Multi-Thread Recommender Training Across Distributed Worker Nodes
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Solution Overview
Problem
Centralized training algorithms for recommender models are computationally expensive and inefficient in distributed computing environments due to the requirement of full training datasets, impacting performance as dataset size grows.
Innovation Solution
A distributed computing approach involving a controller node and worker nodes, where a controller-specific user and item parameters matrix are initialized and broadcasted, with concurrent aggregation and localized model training algorithms updating these matrices until a termination condition is satisfied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized training algorithms are used for recommender models, then training can be performed with complete data consistency, but computational cost increases and performance deteriorates as dataset size grows
Solution Approach 1:
The patent divides the centralized training process into distributed segments by partitioning the training dataset across multiple worker nodes. Each worker node independently trains a local model on its assigned data subset, while a parameter server aggregates gradients and updates global model parameters. This segmentation enables parallel processing and reduces the computational burden on any single node, thereby improving training efficiency while maintaining data consistency through coordinated parameter updates.
2Measurement precision
If the full training dataset is used in centralized algorithms, then model accuracy can be maximized, but computational expense increases
Solution Approach 1:
The training dataset is segmented and distributed across multiple worker nodes, each processing a portion of the data in parallel. This allows the system to handle large datasets without requiring a single node to process the entire dataset, thereby reducing computational expense per node while maintaining overall model accuracy through aggregated learning from all data segments.
Solution Approach 2:
The patent merges the computational results from multiple worker nodes by aggregating gradients and averaging model updates on the parameter server. This combining of distributed training results produces a unified global model that achieves the same accuracy as centralized training on the full dataset, but with reduced computational expense per node through parallel processing.
3Loss of information
If centralized algorithms process growing datasets, then comprehensive model learning is achieved, but performance negatively impacts
Solution Approach 1:
The patent segments the training process into parallel operations across multiple worker nodes, each processing data subsets simultaneously. This parallelization maintains comprehensive model learning by ensuring all data segments contribute to the global model updates, while significantly reducing training time compared to sequential centralized processing of the entire dataset.
Solution Approach 2:
The distributed training system maintains continuous useful action by allowing multiple worker nodes to process data and generate model updates simultaneously without idle time. The parameter server continuously aggregates gradients and updates global parameters, ensuring that the training process proceeds without interruption and achieves comprehensive learning efficiently.
Data Source
AI summary
A system, method, and computer-program product includes receiving an input comprising a plurality of pre-defined factor matrices and an implicit feedback dataset partitioned into a plurality of implicit feedback data subsets; distributing the input across a controller node and a plurality of worker nodes implemented in a distributed computing environment; and training a model using the controller node and the plurality of worker nodes, wherein training the model includes: initializing, by the controller node, a controller-specific user parameters matrix and a controller-specific item parameters matrix, broadcasting, by the controller node, the controller-specific user parameters matrix and the controller-specific item parameters matrix to each worker node of the plurality of worker nodes, and concurrently executing an aggregation model training algorithm at the controller node and a plurality of localized model training algorithms across the plurality of worker nodes until a training termination condition is satisfied.