Parallel Hyperparameter Search With Shared Memory Deduplication
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Solution Overview
Problem
Tuning hyperparameters in machine learning models is time-consuming and inadequate for exploring the high-dimensional non-linear search space, particularly for complex models with many variables and algorithms, acting as a brake on model development and deployment.
Innovation Solution
A method utilizing a computer system with a processing unit executing multiple parallel processes to conduct independent and global hyperparameter searches, sharing results in a memory to avoid duplicate testing and enhance exploration of the search space, including grid, random, and adaptive search methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hyperparameter tuning is performed using traditional grid search or random search methods, then the search can be systematically conducted, but the process is time-consuming and inadequate for exploring high-dimensional non-linear search spaces
Solution Approach 1:
The patent divides the hyperparameter search process into multiple independent search methods (grid search, random search, adaptive search) that can be executed in parallel. Each search method operates independently but shares results through a common memory structure, allowing simultaneous exploration of different search spaces without redundant computations.
Solution Approach 2:
The patent introduces a temporal dimension to the search space exploration by maintaining a history of tested hyperparameter combinations across multiple search methods. This history mechanism allows the system to learn from previous searches and avoid duplicate testing, effectively adding a dimensional aspect to the search process that improves efficiency.
2Productivity
If multiple independent search methods are executed in parallel, then the exploration of search space is improved and duplicate testing is avoided, but the system complexity increases
Solution Approach 1:
The patent merges multiple independent search methods into a unified parallel system that shares common resources. The search methods combine their results in a shared memory structure, allowing the system to leverage the strengths of each search method while avoiding the complexity of completely separate systems.
Solution Approach 2:
The patent creates a universal search framework that can accommodate multiple search methods (grid, random, adaptive) within a single system architecture. This multi-functional design allows the same system to perform different search strategies without requiring separate dedicated systems, reducing overall complexity.
3Measurement precision
If the search space is thoroughly explored, then optimal hyperparameter combinations are found, but the time required for tuning increases significantly
Solution Approach 1:
The patent implements feedback mechanisms where the results of hyperparameter testing are stored in shared memory and made available to other search methods. This feedback loop allows the system to learn from previous evaluations and adjust subsequent search strategies, improving optimization accuracy while reducing redundant testing time.
Solution Approach 2:
The patent performs preliminary actions by pre-testing hyperparameter combinations and storing the results in shared memory before the main search process begins. This preliminary exploration establishes a foundation of knowledge that guides subsequent search methods, improving overall efficiency and reducing the time needed for thorough exploration.
Data Source
AI summary
According to an embodiment, a method is proposed carried out by a computer system for tuning hyperparameters in a machine learning model, the computer system having a processing unit designed to execute a plurality of processes in parallel. The method comprising executing a plurality of independent hyperparameter search methods in different parallel processes of the processing unit, the results of the tests of the combinations of hyperparameters being stored in a memory in the computer system shared among the various processes, and wherein each process assesses whether a combination of hyperparameters searched for has already been tested by another process based on the results of tests stored in memory, and takes into account, in its own test history, the results of tests stored in the memory if the combination of hyperparameters searched for has already been tested.