"""This example demonstrates the usage of SigOpt with Ray Tune.
It also checks that it is usable with a separate scheduler.
Requires the SigOpt library to be installed (`pip install sigopt`).
"""
import sys
import time
from ray import train, tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search.sigopt import SigOptSearch
from ray.tune.search.sigopt.sigopt_search import load_sigopt_key
def evaluate(step, width, height):
return (0.1 + width * step / 100) ** (-1) + height * 0.01
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 = evaluate(step, width, height)
# Feed the score back back to Tune.
train.report({"iterations": step, "mean_loss": intermediate_score})
time.sleep(0.1)
if __name__ == "__main__":
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing"
)
args, _ = parser.parse_known_args()
load_sigopt_key()
if "SIGOPT_KEY" not in os.environ:
if args.smoke_test:
print("SigOpt API Key not found. Skipping smoke test.")
sys.exit(0)
else:
raise ValueError(
"SigOpt API Key not found. Please set the SIGOPT_KEY "
"environment variable."
)
space = [
{
"name": "width",
"type": "int",
"bounds": {"min": 0, "max": 20},
},
{
"name": "height",
"type": "int",
"bounds": {"min": -100, "max": 100},
},
]
algo = SigOptSearch(
space,
name="SigOpt Example Experiment",
metric="mean_loss",
mode="min",
)
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tuner = tune.Tuner(
easy_objective,
run_config=train.RunConfig(
name="my_exp",
),
tune_config=tune.TuneConfig(
search_alg=algo,
scheduler=scheduler,
num_samples=4 if args.smoke_test else 100,
),
param_space={"steps": 10},
)
results = tuner.fit()
print(
"Best hyperparameters found were: ",
results.get_best_result("mean_loss", "min").config,
)