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https://github.com/wassname/ray.git
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Richard Liaw
70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
#!/usr/bin/env python
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import json
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import os
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import numpy as np
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import ray
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from ray.tune import Trainable, run
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from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
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from ray.tune.suggest.bohb import TuneBOHB
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class MyTrainableClass(Trainable):
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"""Example agent whose learning curve is a random sigmoid.
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The dummy hyperparameters "width" and "height" determine the slope and
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maximum reward value reached.
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"""
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def _setup(self, config):
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self.timestep = 0
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def _train(self):
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self.timestep += 1
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v = np.tanh(float(self.timestep) / self.config.get("width", 1))
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v *= self.config.get("height", 1)
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# Here we use `episode_reward_mean`, but you can also report other
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# objectives such as loss or accuracy.
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return {"episode_reward_mean": v}
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def _save(self, checkpoint_dir):
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path = os.path.join(checkpoint_dir, "checkpoint")
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with open(path, "w") as f:
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f.write(json.dumps({"timestep": self.timestep}))
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return path
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def _restore(self, checkpoint_path):
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with open(checkpoint_path) as f:
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self.timestep = json.loads(f.read())["timestep"]
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if __name__ == "__main__":
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import ConfigSpace as CS
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ray.init(num_cpus=8)
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# BOHB uses ConfigSpace for their hyperparameter search space
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config_space = CS.ConfigurationSpace()
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config_space.add_hyperparameter(
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CS.UniformFloatHyperparameter("height", lower=10, upper=100))
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config_space.add_hyperparameter(
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CS.UniformFloatHyperparameter("width", lower=0, upper=100))
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experiment_metrics = dict(metric="episode_reward_mean", mode="max")
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bohb_hyperband = HyperBandForBOHB(
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time_attr="training_iteration",
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max_t=100,
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reduction_factor=4,
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**experiment_metrics)
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bohb_search = TuneBOHB(
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config_space, max_concurrent=4, **experiment_metrics)
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run(MyTrainableClass,
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name="bohb_test",
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scheduler=bohb_hyperband,
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search_alg=bohb_search,
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num_samples=10,
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stop={"training_iteration": 100})
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