Repeated Hyper-Parameter Optimization With Non-Uniform HPC Pruning
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Solution Overview
Problem
Existing methods for hyper-parameter optimization in machine learning and web services applications are inefficient, requiring significant computational resources and time due to the large number of possible parameter combinations, with poor choices leading to suboptimal results and resource wastage.
Innovation Solution
A robust non-uniform pruning-based algorithm (RNP) that leverages previous hyper-parameter selections from related tasks to aggressively prune less promising options, focusing resources on previously successful combinations, thereby reducing the number of iterations and computational resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hyper-parameter optimization methods are used to explore all possible parameter combinations, then the quality of model inference is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The system performs preliminary actions by selecting and evaluating a diverse set of hyper-parameter combinations before full optimization. It uses historical data and task characteristics to pre-identify promising parameter sets, reducing the need to exhaustively search all possible combinations while maintaining high inference quality.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring task performance and using results to refine future hyper-parameter selections. Historical performance data is fed back into the selection process, allowing the system to learn from previous outcomes and make more informed decisions about which parameter combinations to explore further.
2Measurement precision
If exhaustive search of hyper-parameter combinations is performed, then optimal parameters are identified, but the time required for optimization increases
Solution Approach 1:
The system performs preliminary selection of hyper-parameter combinations based on task characteristics and historical data before conducting full evaluations. This pre-filtering action reduces the search space significantly while maintaining a high probability of identifying optimal parameters within the reduced set.
Solution Approach 2:
Instead of exhaustively searching all possible hyper-parameter combinations, the system applies partial action by evaluating only a strategically selected subset. It uses task-specific heuristics and historical performance data to determine which combinations are most likely to be optimal, accepting that this partial search is sufficient for practical purposes.
3Adaptability or versatility
If hyper-parameter optimization is performed for each task independently, then task-specific optimal parameters are achieved, but resource wastage increases due to redundant computations
Solution Approach 1:
The system achieves universality by creating a shared pool of historical performance data that serves multiple tasks. Instead of independently optimizing each task from scratch, the system leverages common historical insights and parameter performance patterns across related tasks, reducing redundant computations while maintaining task-specific optimization.
Solution Approach 2:
The system implements feedback by continuously updating a shared historical database with performance results from all tasks. This feedback loop allows the system to learn from every task's outcomes and use that knowledge to guide future optimizations, preventing resource wastage by avoiding re-evaluation of parameters that have already been thoroughly tested in similar contexts.
Data Source
AI summary
A particular hyper-parameter combination (HPC) that was recommended for a first task is included in a collection of candidate HPCs evaluated for a second task. Hyper-parameter analysis iterations are conducted for the second task using the collection. In one of the iterations, the second task is executed using a first iteration-specific set of HPCs, including the particular HPC and one or more other members of the collection. One or more of the HPCs of the first iteration-specific set of HPCs are pruned to generate a second iteration-specific set of HPCs for a subsequent iteration. HPCs are selected for pruning based on a comparison of their results with the results obtained from the particular HPC that was recommended for the first task. A recommended HPC for the second task is identified based on results of the analysis iterations.