wehub-resource-sync
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
"""This example demonstrates the usage of Optuna define-by-run with Ray Tune.
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It also checks that it is usable with a separate scheduler.
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Requires the Optuna library to be installed (`pip install optuna`).
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For an example of using a Tune search space, see
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:doc:`/tune/examples/optuna_example`.
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"""
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import time
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from typing import Any, Dict, Optional
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import ray
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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.optuna import OptunaSearch
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def evaluation_fn(step, width, height, mult=1):
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return (0.1 + width * step / 100) ** (-1) + height * 0.1 * mult
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def easy_objective(config):
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# Hyperparameters
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width, height, mult = config["width"], config["height"], config.get("mult", 1)
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print(config)
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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, mult)
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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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def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
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"""Define-by-run function to create the search space.
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Ensure no actual computation takes place here. That should go into
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the trainable passed to ``Tuner`` (in this example, that's
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``easy_objective``).
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For more information, see https://optuna.readthedocs.io/en/stable\
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/tutorial/10_key_features/002_configurations.html
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This function should either return None or a dict with constant values.
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"""
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# This param is not used in the objective function.
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activation = trial.suggest_categorical("activation", ["relu", "tanh"])
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trial.suggest_float("width", 0, 20)
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trial.suggest_float("height", -100, 100)
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# Define-by-run allows for conditional search spaces.
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if activation == "relu":
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trial.suggest_float("mult", 1, 2)
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# Return all constants in a dictionary.
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return {"steps": 100}
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def run_optuna_tune(smoke_test=False):
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algo = OptunaSearch(space=define_by_run_func, metric="mean_loss", mode="min")
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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 smoke_test else 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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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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