wehub-resource-sync
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
#!/usr/bin/env python
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import argparse
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import time
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from typing import Any, Dict
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from ray import tune
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from ray.tune.schedulers import AsyncHyperBandScheduler
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def evaluation_fn(step, width, height) -> float:
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# simulate model evaluation
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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: Dict[str, Any]) -> None:
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# Config contains the hyperparameters to tune
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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 an 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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parser = argparse.ArgumentParser(description="AsyncHyperBand optimization example")
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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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# AsyncHyperBand enables aggressive early stopping of poorly performing trials
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scheduler = AsyncHyperBandScheduler(
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grace_period=5, # Minimum training iterations before stopping
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max_t=100, # Maximum training iterations
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)
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tuner = tune.Tuner(
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tune.with_resources(easy_objective, {"cpu": 1, "gpu": 0}),
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run_config=tune.RunConfig(
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name="asynchyperband_test",
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stop={"training_iteration": 1 if args.smoke_test else 9999},
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verbose=1,
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),
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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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scheduler=scheduler,
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num_samples=20, # Number of trials to run
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),
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param_space={
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"steps": 100,
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"width": tune.uniform(10, 100),
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"height": tune.uniform(0, 100),
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},
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)
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# Run the hyperparameter optimization
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results = tuner.fit()
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print(f"Best hyperparameters found: {results.get_best_result().config}")
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