wehub-resource-sync
45 lines
1.4 KiB
Python
Executable File
45 lines
1.4 KiB
Python
Executable File
#!/usr/bin/env python
|
|
|
|
import argparse
|
|
|
|
import ray
|
|
from ray import tune
|
|
from ray.tune.schedulers import HyperBandScheduler
|
|
from ray.tune.utils.mock_trainable import MyTrainableClass
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--smoke-test", action="store_true", help="Finish quickly for testing"
|
|
)
|
|
args, _ = parser.parse_known_args()
|
|
|
|
ray.init(num_cpus=4 if args.smoke_test else None)
|
|
|
|
# Hyperband early stopping, configured with `episode_reward_mean` as the
|
|
# objective and `training_iteration` as the time unit,
|
|
# which is automatically filled by Tune.
|
|
hyperband = HyperBandScheduler(time_attr="training_iteration", max_t=200)
|
|
|
|
tuner = tune.Tuner(
|
|
MyTrainableClass,
|
|
run_config=tune.RunConfig(
|
|
name="hyperband_test",
|
|
stop={"training_iteration": 1 if args.smoke_test else 200},
|
|
verbose=1,
|
|
failure_config=tune.FailureConfig(
|
|
fail_fast=True,
|
|
),
|
|
),
|
|
tune_config=tune.TuneConfig(
|
|
num_samples=20 if args.smoke_test else 200,
|
|
metric="episode_reward_mean",
|
|
mode="max",
|
|
scheduler=hyperband,
|
|
),
|
|
param_space={"width": tune.randint(10, 90), "height": tune.randint(0, 100)},
|
|
)
|
|
results = tuner.fit()
|
|
|
|
print("Best hyperparameters found were: ", results.get_best_result().config)
|