#!/usr/bin/env python
import argparse
import json
import os
import time
import numpy as np
import ray
from ray import tune
from ray.tune.schedulers import HyperBandScheduler
class MyTrainableClass(tune.Trainable):
"""Example agent whose learning curve is a random sigmoid.
The dummy hyperparameters "width" and "height" determine the slope and
maximum reward value reached.
"""
def setup(self, config):
self.timestep = 0
def step(self):
self.timestep += 1
v = np.tanh(float(self.timestep) / self.config.get("width", 1))
v *= self.config.get("height", 1)
time.sleep(0.1)
# Here we use `episode_reward_mean`, but you can also report other
# objectives such as loss or accuracy.
return {"episode_reward_mean": v}
def save_checkpoint(self, checkpoint_dir):
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"timestep": self.timestep}))
return path
def load_checkpoint(self, checkpoint_path):
with open(checkpoint_path) as f:
self.timestep = json.loads(f.read())["timestep"]
if __name__ == "__main__":
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:
ray.init(f"ray://{args.server_address}")
else:
ray.init(num_cpus=4 if args.smoke_test else None)
# Hyperband early stopping, configured with `episode_reward_mean` as the
# objective and `training_iteration` as the time unit,
# which is automatically filled by Tune.
hyperband = HyperBandScheduler(time_attr="training_iteration", max_t=200)
analysis = tune.run(
MyTrainableClass,
name="hyperband_test",
num_samples=20 if args.smoke_test else 200,
metric="episode_reward_mean",
mode="max",
stop={"training_iteration": 1 if args.smoke_test else 200},
config={
"width": tune.randint(10, 90),
"height": tune.randint(0, 100)
},
verbose=1,
scheduler=hyperband,
fail_fast=True)
print("Best hyperparameters found were: ", analysis.best_config)
