Asynchronous Meta Learning for Distributed Model Training
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Solution Overview
Problem
Existing machine learning model training methods, particularly in cloud computing environments, face inefficiencies due to heterogeneous computing resources and network connectivity variations, leading to suboptimal training results as they often require synchronous updates across all nodes, which can be slow and inflexible, especially in asynchronous training scenarios.
Innovation Solution
A system employing multiple software agents, including a learning manager and trainers, that evaluates and optimizes model parameters and hyperparameters across a distributed neural network, allowing asynchronous training with partial gradient applications and dynamic hyperparameter adjustments to determine the best model parameters and training scores, thereby exploiting the heterogeneous nature of cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronous training is used to ensure consistent model parameter updates across all nodes, then model training accuracy is improved, but training speed deteriorates due to waiting for the slowest node
Solution Approach 1:
The patent segments the model parameter updates into multiple schemes (e.g., full synchronization, partial synchronization, asynchronous updates) that can be applied independently to different node groups or at different times, allowing the system to balance between consistency and speed by selecting appropriate segmentation strategies
Solution Approach 2:
The system dynamically adjusts the synchronization strategy based on real-time node performance, network conditions, and training progress. Nodes can transition between synchronous and asynchronous update modes, and the system can adaptively change which nodes participate in synchronous updates versus asynchronous updates, optimizing both accuracy and speed
2Productivity
If asynchronous training is used to improve training speed by allowing nodes to update parameters independently, then productivity is improved, but model training accuracy deteriorates due to inconsistent parameter updates
Solution Approach 1:
The system implements feedback mechanisms where nodes report their update status, performance metrics, and parameter changes to a central coordinator or peer nodes. This feedback enables the system to detect divergence, adjust synchronization frequency, and correct inconsistent updates, thereby maintaining accuracy while preserving asynchronous speed benefits
Solution Approach 2:
The patent employs multiple parameter update schemes with different synchronization frequencies and consistency requirements. The system can change parameters such as update intervals, synchronization thresholds, and node selection criteria dynamically, allowing nodes to operate asynchronously while still achieving consistent convergence through adaptive parameter adjustment
3Adaptability or versatility
If multiple software agents are deployed to evaluate and optimize model parameters across distributed neural networks, then adaptability is improved, but device complexity increases
Solution Approach 1:
The software agents are designed with multi-functionality, serving as trainers, evaluators, and coordinators simultaneously. Each agent can perform multiple roles depending on the training phase and system needs, reducing the total number of specialized components required while maintaining high adaptability across different training scenarios
Data Source
AI summary
Building machine learning models by receiving, a plurality of training process scores associated with the model parameter lists, determining, a best model parameter list according to the training process scores, determining a descendant model parameter list according to the best model parameter list, wherein the descendant parameter list comprises a portion of the best model parameter list, distributing the descendant model parameter list, conducting a model training process according to the descendant model parameter list, determining a training process score according to the descendant model parameter list, and sending the training process score for the descendant model parameter list.