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https://github.com/wassname/ray.git
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Richard Liaw
ea5a6f8455
Uses `tune.run` to execute experiments as preferred API. @noahgolmant This does not break backwards compat, but will slowly internalize `Experiment`. In a separate PR, Tune schedulers should only support 1 running experiment at a time.
78 lines
1.9 KiB
Python
78 lines
1.9 KiB
Python
"""This test checks that SigOpt is functional.
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It also checks that it is usable with a separate scheduler.
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"""
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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 ray
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from ray.tune import run
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from ray.tune.schedulers import AsyncHyperBandScheduler
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from ray.tune.suggest import SigOptSearch
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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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for i in range(config["iterations"]):
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reporter(
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timesteps_total=i,
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neg_mean_loss=-(config["height"] - 14)**2 +
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abs(config["width"] - 3))
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time.sleep(0.02)
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if __name__ == "__main__":
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import argparse
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import os
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assert "SIGOPT_KEY" in os.environ, \
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"SigOpt API key must be stored as environment variable at SIGOPT_KEY"
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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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space = [
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{
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'name': 'width',
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'type': 'int',
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'bounds': {
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'min': 0,
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'max': 20
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},
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},
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{
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'name': 'height',
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'type': 'int',
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'bounds': {
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'min': -100,
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'max': 100
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},
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},
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]
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config = {
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"num_samples": 10 if args.smoke_test else 1000,
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"config": {
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"iterations": 100,
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},
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"stop": {
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"timesteps_total": 100
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},
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}
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algo = SigOptSearch(
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space,
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name="SigOpt Example Experiment",
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max_concurrent=1,
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reward_attr="neg_mean_loss")
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scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
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run(easy_objective,
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name="my_exp",
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search_alg=algo,
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scheduler=scheduler,
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**config)
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