Distributed Model Training with Aggregated Gradient Updates
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Solution Overview
Problem
The increasing amount of training data leads to a significant increase in communication interactions between worker nodes and parameter nodes, reducing the efficiency of model training due to network traffic congestion.
Innovation Solution
A method for distributed training of machine learning models involves processing groups of training samples separately to obtain local gradients, which are then used to determine a global gradient for updating training parameters, with reduced communication frequency between nodes by aggregating gradients only after multiple iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training is performed once in worker nodes and parameter gradients are transmitted to parameter node for each training iteration, then model training can be performed in parallel across multiple worker nodes, but the number of communication interactions between worker nodes and parameter node increases significantly, causing network traffic congestion and reducing training efficiency
Solution Approach 1:
The patent merges multiple gradient transmission operations into a single aggregated transmission. Worker nodes accumulate local gradients from multiple training iterations and transmit them collectively to the parameter node, reducing the frequency of communication interactions and network traffic volume while maintaining parallel training productivity
Solution Approach 2:
Worker nodes perform preliminary gradient accumulation locally before transmitting to the parameter node. By pre-aggregating gradients from multiple iterations at the worker node level, the system reduces subsequent communication overhead and network traffic while preserving the benefits of parallel processing
2Productivity
If multiple worker nodes are used to process training samples in parallel, then learning efficiency is improved, but the communication overhead and coordination between nodes increases, reducing overall training efficiency
Solution Approach 1:
The patent segments the gradient computation and aggregation processes across different levels. Worker nodes independently compute local gradients from their assigned data partitions, then aggregate these segmented gradients at the parameter node to form the global gradient, reducing coordination complexity while maintaining parallel learning efficiency
Solution Approach 2:
Each worker node performs partial gradient computation on its subset of training data rather than processing the entire dataset. This partial action approach allows parallel processing while reducing the communication burden on each node, as only local gradients need to be transmitted and aggregated
Data Source
AI summary
A method, apparatus, computer device and storage medium for distributed training of a machine learning model. The method includes: obtaining more than one group of training samples, and processing each group of training samples separately according to a current training parameter of a machine learning model to obtain a corresponding parameter gradient; determining a local gradient of a current training process based on the parameter gradient corresponding to each group of training samples; transmitting the local gradient of the current training process to a parameter node; and obtaining the updated training parameter from the parameter node, and performing model training on the machine learning model based on the updated training parameter until a training end condition is met.