hyperopt/hyperopt

Features

• Asynchronous Optimization Frameworks - Provides an asynchronous optimization framework that decouples the generation of search points from their evaluation.
• Hyperparameter Optimization - Implements automated methods for searching and selecting the best configuration parameters for scalar-valued objective functions.
• Hyperparameter Optimizers - A Python library for minimizing objective functions by searching through real-valued, discrete, and conditional hyperparameter spaces.
• Conditional Parameter Dependencies - Provides support for complex search spaces with conditional parameter hierarchies.
• Search Space Definition - Defines stochastic search spaces using expressions that support nested conditional logic and diverse data types.
• Conditional Search Space Configuration - Constructs hierarchical parameter sets where specific distributions are active only when their parent parameters are selected.
• Automated Parameter Searches - Implements a framework for parallelizing the search for optimal model parameters across multiple workers.
• Objective Function Minimization - Minimizes scalar-valued objective functions by iteratively sampling from a defined hyperparameter search space.
• Scalar-Valued Function Optimization - Optimizes potentially stochastic scalar-valued functions to find the best set of input arguments.
• Iterative Error Minimization - Iteratively samples candidate parameters and updates the internal model to minimize a scalar-valued objective function.
• Parallel Evaluators - Spreads the computation of search points across multiple worker processes to accelerate objective function evaluation.
• Experiment Tracking - Tracks long-term progress and allows resuming interrupted searches by storing results and metadata in a database.
• Result Persistence Layers - Persists trial data in a database to track long-term progress and resume interrupted optimization searches.
• Distributed Computing Frameworks - Parallelizes the hyperparameter search process across multiple machines using external clusters or database backends.
• Distributed Optimization Synchronization - Coordinates parallel optimization trials across multiple processes using a shared database for synchronization.
• Distributed Task Workers - Implements a system of distributed workers to evaluate objective functions asynchronously without blocking the main process.
• Master-Worker Coordination - Uses a shared database to coordinate multiple optimization processes across different workers.
• Distributed State Coordination - Uses a database-backed state coordination mechanism to synchronize trial results across distributed workers.
• Real-Time Trial Synchronization - Enables real-time updates of partial results and injection of new search points into parallel experiments.
• Optimization Trials - Runs multiple optimization trials across different machines or clusters to find optimal parameters faster.
• Automated Machine Learning - Library for distributed asynchronous hyperparameter optimization.
• Hyperparameter Tuning - Distributed asynchronous hyperparameter optimization.
• Optimization - Distributed asynchronous hyperparameter optimization.

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