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- #!/usr/bin/env python
- import argparse
- import time
- from typing import Any, Dict
- from ray import tune
- from ray.tune.schedulers import AsyncHyperBandScheduler
- def evaluation_fn(step, width, height) -> float:
- # simulate model evaluation
- time.sleep(0.1)
- return (0.1 + width * step / 100) ** (-1) + height * 0.1
- def easy_objective(config: Dict[str, Any]) -> None:
- # Config contains the hyperparameters to tune
- width, height = config["width"], config["height"]
- for step in range(config["steps"]):
- # Iterative training function - can be an 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__":
- parser = argparse.ArgumentParser(description="AsyncHyperBand optimization example")
- parser.add_argument(
- "--smoke-test", action="store_true", help="Finish quickly for testing"
- )
- args, _ = parser.parse_known_args()
- # AsyncHyperBand enables aggressive early stopping of poorly performing trials
- scheduler = AsyncHyperBandScheduler(
- grace_period=5, # Minimum training iterations before stopping
- max_t=100, # Maximum training iterations
- )
- tuner = tune.Tuner(
- tune.with_resources(easy_objective, {"cpu": 1, "gpu": 0}),
- run_config=tune.RunConfig(
- name="asynchyperband_test",
- stop={"training_iteration": 1 if args.smoke_test else 9999},
- verbose=1,
- ),
- tune_config=tune.TuneConfig(
- metric="mean_loss",
- mode="min",
- scheduler=scheduler,
- num_samples=20, # Number of trials to run
- ),
- param_space={
- "steps": 100,
- "width": tune.uniform(10, 100),
- "height": tune.uniform(0, 100),
- },
- )
- # Run the hyperparameter optimization
- results = tuner.fit()
- print(f"Best hyperparameters found: {results.get_best_result().config}")
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