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- """This example demonstrates the usage of Optuna with Ray Tune for
- multi-objective optimization.
- Please note that schedulers may not work correctly with multi-objective
- optimization.
- Requires the Optuna library to be installed (`pip install optuna`).
- """
- import time
- import ray
- from ray import tune
- from ray.tune.search import ConcurrencyLimiter
- from ray.tune.search.optuna import OptunaSearch
- 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,
- "loss": intermediate_score,
- "gain": intermediate_score * width,
- }
- )
- time.sleep(0.1)
- def run_optuna_tune(smoke_test=False):
- algo = OptunaSearch(metric=["loss", "gain"], mode=["min", "max"])
- algo = ConcurrencyLimiter(algo, max_concurrent=4)
- tuner = tune.Tuner(
- easy_objective,
- tune_config=tune.TuneConfig(
- search_alg=algo,
- num_samples=10 if smoke_test else 100,
- ),
- param_space={
- "steps": 100,
- "width": tune.uniform(0, 20),
- "height": tune.uniform(-100, 100),
- # This is an ignored parameter.
- "activation": tune.choice(["relu", "tanh"]),
- },
- )
- results = tuner.fit()
- print(
- "Best hyperparameters for loss found were: ",
- results.get_best_result("loss", "min").config,
- )
- print(
- "Best hyperparameters for gain found were: ",
- results.get_best_result("gain", "max").config,
- )
- 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()
- ray.init(configure_logging=False)
- run_optuna_tune(smoke_test=args.smoke_test)
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