SELECTING TRAINING NODES FOR TRAINING MACHINE-LEARNING MODELS IN A DISTRIBUTED COMPUTING ENVIRONMENT

Training nodes can be selected for use in training a machine-learning model according to some aspects described herein. In one example, a system can receive performance-metric values generated by training nodes, where the training nodes are configured to generate the performance-metric values by imp...

Full description

Saved in:
Bibliographic Details
Main Authors Chen, Huamin, De Soto, Ricardo Noriega
Format Patent
LanguageEnglish
Published 15.06.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:Training nodes can be selected for use in training a machine-learning model according to some aspects described herein. In one example, a system can receive performance-metric values generated by training nodes, where the training nodes are configured to generate the performance-metric values by implementing an evaluation phase in which the training nodes partially train models using first training data. The system can select a subset of the training nodes based on the performance-metric values. The system can then transmit commands to the subset of training nodes for causing the subset of training nodes to implement a training phase in which the subset of training nodes further train the models using second training data.
Bibliography:Application Number: US202117546536