mirror of
https://github.com/wassname/ray.git
synced 2026-07-04 11:03:54 +08:00
Kai Fricke
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
#!/usr/bin/env python
|
|
|
|
import json
|
|
import os
|
|
|
|
import numpy as np
|
|
|
|
import ray
|
|
from ray import tune
|
|
from ray.tune import Trainable
|
|
from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
|
|
from ray.tune.suggest.bohb import TuneBOHB
|
|
|
|
|
|
class MyTrainableClass(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)
|
|
|
|
# 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__":
|
|
import ConfigSpace as CS # noqa: F401
|
|
ray.init(num_cpus=8)
|
|
|
|
config = {
|
|
"iterations": 100,
|
|
"width": tune.uniform(0, 20),
|
|
"height": tune.uniform(-100, 100),
|
|
"activation": tune.choice(["relu", "tanh"])
|
|
}
|
|
|
|
# Optional: Pass the parameter space yourself
|
|
# config_space = CS.ConfigurationSpace()
|
|
# config_space.add_hyperparameter(
|
|
# CS.UniformFloatHyperparameter("width", lower=0, upper=20))
|
|
# config_space.add_hyperparameter(
|
|
# CS.UniformFloatHyperparameter("height", lower=-100, upper=100))
|
|
# config_space.add_hyperparameter(
|
|
# CS.CategoricalHyperparameter(
|
|
# "activation", choices=["relu", "tanh"]))
|
|
|
|
experiment_metrics = dict(metric="episode_reward_mean", mode="max")
|
|
|
|
bohb_hyperband = HyperBandForBOHB(
|
|
time_attr="training_iteration",
|
|
max_t=100,
|
|
reduction_factor=4,
|
|
**experiment_metrics)
|
|
|
|
bohb_search = TuneBOHB(
|
|
# space=config_space, # If you want to set the space manually
|
|
max_concurrent=4,
|
|
**experiment_metrics)
|
|
|
|
tune.run(
|
|
MyTrainableClass,
|
|
name="bohb_test",
|
|
config=config,
|
|
scheduler=bohb_hyperband,
|
|
search_alg=bohb_search,
|
|
num_samples=10,
|
|
stop={"training_iteration": 100})
|