wehub-resource-sync
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
"""This example demonstrates basic Ray Tune random search and grid search."""
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import time
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import ray
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from ray import tune
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def evaluation_fn(step, width, height):
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time.sleep(0.1)
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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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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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# This will do a grid search over the `activation` parameter. This means
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# that each of the two values (`relu` and `tanh`) will be sampled once
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# for each sample (`num_samples`). We end up with 2 * 50 = 100 samples.
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# The `width` and `height` parameters are sampled randomly.
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# `steps` is a constant parameter.
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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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num_samples=5 if args.smoke_test else 50,
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),
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param_space={
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"steps": 5 if args.smoke_test else 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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"activation": tune.grid_search(["relu", "tanh"]),
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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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