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