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- """This example demonstrates basic Ray Tune random search and grid search."""
- import time
- import ray
- from ray import tune
- def evaluation_fn(step, width, height):
- time.sleep(0.1)
- 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})
- 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)
- # This will do a grid search over the `activation` parameter. This means
- # that each of the two values (`relu` and `tanh`) will be sampled once
- # for each sample (`num_samples`). We end up with 2 * 50 = 100 samples.
- # The `width` and `height` parameters are sampled randomly.
- # `steps` is a constant parameter.
- tuner = tune.Tuner(
- easy_objective,
- tune_config=tune.TuneConfig(
- metric="mean_loss",
- mode="min",
- num_samples=5 if args.smoke_test else 50,
- ),
- param_space={
- "steps": 5 if args.smoke_test else 100,
- "width": tune.uniform(0, 20),
- "height": tune.uniform(-100, 100),
- "activation": tune.grid_search(["relu", "tanh"]),
- },
- )
- results = tuner.fit()
- print("Best hyperparameters found were: ", results.get_best_result().config)
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