Standardizing Machine Learning Framework Versions
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Solution Overview
Problem
Conventional model integration systems for deep learning lack standardization in model development techniques, leading to inefficiencies in updating and improving models, particularly due to the lack of standardized framework environments and library information management.
Innovation Solution
An information processing system comprising an acquisition unit, environment setting unit, model processing unit, management unit, and interface providing unit, which standardizes the development environment by acquiring design information, setting a framework environment, generating or updating models, managing development history, and providing a standardized interface for users, thereby streamlining the machine learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional model integration systems are used without standardization, then flexibility in using different frameworks and libraries is maintained, but maintainability and efficiency of model updates deteriorate
Solution Approach 1:
The patent applies parameter changes by standardizing framework environment versions and library information versions as fixed parameters. The environment setting unit configures specific version parameters for frameworks (e.g., TensorFlow 2.3.0, PyTorch 1.7.0) and libraries (e.g., NumPy 1.19.2, Pandas 1.1.4), transforming the flexible but chaotic version selection into a controlled parameter management system. This resolves the contradiction by maintaining ease of development through standardized parameters while improving maintainability through consistent version control across all models.
Solution Approach 2:
The patent segments the model development environment into distinct standardized components: framework environment (TensorFlow/PyTorch versions), library information (NumPy, Pandas, Scikit-learn versions), and model configuration files. By dividing the development environment into separable, standardized segments with explicit version specifications, the system enables independent management and updating of each component, thereby improving maintainability while preserving development flexibility through modular configuration.
2Adaptability or versatility
If each developer builds their own development environment independently, then customization and adaptability are improved, but device complexity and time consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring standardized framework environments and library information versions before model development begins. The environment setting unit prepares standardized configurations (e.g., TensorFlow 2.3.0 with specific library versions) in advance, so developers inherit a ready-to-use environment rather than building it from scratch. This reduces setup complexity and time while maintaining adaptability through standardized yet flexible configuration parameters.
Solution Approach 2:
The patent creates a universal standardized environment that serves multiple developers and models simultaneously. The same framework version specifications and library information configurations are reused across different model development projects, eliminating the need for each developer to build a unique environment. This universal approach reduces overall system complexity while maintaining adaptability through parameterized configurations that can be adjusted when needed.
3Manufacturing precision
If new generation modules and library information are released frequently, then model accuracy and functionality are improved, but standardization and stability deteriorate
Solution Approach 1:
The patent applies dynamics by creating a standardized environment that can be dynamically updated while maintaining stability. The system establishes baseline version standards for frameworks and libraries that provide stability, while allowing controlled updates through the environment setting unit. When new modules or library versions are released, the system can selectively update specific components while maintaining the standardized structure, thus achieving both model accuracy improvement through new features and environmental stability through controlled evolution.
4Productivity
If standardization of development environment is implemented, then maintainability and efficiency are improved, but ease of operation and flexibility may worsen
Solution Approach 1:
The patent applies self-service by making the standardized environment configuration automatic and self-configuring. The environment setting unit automatically retrieves and configures the standardized framework environment and library information versions based on pre-defined standards, without requiring manual intervention from developers. This automation maintains ease of operation by eliminating manual configuration tasks while improving productivity through consistent, efficient model updates and deployments.
Data Source
AI summary
According to one embodiment, an information processing system includes an acquisition unit, an environment setting unit, a model processing unit, a management unit, an interface unit, and a control unit. The acquisition unit acquires design information and target data of machine learning. The environment setting unit sets a framework environment and library information executing the design information, and sets a software configuration supporting execution of the machine learning. The model processing unit generates or updates a model by executing learning for the target data using the design information and framework environment. The management unit manages design information, information about the framework environment and information about the model. The interface providing unit receives, from a user, an output instruction for the environment setting information, and presents, to the user, setting information or the like being recommended.