"""This example demonstrates the usage of BlendSearch with Ray Tune.
It also checks that it is usable with a separate scheduler.
Requires the FLAML library to be installed (`pip install flaml`).
"""
import time
import ray
from ray import train, tune
from ray.tune.search import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search.flaml import BlendSearch
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 to Tune.
train.report({"iterations": step, "mean_loss": intermediate_score})
time.sleep(0.1)
def run_blendsearch_tune(smoke_test=False):
algo = BlendSearch()
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
tuner = tune.Tuner(
easy_objective,
tune_config=tune.TuneConfig(
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if smoke_test else 100,
),
param_space={
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
# This is an ignored parameter.
"activation": tune.choice(["relu", "tanh"]),
},
)
results = tuner.fit()
print("Best hyperparameters found were: ", results.get_best_result().config)
def run_blendsearch_tune_w_budget(time_budget_s=10):
"""run BlendSearch with given time_budget_s"""
algo = BlendSearch(
metric="mean_loss",
mode="min",
space={
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"]),
},
)
algo.set_search_properties(config={"time_budget_s": time_budget_s})
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
tuner = tune.Tuner(
easy_objective,
tune_config=tune.TuneConfig(
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
time_budget_s=time_budget_s,
num_samples=-1,
),
param_space={
"steps": 100,
},
)
results = tuner.fit()
print("Best hyperparameters found were: ", results.get_best_result().config)
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)
run_blendsearch_tune_w_budget(time_budget_s=30)
run_blendsearch_tune(smoke_test=args.smoke_test)