mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 06:47:13 +08:00
Richard Liaw
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
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 ray
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from ray.tune import run_experiments, register_trainable
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from ray.tune.hpo_scheduler import HyperOptScheduler
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def easy_objective(config, reporter):
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import time
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time.sleep(0.2)
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reporter(
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timesteps_total=1,
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episode_reward_mean=-((config["height"]-14) ** 2
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+ abs(config["width"]-3)))
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time.sleep(0.2)
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if __name__ == '__main__':
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import argparse
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from hyperopt import hp
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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(redirect_output=True)
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register_trainable("exp", easy_objective)
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space = {
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'width': hp.uniform('width', 0, 20),
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'height': hp.uniform('height', -100, 100),
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}
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config = {"my_exp": {
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"run": "exp",
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"repeat": 5 if args.smoke_test else 1000,
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"stop": {"training_iteration": 1},
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"config": {
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"space": space}}}
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hpo_sched = HyperOptScheduler()
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run_experiments(config, verbose=False, scheduler=hpo_sched)
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