A system and a method for training a parameter set in a neural network includes a main-control-node set, used for controlling a training process and storing a data set and a parameter set that are used for training, where the main-control-node set includes M main control nodes, every two of the M main control nodes are in a communication connection, and at least one main control node of the M main control nodes is configured to back up the parameter set. The system also includes N training-node sets, where the training-node set includes multiple training nodes, and the training node is configured to perform training according to a data set and a parameter set that are delivered by the main-control-node set, and send a training result to a corresponding main control node.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a main-control-node set comprising M main control nodes, wherein the main-control-node set controls a process of training a parameter set in a neural network and storing a data set and the parameter set that are used in the process of training the parameter set, wherein the data set comprises multiple data subsets, wherein the parameter set comprises multiple parameter subsets, wherein the multiple parameter subsets are stored separately in different main control nodes, wherein a set of parameter subsets stored in all main control nodes in the main-control-node set constitutes the parameter set, wherein every two of the M main control nodes are in a communication connection, and wherein at least one main control node of the M main control nodes is configured to back up the parameter set, and wherein M is a positive integer greater than 1; and N training-node sets, wherein each of the N training-node sets is in a communication connection with the main-control-node set, wherein a training-node set of the N training-node sets comprises multiple training nodes, wherein the training node is configured to: receive the data subset and the parameter set that are delivered by the main-control-node set, train, according to the received data subset and parameter set, a parameter subset for which the training node is responsible, and send a training result to a main control node storing the parameter subset, wherein N is a positive integer greater than 1, wherein different data subsets using any two of the N training-node sets for training, and wherein a set of parameter subsets trained by all training nodes in each training-node set is the parameter set. 2. The system according to claim 1, wherein the training result is a parameter variation obtained by a training node by training, according to the received data subset and parameter set, the parameter subset for which the training node is responsible, of the parameter subset for which the training node is responsible, and wherein the main control node in the main-control-node set is further configured to: receive the parameter variation sent by the training node; and update, according to the parameter variation, the parameter subset stored in the main control node. 3. The system according to claim 1, wherein the main-control-node set is further configured to: divide the parameter set into multiple parameter subsets; store the multiple parameter subsets separately in different main control nodes, wherein a set of the parameter subsets stored in all of the main control nodes in the main-control-node set is the parameter set; and determine each training node in the N training-node sets according to sizes of the multiple parameter subsets. 4. The system according to claim 1, wherein the main control node is further configured to: update, at a first time point according to a parameter variation sent by a first training node of a first-training-node set, the parameter subset stored in the main control node; and update, at a second time point according to a parameter variation sent by a second training node of a second-training-node set, the parameter subset stored in the main control node. 5. The system according to claim 1, wherein the main-control-node set is further configured to: determine, according to an accuracy of the training result, whether to stop the process of training the parameter set. 6. The system according to claim 1, wherein a training node is further configured to: receive an instruction sent by the main-control-node set; and stop the process of training the parameter set. 7. The system according to claim 1, wherein every two training nodes in a same training-node set are in a communication connection. 8. A method comprising: storing, by a main-control-node set, a data set and a parameter set that for training, wherein the main-control-node set comprises M main control nodes, wherein every two of the M main control nodes are in a communication connection, wherein M is a positive integer greater than 1, and N is a positive integer greater than 1, wherein the data set comprises multiple data subsets, wherein the parameter set comprises multiple parameter subsets, wherein the multiple parameter subsets are stored separately in different main control nodes, wherein a set of parameter subsets stored in all main control nodes in the main-control-node set constitutes the parameter set, and wherein at least one main control node of the M main control nodes is configured to back up the parameter set; delivering, by a main control node in the main-control-node set, a data subset and a parameter subset to a training node; and receiving, by the main control node in the main-control-node set, a training result sent by the training node, wherein the training node belongs to a training-node set, wherein the training-node set is in a communication connection with the main-control-node set, wherein the training-node set comprises multiple training nodes, and wherein the training result is obtained by performing training, according to the data subset and parameter set that are delivered by the main-control-node set. 9. The method according to claim 8, wherein the training result is a parameter variation, and wherein the method further comprises: receiving, by the main control node in the main-control-node set, the parameter variation sent by the training node; and updating, by the main control node in the main-control-node set according to the parameter variation, the parameter subset stored in the main control node. 10. The method according to claim 8, wherein storing the data set and the parameter set comprises: dividing, by the main-control-node set, the parameter set into multiple parameter subsets; and storing the multiple parameter subsets separately in different main control nodes, wherein the set of the parameter subsets stored in all of the main control nodes in the main-control-node set is the parameter set; and wherein the method further comprises determining, by the main-control-node set, each training node in N training-node sets according to sizes of the multiple parameter subsets. 11. The method according to claim 8, wherein updating the parameter subset stored in the main control node comprises: updating, by the main control node in the main-control-node set, at a first time point, according to a parameter variation sent by a first training node of a first-training-node set, the parameter subset stored in the main control node; and updating, by the main control node in the main-control-node set, at a second time point, according to a parameter variation sent by a second training node of a second-training-node set, the parameter subset stored in the main control node. 12. The method according to claim 8, wherein the method further comprises: determining, by the main-control-node set according to an accuracy of the training result, whether to stop training the parameter set. 13. The method according to claim 8, wherein at least one main control node stores and is responsible for a first of the parameter subsets, wherein at least two training nodes are responsible for a second of the parameter subsets, wherein the at least two training nodes belong to different training-node sets, wherein different data subsets use any two of multiple training-node sets for training, and wherein a set of parameter subsets trained by all training nodes in each training-node set is the parameter set. 14. The method according to claim 8, wherein every two training nodes in a same training-node set are in a communication connection. 15. A system comprising: a main-control-node set comprising M main control nodes, wherein the main-control-node set is configured to control a process of training a parameter set in a neural network and store a data set and a parameter set that are used in the process of training the parameter set, wherein the data set comprises multiple data subsets, wherein the parameter set comprises multiple parameter subsets, wherein the multiple parameter subsets are stored separately in different main control nodes, wherein a set of parameter subsets stored in all main control nodes in the main-control-node set constitutes the parameter set, wherein every two of the M main control nodes are in a communication connection, and at least one main control node of the M main control nodes is configured to back up the parameter set, wherein M is a positive integer greater than 1; and N training-node sets, wherein each training-node set of the N training-node sets is in a communication connection with the main-control-node set, and wherein the N training-node sets comprise multiple training nodes, wherein the training node is configured to: receive the data subset and the parameter set that are delivered by the main-control-node set, train, according to the received data subset and parameter set, a parameter subset for which the training node is responsible, and send a training result to a main control node storing the parameter subset, wherein N is a positive integer greater than 1, wherein different data subsets using any two of the N training-node sets for training, and wherein a set of parameter subsets trained by all training nodes in each training-node set constitutes the parameter set. 16. The system according to claim 15, wherein the training result is a parameter variation, obtained by a training node by training, according to the received data subset and parameter set, the parameter subset for which the training node is responsible, and wherein the main control node is further configured to: receive the parameter variation sent by the training node; and update, according to the parameter variation, the parameter subset stored in the main control node. 17. The system according to claim 16, wherein the main-control-node set is further configured to: divide the parameter set into multiple parameter subsets; store the multiple parameter subsets separately in different main control nodes, wherein the set of the parameter subsets stored in all of the main control nodes in the main-control-node set constitutes the parameter set; and determine each training node in the N training-node sets according to sizes of the multiple parameter subsets. 18. The system according to claim 15, wherein the main control node is further configured to: update, at a first time point, according to a parameter variation sent by a first training node of a first-training-node set, the parameter subset stored in the main control node; and update, at a second time point, according to a parameter variation sent by a second training node of a second-training-node set, the parameter subset stored in the main control node. 19. The system according to claim 18, wherein the main-control-node set is further configured to: determine, according to an accuracy of the training result, whether to stop the process of training the parameter set. 20. The system according to claim 15, wherein a training node is further configured to: receive an instruction sent by the main-control-node set and stop the process of training the parameter set.