Asynchronous Parameter Updates in Distributed Machine Learning Models
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Solution Overview
Problem
Training deep neural networks is resource-intensive and time-consuming due to the increasing complexity with more layers and neurons, and distributed machine learning models face prolonged training times due to their serial execution and communication lag across computing devices.
Innovation Solution
A method for training distributed machine learning models involves initializing a model on multiple computing devices, where each device updates parameters asynchronously as training data is propagated, with forward and backward propagation occurring in parallel, and parameters are shared across devices to reduce computational resource requirements and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of layers and neurons in a deep neural network is increased to improve performance, then the model performance improves, but the computational resource requirements and training time increase significantly
Solution Approach 1:
The patent divides the deep neural network into multiple distributed computing devices, with each device hosting a subset of computational nodes. This segmentation allows the large model to be distributed across multiple machines, reducing the computational burden on any single device while maintaining overall model performance.
Solution Approach 2:
The patent introduces a distributed dimension by deploying computational nodes across multiple computing devices rather than concentrating them on a single machine. This dimensional expansion allows parallel processing and reduces the resource requirements per device.
2Measurement precision
If the number of training samples is increased to improve model performance, then the model performance improves, but the training time becomes prohibitively long
Solution Approach 1:
The patent implements continuous training by allowing multiple training samples to be processed in parallel across distributed computing devices without waiting for sequential completion. The asynchronous parameter updates ensure that training continues continuously rather than pausing for synchronization.
Solution Approach 2:
The patent processes training samples in parallel across multiple devices simultaneously, performing more training operations at the same time rather than sequentially. This partial parallel processing reduces overall training time while maintaining model quality.
3Device complexity
If distributed machine learning models are used to reduce computational resource requirements, then the resource requirements per device decrease, but the training time increases due to serial execution and communication lag
Solution Approach 1:
The patent implements dynamic asynchronous parameter updates where computing devices do not wait for synchronization barriers. Parameters are updated and communicated to other devices as soon as they are available, creating a dynamic training process that eliminates idle waiting time inherent in static synchronous approaches.
Solution Approach 2:
The patent performs preliminary local computations at each computing device before communication is needed. Each device computes parameter updates locally and only communicates when necessary, reducing the frequency and impact of communication lag across the distributed system.
Data Source
AI summary
Provided is a method for training distributed machine learning models. The method may include initializing a distributed machine learning model on a plurality of computing devices. Training data associated with a plurality of samples may be received. Each sample may be forward propagated through the distributed machine learning model to generate an output. A loss for each sample of the plurality of samples may be determined based on the output. The loss for each sample may be backward propagated to each computing device. The parameter(s) of each computational node may be asynchronously updated based on the loss as it is backward propagated and/or while at least one of the samples is forward propagating. The parameter(s) may be stored and/or communicated to the other computing devices. Each of the other computing devices of the plurality of computing devices may store the parameter(s). A system and computer program product are also disclosed.