"""This example demonstrates the usage of Optuna with Ray Tune.
It also checks that it is usable with a separate scheduler.
"""
import time
import ray
from ray import tune
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.optuna import OptunaSearch
def evaluation_fn(step, width, height):
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)
time.sleep(0.1)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(configure_logging=False)
algo = OptunaSearch()
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(
easy_objective,
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if args.smoke_test else 100,
config={
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
# This is an ignored parameter.
"activation": tune.choice(["relu", "tanh"])
})
print("Best hyperparameters found were: ", analysis.best_config)
