mirror of
https://github.com/wassname/ray.git
synced 2026-07-03 15:44:37 +08:00
Richard Liaw
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""This test checks that Nevergrad 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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import time
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import ray
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from ray import tune
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from ray.tune.schedulers import AsyncHyperBandScheduler
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from ray.tune.suggest.nevergrad import NevergradSearch
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def evaluation_fn(step, width, height):
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return (0.1 + width * step / 100)**(-1) + height * 0.1
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def easy_objective(config):
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# Hyperparameters
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width, height = config["width"], config["height"]
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for step in range(config["steps"]):
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# Iterative training function - can be any arbitrary training procedure
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intermediate_score = evaluation_fn(step, width, height)
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# Feed the score back back to Tune.
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tune.report(iterations=step, mean_loss=intermediate_score)
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time.sleep(0.1)
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if __name__ == "__main__":
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import argparse
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from nevergrad.optimization import optimizerlib
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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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config = {
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"num_samples": 10 if args.smoke_test else 50,
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"config": {
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"steps": 100,
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}
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}
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instrumentation = 2
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parameter_names = ["height", "width"]
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# With nevergrad v0.2.0+ the following is also possible:
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# from nevergrad import instrumentation as inst
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# instrumentation = inst.Instrumentation(
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# height=inst.var.Array(1).bounded(0, 200).asfloat(),
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# width=inst.var.OrderedDiscrete([0, 10, 20, 30, 40, 50]))
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# parameter_names = None # names are provided by the instrumentation
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optimizer = optimizerlib.OnePlusOne(instrumentation)
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algo = NevergradSearch(
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optimizer, parameter_names, metric="mean_loss", mode="min")
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scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
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tune.run(
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easy_objective,
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name="nevergrad",
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search_alg=algo,
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scheduler=scheduler,
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**config)
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