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- #!/usr/bin/env python
- import argparse
- import time
- from ray import tune
- from ray.tune.logger import LoggerCallback
- class TestLoggerCallback(LoggerCallback):
- def on_trial_result(self, iteration, trials, trial, result, **info):
- print(f"TestLogger for trial {trial}: {result}")
- def trial_str_creator(trial):
- return "{}_{}_123".format(trial.trainable_name, trial.trial_id)
- 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__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--smoke-test", action="store_true", help="Finish quickly for testing"
- )
- args, _ = parser.parse_known_args()
- tuner = tune.Tuner(
- easy_objective,
- run_config=tune.RunConfig(
- name="hyperband_test",
- callbacks=[TestLoggerCallback()],
- stop={"training_iteration": 1 if args.smoke_test else 100},
- ),
- tune_config=tune.TuneConfig(
- metric="mean_loss",
- mode="min",
- num_samples=5,
- trial_name_creator=trial_str_creator,
- trial_dirname_creator=trial_str_creator,
- ),
- param_space={
- "steps": 100,
- "width": tune.randint(10, 100),
- "height": tune.loguniform(10, 100),
- },
- )
- results = tuner.fit()
- print("Best hyperparameters: ", results.get_best_result().config)
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