Invention Grant
- Patent Title: Distributed training for deep learning models
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Application No.: US16529223Application Date: 2019-08-01
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Publication No.: US11574253B2Publication Date: 2023-02-07
- Inventor: Ivo José Garcia dos Santos , Mehdi Aghagolzadeh , Rihui Peng
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Schwegman Lundberg & Woessner. P.A.
- Main IPC: G06N20/20
- IPC: G06N20/20 ; G06N3/04 ; G06N3/08

Abstract:
A computer implemented method trains distributed sets of machine learning models by training each of the distributed machine learning models on different subsets of a set of training data, performing a first layer model synchronization operation in a first layer for each set of machine learning models, wherein each model synchronization operation in the first layer generates first updates for each of the machine learning models in each respective set, updating the machine learning models based on the first updates, performing a second layer model synchronization operation in a second layer for first supersets of the machine learning models wherein each model synchronization in the second layer generates second updates for updating each of the machine learning models in the first supersets based on the second updates such that each machine learning model in a respective first superset is the same.
Public/Granted literature
- US20210035027A1 DISTRIBUTED TRAINING FOR DEEP LEARNING MODELS Public/Granted day:2021-02-04
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