Virtualized S2S Model Training via Parallel Segmentation
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Solution Overview
Problem
Current machine learning model training and hosting systems face challenges such as high computational requirements, steep learning curves, limited flexibility in handling different data formats, and inefficiencies in resource allocation, which hinder widespread adoption of sequence-to-sequence (S2S) models due to their complexity and resource-intensive nature.
Innovation Solution
The system provides a virtualized environment that allows users to train and host machine learning models using virtual machines, enabling distributed training, automatic resource allocation, and flexible execution of S2S models across various data formats, with features like automatic initialization of virtual machine instances, container management, and dynamic resource scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If S2S models are trained using traditional systems, then model accuracy can be achieved, but computational resources and training time are excessively high
Solution Approach 1:
The patent segments the S2S model training process into multiple parallel training runs with different hyperparameter configurations. Multiple virtual machine instances train different model versions simultaneously, dividing the overall training task into manageable segments that can be executed in parallel, reducing total computational resource requirements while maintaining model accuracy.
Solution Approach 2:
The system performs preliminary actions by automatically generating hyperparameter configurations and preparing multiple virtual machine instances before training begins. Hyperparameter optimization is pre-computed based on previous training results, and training jobs are pre-configured with optimal settings, reducing the computational burden during actual model training while preserving accuracy.
2Measurement precision
If S2S models are trained using traditional systems, then model performance can be achieved, but training time is excessively long
Solution Approach 1:
The training process is segmented into multiple parallel jobs running on different virtual machine instances. Each instance trains a model version with specific hyperparameters, and the segments are executed concurrently rather than sequentially, dramatically reducing total training time while maintaining model performance through ensemble selection.
Solution Approach 2:
The system maintains continuous useful action by automatically launching new training jobs as others complete, ensuring the computing cluster is constantly utilized. The pipeline continuously ingests training data, executes multiple model versions in parallel, and evaluates results without idle time, maximizing throughput and reducing overall training time while preserving model performance.
3Adaptability or versatility
If virtual machine instances are manually managed, then resource allocation can be controlled, but system complexity and operational overhead increase
Solution Approach 1:
The system implements self-service by automatically provisioning virtual machine instances, configuring hyperparameters, launching training jobs, and managing resource allocation without manual intervention. The platform autonomously monitors training progress, allocates computing resources dynamically, and orchestrates the entire S2S model training pipeline, reducing system complexity from the user perspective while maintaining flexible resource control.
Solution Approach 2:
The virtualized training environment provides universal functionality by consolidating multiple functions into a single platform: hyperparameter optimization, virtual machine provisioning, parallel job execution, model evaluation, and result aggregation. This multi-functional system replaces multiple separate manual processes, reducing operational overhead while maintaining adaptability in resource allocation across different S2S model training scenarios.
4Measurement precision
If multiple S2S models are trained simultaneously, then model selection accuracy improves, but resource requirements increase
Solution Approach 1:
The patent merges multiple training operations into a unified virtualized environment where multiple S2S model versions are trained simultaneously on the same infrastructure. By combining resource management, hyperparameter optimization, and parallel execution into a single system, the platform achieves improved model selection accuracy through ensemble evaluation while optimizing resource utilization to avoid linear increases in resource requirements.
Solution Approach 2:
The system changes parameters dynamically by automatically adjusting hyperparameter configurations for each model version based on previous training results and performance metrics. Different model instances use varied hyperparameter settings optimized for their specific configurations, allowing multiple models to be trained efficiently with tailored parameters rather than uniform resource allocation, improving selection accuracy while controlling overall resource consumption.
Data Source
AI summary
Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.