Distributed Predictive Model Training with Early Termination
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Solution Overview
Problem
Training predictive models with large datasets is inefficient due to memory constraints, requiring extensive computational resources and time, especially when datasets exceed a single computer's memory capacity.
Innovation Solution
Divide large training datasets into smaller chunks, distribute them across multiple computing devices for parallel processing, and combine the trained models using a data collecting node, allowing for early termination of training when accuracy plateaus and reducing the number of required computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a single computer is used to train a predictive model on a large dataset, then the training process is simple to manage, but the computer's memory capacity is insufficient to store the entire dataset
Solution Approach 1:
The patent divides the large training dataset into multiple smaller chunks that can be distributed across multiple computing devices. Each computing device receives and processes a specific chunk of data independently, allowing the system to handle datasets larger than a single machine's memory capacity while maintaining manageable processing units.
2Productivity
If multiple computing devices are used to process large datasets in parallel, then the training speed increases, but the number of computing devices required increases
Solution Approach 1:
The patent implements dynamic training termination where each computing device monitors its local model's accuracy and automatically stops processing a data chunk when the accuracy plateaus. This dynamic approach allows the system to reduce the number of active computing devices over time as models converge, while still achieving high initial training speed through parallel processing.
3Measurement precision
If training on all data chunks is completed thoroughly, then the model accuracy is maximized, but the processing time increases
Solution Approach 1:
The patent incorporates feedback mechanisms where each computing device continuously monitors the accuracy of its trained model on validation data. When the accuracy curve plateaus indicates no further improvement is possible, the device automatically terminates training for that chunk. This feedback-driven early stopping prevents unnecessary processing time while maintaining sufficient model accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs stored on a computer storage medium, for training predictive models using large datasets.