Model Training Data Adapter for Format Decoupling
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Solution Overview
Problem
Current model training frameworks face challenges in training speed and complexity due to the need for users to implement separate codes for each data storage type and format, leading to inefficiencies in data reading and processing.
Innovation Solution
A model training system is introduced, comprising a source data processing module that converts input data into a preset general format and a target training adaptation module that converts this data into a specific format supported by the target training module, thereby decoupling data reading from the training framework and improving training speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users implement separate codes for each data storage type and format, then the training framework can support multiple data formats, but the complexity of data reading and processing increases significantly
Solution Approach 1:
The patent introduces a data adapter as an intermediary component that sits between the data storage layer and the training framework. This adapter handles the conversion and adaptation of different data formats into a unified internal format, eliminating the need for users to implement separate codes for each format. The adapter acts as a mediator that translates various data storage types into a standardized structure that the training framework can process uniformly.
Solution Approach 2:
The data adapter is designed with universal functionality to handle multiple data storage types and formats through a single unified interface. It provides multi-functional capabilities including reading, parsing, and converting different data formats (such as TFRecord, CSV, JSON, etc.) into a common internal representation, thereby supporting diverse data sources without requiring format-specific implementation code in the training framework.
2Adaptability or versatility
If users implement separate codes for each data storage type and format, then the training framework can support multiple data formats, but the training speed decreases due to inefficiencies in data reading and processing
Solution Approach 1:
The data adapter performs preliminary actions by pre-processing and converting data into the required format before the training process begins. It reads and parses data from various storage types in advance, transforming them into a standardized internal format that can be efficiently consumed by the training framework during model training, thereby avoiding format conversion overhead during the actual training execution.
Solution Approach 2:
The adapter serves as an optimization intermediary that efficiently handles data format conversion and preprocessing operations. By centralizing these operations in a dedicated component, the system achieves better performance through optimized data reading paths and reduced redundancy, thereby improving training speed while maintaining format compatibility.
3Adaptability or versatility
If the training framework handles both training algorithms and data format compatibility, then it can perform complete model training, but it cannot focus on training algorithms due to data format compatibility concerns
Solution Approach 1:
The patent extracts the data format compatibility handling functionality from the training framework and places it into a separate, independent data adapter component. This separation allows the training framework to focus exclusively on training algorithms without being burdened by data format compatibility concerns. Users can implement training algorithms with ease, knowing that the adapter will handle all data format adaptations automatically.
Solution Approach 2:
The system is segmented into distinct functional modules: the data adapter module handles all data reading, parsing, and format conversion operations, while the training framework module focuses on model training algorithms. This segmentation creates clear separation of concerns, making the training framework easier to use for algorithm implementation while maintaining comprehensive data format support through the specialized adapter component.
Data Source
AI summary
A model training system comprises: a source data processing module and one or more training frameworks, wherein the training framework comprises a training adaptation module and a training module, wherein the source data processing module performs data format conversion on an input data set corresponding to service requirements, so as to obtain initial training data in a preset general format, and outputs the initial training data to a target training framework corresponding to the service requirements; converting the initial training data by means of a target training adaptation module comprised in the target training framework, so as to obtain target training data conforming to a specified data format supported by a target training module, and outputting the target training data to the target training module for model training, so as to perform training to obtain a model satisfying a training end condition.