Ray Tune schedules many training trials across a cluster of workers in parallel, each trial testing a different combination of hyperparameters. Rather than waiting for every trial to finish, an ASHA-style scheduler checks progress early and kills (prunes) trials that are clearly under-performing, freeing workers to launch new candidates sooner.
Ray Tune's ASHA (Asynchronous Successive Halving Algorithm) scheduler can cut total tuning time by an order of magnitude versus exhaustive grid search, because most of the compute budget is spent only on the configurations that already look promising.
A simulated Ray Tune cluster launches parallel trials across a 3D loss landscape, using an ASHA-style scheduler to prune weak configurations early while the strongest trial converges on the global minimum.
The purple-to-red surface encodes loss over two hyperparameters. Trials fall from parallel workers toward the surface; the scheduler checks each one mid-flight and kills those trailing far behind the current best, mirroring how ASHA reallocates compute in real Ray Tune runs.
Set the number of parallel workers, pick a search strategy, and tune early-stop aggressiveness to see how many trials get pruned versus reach the valley. Watch the gold beacon jump to the new best configuration as the study progresses.
Ray Tune's ASHA scheduler can evaluate orders of magnitude more configurations than grid search in the same wall-clock budget, because it stops unpromising trials after only a fraction of their training.