#!/usr/bin/env python
import argparse
import time
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
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 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()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing"
)
parser.add_argument(
"--ray-address",
help="Address of Ray cluster for seamless distributed execution.",
required=False,
)
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 is not None:
ray.init(f"ray://{args.server_address}")
else:
ray.init(address=args.ray_address)
# AsyncHyperBand enables aggressive early stopping of bad trials.
scheduler = AsyncHyperBandScheduler(grace_period=5, max_t=100)
# 'training_iteration' is incremented every time `trainable.step` is called
stopping_criteria = {"training_iteration": 1 if args.smoke_test else 9999}
analysis = tune.run(
easy_objective,
name="asynchyperband_test",
metric="mean_loss",
mode="min",
scheduler=scheduler,
stop=stopping_criteria,
num_samples=20,
verbose=1,
resources_per_trial={"cpu": 1, "gpu": 0},
config={ # Hyperparameter space
"steps": 100,
"width": tune.uniform(10, 100),
"height": tune.uniform(0, 100),
},
)
print("Best hyperparameters found were: ", analysis.best_config)
