Distributed Hierarchical Worker Architecture for Scalable Machine Learning
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Solution Overview
Problem
Existing machine learning systems, particularly in natural language processing and structured prediction, face scalability issues with large numbers of training examples and features, leading to computationally intensive decoding and optimization challenges, which current approaches like maximum margin methods or voted perceptron do not adequately address.
Innovation Solution
A distributed architecture is implemented where a parameterized model is distributed across a hierarchy of workers, allowing each worker to process a portion of the training data independently, calculate updates, and combine them at higher levels to generate real updates to model parameters, enabling efficient learning and robustness without sacrificing convergence rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning system processes all training data, then training accuracy can be maintained, but computational complexity and decoding time increase significantly with large numbers of training examples and features
Solution Approach 1:
The patent divides the training data into multiple partitions and distributes them across a hierarchy of worker processes. Each worker processes a subset of data independently, computing local parameter updates. This segmentation allows the system to maintain training accuracy while reducing the computational burden on any single machine, thereby lowering overall computational complexity and decoding time.
2Productivity
If a distributed system is used to process training data, then scalability to large numbers of training examples is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The training data is segmented into partitions distributed across worker processes, enabling scalability to large datasets. Each worker independently processes its partition, and results are aggregated up the hierarchy to the master process, achieving parallel processing without requiring complex centralized coordination.
Solution Approach 2:
The patent introduces a master process as an intermediary that coordinates between worker processes. The master distributes data partitions to workers, collects parameter updates from them, and performs aggregation. This intermediary role simplifies the system architecture by centralizing coordination functions, reducing the complexity of direct peer-to-peer communication between all workers.
3Measurement precision
If traditional maximum margin approaches are used, then classification performance is achieved, but the methods do not scale well to very large numbers of training examples or models with very large numbers of features
Solution Approach 1:
The patent segments the training data into partitions distributed across multiple worker processes. Each worker independently processes its partition and computes local parameter updates using the classification model. This segmentation enables the system to scale to very large numbers of training examples and features by distributing the computational workload, while maintaining classification performance through accurate aggregation of parameter updates.
Solution Approach 2:
The patent transitions from a single-machine processing model to a distributed multi-machine model, adding the dimension of computational distribution. By organizing workers in a hierarchy and using parallel processing across multiple machines, the system achieves scalability to large datasets and large-scale models while maintaining classification performance through coordinated parameter updates.
Data Source
AI summary
Systems, methods, and apparatuses including computer program products for machine learning are provided. A method is provided that includes distributing a parameterized model to each worker of a hierarchy of workers, the parameterized model including a plurality of feature functions and corresponding model parameters, processing a portion of training data at each worker of the plurality of workers according to the parameterized model to calculate updates to model parameters, for each worker at a lowest level of the hierarchy of workers, sending the calculated updates to a next higher level worker, for each other worker in the hierarchy of workers, combining updates of the respective worker with updates received from one or more lower level workers, collecting all updates from the workers at a master to generate real updates to the model parameters, and generating an updated model using the real updates to the model parameters.