Ray Tune is a Python library for distributed hyperparameter optimization and experiment execution, built on Ray. It fits teams that need to search training configurations across managed clusters rather than adjust them manually. section 5.0 section 5.4.3
The workflow centers on an objective function: it reads hyperparameters from a configuration, trains and evaluates a model, and reports a score. Tune connects that function to a search space and starts the trials. section 5.4 section 5.4.3
Ray Tune’s implementation responsibilities include:
- Supporting common machine-learning frameworks, including PyTorch, XGBoost, MXNet, and Keras. section 5.4.3
- Running searches with algorithms such as Population Based Training, BayesOptSearch, and HyperBand/ASHA, while integrating algorithms from other HPO libraries. section 5.4.3
- Using Ray to distribute code and trials across multiple GPUs and nodes, with resource management, cloud support, and fault tolerance. section 5.4.3
- Using ASHA to stop less-promising trials early and allocate time and resources to more-promising trials. section 5.4.3
Minimal execution pattern
Define an objective_function(config), train a model with config["lr"] and config["momentum"], report mean_accuracy with tune.report, declare a search space, and call tune.run. Enabling ray.init(address="auto") allows cluster execution.
