Abstract: The present application provides a method of training a deep learning model. A specific implementation solution of the method of training the deep learning model includes: determining, according to first training data for a current training round, a first target parameter required to be written into a target memory in a first network parameter required by an embedding of the first training data, wherein the target memory is a memory contained in a target processor; determining a remaining storage slot in the target memory according to a first mapping relationship between a storage slot of the target memory and a network parameter; and writing, in response to the remaining storage slot meeting a storage requirement of the first target parameter, the first target parameter into the target memory so that a computing core contained in the target processor adjusts the first network parameter according to the first training data.

Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.

Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural n