GIN-Based ML Configuration Prediction for Large Search Spaces
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Solution Overview
Problem
Existing methods for predicting the configuration of a machine learning system fail to effectively address the technical problem of efficiently solving the configuration of a machine learning system configuration, which is complex due to a large search space of combinations, making it difficult to find an optimal configuration within a reasonable time.
Innovation Solution
A method using a graph isomorphism network (GIN) to predict a suitable configuration of a machine learning system by training multiple systems on a first data set, creating a second data set with performance capabilities, and selecting the best configuration for initialization and optimization, allowing exploration of a larger search space and avoiding sub-optimal architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard optimization methods are used to search for optimal configuration, then the search process is systematic, but the complexity increases exponentially due to the large search space (up to 10^23 combinations)
Solution Approach 1:
A graph isomorphism network (GIN) is introduced as an intermediary between the configuration search space and performance prediction. The GIN learns a mapping from configuration graphs to performance metrics, enabling efficient prediction without exhaustive search. This intermediary model reduces the computational complexity from exponential to polynomial time while maintaining prediction accuracy.
Solution Approach 2:
The method performs preliminary training of the GIN model on a subset of configurations before actual configuration search. This preliminary action creates a predictive framework that can quickly evaluate new configurations, avoiding the need to systematically explore all 10^23 possible combinations during the actual optimization process.
2Productivity
If the search space is reduced to make optimization feasible, then the computational complexity decreases, but the quality of the found configuration deteriorates due to limited exploration
Solution Approach 1:
The method creates a virtual copy of the configuration search process through the GIN model. Instead of physically exploring each configuration, the GIN predicts performance outcomes based on learned patterns from training data. This copying approach allows rapid evaluation of many configurations without actual training, maintaining quality while improving speed.
Solution Approach 2:
The GIN model changes the parameter representation from raw configuration specifications to learned graph embeddings. This transformation enables the model to capture complex configuration-performance relationships efficiently, allowing high-quality predictions with reduced computational parameters compared to traditional exhaustive search methods.
3Ease of operation
If architecture parameters and hyperparameters are optimized separately, then the optimization process is simpler, but the final configuration quality deteriorates because the two parameter types are dependent on each other
Solution Approach 1:
The method merges architecture parameter optimization and hyperparameter optimization into a unified configuration graph representation. The GIN processes the entire configuration (including both architecture and hyperparameters) as a single integrated input, capturing their interdependencies. This unified approach maintains optimization simplicity while improving configuration quality by considering parameter interactions.
Data Source
AI summary
A method for predicting a suitable configuration of a machine learning system for a first training data set. The method starts by training a plurality of machine learning systems on the first training data set, the machine learning systems and/or the training methods used being configured differently. This is followed by a creation of a second training data set including ascertained performances of the trained machine learning systems and the assigned configuration of the particular machine learning systems and/or training methods. This is followed by a training of a graph isomorphism network, depending on the second training data set, and a prediction in each case of the performance of a plurality of configurations not used for the training, with the aid of the GIN. A computer program and a device for carrying out the method and a machine-readable memory element, on which the computer program is stored, are also described.