Tune distributed model-selection and training interfaces

What changed

Separates trainable-model and trial-scheduler interfaces so intermediate reporting, checkpointing and runtime configuration changes support varied distributed search algorithms over Ray.

Scientific scope

Separates trainable-model and trial-scheduler interfaces so intermediate reporting, checkpointing and runtime configuration changes support varied distributed search algorithms over Ray. This is an orchestration/API contribution, not invention of Hyperband, PBT or Bayesian optimization. Algorithms require cooperative reporting/restorable state and appropriate resources; original2018interfaces are not claimed as current APIs. First two authors contribute equally.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. This is an orchestration/API contribution, not invention of Hyperband, PBT or Bayesian optimization. Algorithms require cooperative reporting/restorable state and appropriate resources; original2018interfaces are not claimed as current APIs. First two authors contribute equally.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Tune: A Research Platform for Distributed Model Selection and Training

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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