Ray Tune search space design

Ray Tune helps teams explore hyperparameters without building a scheduler from scratch. The biggest wins come from designing experiments that answer a clear question.

Search spaces should encode intent

from ray import tune

param_space = {
    "lr": tune.loguniform(1e-5, 1e-2),
    "batch_size": tune.choice([32, 64, 128]),
    "dropout": tune.uniform(0.0, 0.4),
}

Early stopping

Schedulers such as ASHA can stop weak trials early and move resources to promising configurations. This is especially useful when training cost varies across parameter choices.

Experiment hygiene

• Track the exact dataset version.
• Save the best checkpoint and the winning config.
• Compare against a baseline run.
• Use enough samples to make the result credible.