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- """This example demonstrates the usage of BayesOpt with Ray Tune.
- It also checks that it is usable with a separate scheduler.
- Requires the BayesOpt library to be installed (`pip install bayesian-optimization`).
- """
- import time
- from ray import tune
- from ray.tune.schedulers import AsyncHyperBandScheduler
- from ray.tune.search import ConcurrencyLimiter
- from ray.tune.search.bayesopt import BayesOptSearch
- def evaluation_fn(step, width, height):
- return (0.1 + width * step / 100) ** (-1) + height * 0.1
- def easy_objective(config):
- # Hyperparameters
- width, height = config["width"], config["height"]
- for step in range(config["steps"]):
- # Iterative training function - can be any arbitrary training procedure
- intermediate_score = evaluation_fn(step, width, height)
- # Feed the score back back to Tune.
- tune.report({"iterations": step, "mean_loss": intermediate_score})
- time.sleep(0.1)
- if __name__ == "__main__":
- import argparse
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--smoke-test", action="store_true", help="Finish quickly for testing"
- )
- args, _ = parser.parse_known_args()
- algo = BayesOptSearch(utility_kwargs={"kind": "ucb", "kappa": 2.5, "xi": 0.0})
- algo = ConcurrencyLimiter(algo, max_concurrent=4)
- scheduler = AsyncHyperBandScheduler()
- tuner = tune.Tuner(
- easy_objective,
- tune_config=tune.TuneConfig(
- metric="mean_loss",
- mode="min",
- search_alg=algo,
- scheduler=scheduler,
- num_samples=10 if args.smoke_test else 1000,
- ),
- run_config=tune.RunConfig(name="my_exp"),
- param_space={
- "steps": 100,
- "width": tune.uniform(0, 20),
- "height": tune.uniform(-100, 100),
- },
- )
- results = tuner.fit()
- print("Best hyperparameters found were: ", results.get_best_result().config)
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