Distributed cluster training method and apparatus

An apparatus comprising:

• one or more processors;
• memory;
• a sample reading module stored in the memory and executable by the one or more processors to read a sample set, the sample set including at least one piece of sample data;
• an iterative training module stored in the memory and executable by the one or more processors to use the sample data and current weights to substitute into a target model training function for iterative training to obtain a first gradient before receiving a collection instruction, the collection instruction being issued by a scheduling server when a cluster system environment meets a threshold condition, wherein first weights are generated based on the first gradient obtained in a previous training and are used as current weights of a subsequent iteration of the iterative training, in response to determining that multiple iterations of the training exist before receiving the collection instruction;
• a result sending module stored in the memory and executable by the one or more processors to send the first gradient to an aggregation server in response to determining that a collection instruction is received, wherein the aggregation server collects a plurality of first gradients and calculates second weights;
• and
• an updating module stored in the memory and executable by the one or more processors to receive the second weights sent by the aggregation server to update the current weights.

One or more computer readable media storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform acts comprising:

• reading, by a training server of a plurality of training servers in a distributed cluster training system, a sample set, the sample set including at least one piece of sample data;
• using, by the training server, the sample data and current weights to substitute into a target model training function for iterative training to obtain a first gradient before receiving a collection instruction, the collection instruction being issued by a scheduling server when a cluster system environment meets a threshold condition;
• sending, by the training server, the first gradient to an aggregation server in response to determining that a collection instruction is received, wherein the aggregation server collects a plurality of first gradients from the plurality of training servers and calculates second weights;
• and
• receiving, by the training server, the second weights sent by the aggregation server to update the current weights.