"""This example demonstrates the usage of BayesOpt with Ray Tune.
It also checks that it is usable with a separate scheduler.
"""
import time
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.suggest.bayesopt import BayesOptSearch
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")
parser.add_argument(
"--server-address",
type=str,
default=None,
required=False,
help="The address of server to connect to if using "
"Ray Client.")
args, _ = parser.parse_known_args()
if args.server_address:
import ray
ray.util.connect(args.server_address)
algo = BayesOptSearch(utility_kwargs={
"kind": "ucb",
"kappa": 2.5,
"xi": 0.0
})
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(
easy_objective,
name="my_exp",
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if args.smoke_test else 1000,
config={
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100)
})
print("Best hyperparameters found were: ", analysis.best_config)
