US 11,386,350 B2Granted

Model parameter combination method and apparatus

AssigneeHuawei Technologies Co., Ltd.

InventorsYunfeng Shao, Jun Xu, Masood Mortazavi

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Abstract

The method and apparatus that are applied to a machine learning system which includes at least one parameter collection group and at least one parameter delivery group. Each parameter collection group is corresponding to at least one parameter delivery group. The method includes: when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group to obtain a first model parameter of the parameter collection group, where a smallest quantity s of combination nodes in the parameter collection group≤M≤a total quantity of nodes included in the parameter collection group; and sending the first model parameter of the parameter collection group to N nodes in a parameter delivery group corresponding to the parameter collection group, where 1≤N≤a total quantity of nodes included in the parameter delivery group corresponding to the parameter collection group.

Claims24

Dependent claims

A model parameter combination method, wherein the method is applied to a machine learning system, the machine learning system comprises at least one parameter collection group and at least one parameter delivery group, each parameter collection group is corresponding to at least one parameter delivery group, each parameter collection group comprises at least one node, each parameter delivery group comprises at least one node, a node comprised in the at least one parameter collection group is different from a node comprised in a corresponding parameter delivery group, and the method comprises:

when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group that meets the condition, to obtain a first model parameter of the parameter collection group that meets the condition, wherein a smallest quantity s of combination nodes in the parameter collection group that meets the condition≤M≤a total quantity of nodes comprised in the parameter collection group that meets the condition;
and
sending, to N nodes in a parameter delivery group corresponding to the parameter collection group that meets the condition, the first model parameter of the parameter collection group that meets the condition, wherein 1≤N≤a total quantity of nodes comprised in the parameter delivery group corresponding to the parameter collection group that meets the condition.

A model parameter combination apparatus, wherein the apparatus is applied to a machine learning system, the machine learning system comprises at least one parameter collection group and at least one parameter delivery group, each parameter collection group is corresponding to at least one parameter delivery group, each parameter collection group comprises at least one node, each parameter delivery group comprises at least one node, a node comprised in the at least one parameter collection group is different from a node comprised in a corresponding parameter delivery group, and the apparatus comprises:

at least one processor;
and
a non-transitory computer-readable storage medium coupled to the at least one processor and storing programming instructions for execution by the at least one processor, wherein the programming instructions instruct the at least one processor to:
when any parameter collection group meets an intra-group combination condition, combine model parameters of M nodes in the parameter collection group that meets the condition, to obtain a first model parameter of the parameter collection group that meets the condition, wherein a smallest quantity s of combination nodes in the parameter collection group that meets the condition≤M≤a total quantity of nodes comprised in the parameter collection group that meets the condition;
and
send, to N nodes in a parameter delivery group corresponding to the parameter collection group that meets the condition, the first model parameter of the parameter collection group that meets the condition, wherein 1≤N≤a total quantity of nodes comprised in the parameter delivery group corresponding to the parameter collection group that meets the condition.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/980,866Filed 16 May 2018 · continuation
Priority: 16 Nov 2015
Published: US 2018/0260739 A113 Sep 2018
Granted: 12 Jul 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06N 20/00
Machine learning
G06F 3/0646
Making use of a particular technique: horizontal data movement in storage systems, i.e. moving data in between storage devices or systems
G06F 8/10
Arrangements for software engineering: requirements analysis; Specification techniques
G06F 15/16
Digital computers in general ; Data processing equipment in general: combinations of two or more digital computers each having at least an…