"""This test checks that Nevergrad is functional.
It also checks that it is usable with a separate scheduler.
"""
import time
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.nevergrad import NevergradSearch
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
from nevergrad.optimization import optimizerlib
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init()
config = {
"num_samples": 10 if args.smoke_test else 50,
"config": {
"steps": 100,
}
}
instrumentation = 2
parameter_names = ["height", "width"]
# With nevergrad v0.2.0+ the following is also possible:
# from nevergrad import instrumentation as inst
# instrumentation = inst.Instrumentation(
# height=inst.var.Array(1).bounded(0, 200).asfloat(),
# width=inst.var.OrderedDiscrete([0, 10, 20, 30, 40, 50]))
# parameter_names = None # names are provided by the instrumentation
optimizer = optimizerlib.OnePlusOne(instrumentation)
algo = NevergradSearch(
optimizer, parameter_names, metric="mean_loss", mode="min")
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tune.run(
easy_objective,
name="nevergrad",
search_alg=algo,
scheduler=scheduler,
**config)