate representation, overheads of computer resources are reduced.