Distributed Training Control Descriptors for ML Model Optimization
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Solution Overview
Problem
Existing machine learning models often require large training data sets and numerous hyper-parameter combinations, leading to significant computational challenges and inefficiencies, particularly when dealing with models that need to be trained within short timeframes.
Innovation Solution
The implementation of automated distributed training methods that utilize distributed training control descriptors (DTCDs) to orchestrate data and model parallelism across a network-accessible machine learning service and a fleet of distributed computing resources, allowing for efficient batched training and adaptive experimentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If large training data sets are used to improve model quality, then the quality of predictions is improved, but the computational resources and time required for training increase significantly
Solution Approach 1:
The patent divides the large training data set into multiple smaller batches, allowing the training process to handle data in manageable segments. This segmentation enables parallel processing across multiple compute resources, reducing overall training time while maintaining model quality through comprehensive data processing.
Solution Approach 2:
The patent introduces a distributed computing dimension by deploying training tasks across multiple compute resources in a network. This dimensional shift from single-node to multi-node processing enables simultaneous training on different data batches, effectively reducing training time without sacrificing model quality.
2Manufacturing precision
If multiple hyper-parameter combinations are tested to find optimal settings, then the model performance is improved, but the number of experiments and computational cost increase
Solution Approach 1:
The patent segments the hyper-parameter experimentation process into organized batches, where different hyper-parameter combinations are tested systematically. This segmentation allows for efficient exploration of the hyper-parameter space by processing multiple combinations in parallel across distributed compute resources, reducing the overall complexity of experimentation.
Solution Approach 2:
The patent uses training control descriptors that can be copied and reused across different experiment configurations. These descriptors encapsulate hyper-parameter settings and training parameters, allowing efficient replication of experiments and reducing the computational overhead of setting up multiple distinct experiment configurations.
3Manufacturing precision
If the entire training data set is processed for each hyper-parameter combination, then the accuracy of each experiment is improved, but the time required for each experiment increases
Solution Approach 1:
The patent processes training data in batches rather than loading the entire data set for each experiment. This segmentation allows the system to maintain accurate experimentation by processing data in manageable chunks while significantly reducing the time required for each hyper-parameter combination test through parallel processing.
Solution Approach 2:
The patent implements continuous training where models can be trained on batches of data continuously across multiple compute resources. This continuous action allows overlapping of experiments, where different hyper-parameter combinations are evaluated simultaneously on different data batches, reducing total experiment duration while maintaining accuracy.
Data Source
AI summary
Multiple distinct control descriptors, each specifying an algorithm and values of one or more parameters of the algorithm, are created. A plurality of tuples, each indicating a respective record of a data set and a respective descriptor, are generated. The tuples are distributed among a plurality of compute resources such that the number of distinct descriptors indicated in the tuples received at a given resource is below a threshold. The algorithm is executed in accordance with the descriptors' parameters at individual compute resources.