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Eric Liang
72595cca0d
* update * update examples * Wed Mar 21 15:19:56 PDT 2018 * Wed Mar 21 15:21:32 PDT 2018 * Update train_a3c.py * Update train.py * fix resources accounting
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
#!/usr/bin/env python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import json
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import os
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import random
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import numpy as np
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import ray
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from ray.tune import Trainable, TrainingResult, register_trainable, \
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run_experiments
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from ray.tune.async_hyperband import AsyncHyperBandScheduler
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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):
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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["width"])
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v *= self.config["height"]
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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 (see tune/result.py).
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return TrainingResult(episode_reward_mean=v, timesteps_this_iter=1)
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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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register_trainable("my_class", MyTrainableClass)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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ray.init()
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# asynchronous hyperband early stopping, configured with
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# `episode_reward_mean` as the
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# objective and `timesteps_total` as the time unit.
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ahb = AsyncHyperBandScheduler(
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time_attr="timesteps_total", reward_attr="episode_reward_mean",
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grace_period=5, max_t=100)
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run_experiments({
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"asynchyperband_test": {
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"run": "my_class",
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"stop": {"training_iteration": 1 if args.smoke_test else 99999},
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"repeat": 20,
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"trial_resources": {"cpu": 1, "gpu": 0},
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"config": {
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"width": lambda spec: 10 + int(90 * random.random()),
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"height": lambda spec: int(100 * random.random()),
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},
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}
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}, scheduler=ahb)
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