Model Training Framework Automating ML Setup
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Solution Overview
Problem
Manually writing custom programming code for each machine learning model is cumbersome and resource-intensive, making it inefficient for training and evaluating machine learning models.
Innovation Solution
A model training framework that allows users to input a model definition and configuration, which specifies computations, parameters, hyperparameters, and training settings, enabling automated training across multiple processing units and providing human-readable and machine-readable formats for record-keeping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manually writing custom programming code for each machine learning model is used, then the model can be trained with specific customizations, but the process becomes cumbersome and resource intensive
Solution Approach 1:
The patent uses template copying to create a standardized framework structure that can be instantiated for different models. The template contains pre-defined computational graphs, optimization routines, and training procedures that are copied and configured for specific models, eliminating the need to write custom code from scratch while maintaining model-specific customizations.
Solution Approach 2:
The framework creates a universal template that can serve multiple model types through parameter configuration rather than code rewriting. The same template structure supports different machine learning models by adjusting hyperparameters and data specifications, making the system multi-functional across various modeling tasks.
2Reliability
If manually writing custom programming code for each machine learning model is used, then specific model requirements can be met, but the process is time consuming and resource intensive
Solution Approach 1:
The template contains pre-written and pre-tested code for common training operations, optimization algorithms, and computational graphs. By performing these actions in advance within the template, the framework eliminates repetitive manual coding during model setup, reducing time consumption while maintaining reliable training procedures through pre-validated code structures.
Solution Approach 2:
The framework enables self-service configuration where users specify model requirements through high-level parameters rather than manual coding. The system automatically generates the necessary training code, manages computational resources, and executes training procedures based on the provided configuration, reducing both time investment and the need for extensive programming knowledge.
3Ease of operation
If a standardized framework is used for model training, then coding effort is reduced, but the framework must accommodate various model types and configurations
Solution Approach 1:
The framework handles model diversity through parameter changes rather than structural modifications. By adjusting hyperparameters, data path configurations, and optimization settings within the template, the same framework structure adapts to different model types. This approach simplifies user interaction while managing internal complexity through parameterized configuration rather than multiple specialized codebases.
Data Source
AI summary
One or more computing devices, systems, and/or methods for a model training framework are provided. A definition and configuration of a model are received. Computations that the model will perform during training of the model are setup based upon the definition and configuration. Summary statistics to be tracked during training are specified. A batch of training data is input into the model to train the model based upon hyper parameters specified in the configuration of the model, and the summary statistics are tacked during the training. The parameters of the model are updated based upon a function corresponding to accuracy of the model processing the training data. The summary statistics are outputted.