wehub-resource-sync
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
"""This example demonstrates the usage of Optuna with Ray Tune for
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multi-objective optimization.
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Please note that schedulers may not work correctly with multi-objective
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optimization.
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Requires the Optuna library to be installed (`pip install optuna`).
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"""
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import time
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import ray
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from ray import tune
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from ray.tune.search import ConcurrencyLimiter
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from ray.tune.search.optuna import OptunaSearch
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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(
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{
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"iterations": step,
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"loss": intermediate_score,
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"gain": intermediate_score * width,
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}
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)
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time.sleep(0.1)
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def run_optuna_tune(smoke_test=False):
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algo = OptunaSearch(metric=["loss", "gain"], mode=["min", "max"])
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algo = ConcurrencyLimiter(algo, max_concurrent=4)
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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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search_alg=algo,
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num_samples=10 if smoke_test else 100,
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),
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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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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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},
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)
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results = tuner.fit()
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print(
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"Best hyperparameters for loss found were: ",
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results.get_best_result("loss", "min").config,
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)
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print(
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"Best hyperparameters for gain found were: ",
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results.get_best_result("gain", "max").config,
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)
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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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ray.init(configure_logging=False)
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run_optuna_tune(smoke_test=args.smoke_test)
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