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b7a010e657
* ray version check when importing * display learner_class when starting_points removed * test ray 2
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import time
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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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from ray import tune
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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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def test_blendsearch_tune(smoke_test=True):
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try:
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from ray import tune
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from ray.tune.schedulers import AsyncHyperBandScheduler
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from ray import __version__ as ray_version
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if ray_version.startswith("1."):
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from ray.tune.suggest import ConcurrencyLimiter
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from ray.tune.suggest.flaml import BlendSearch
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else:
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from ray.tune.search import ConcurrencyLimiter
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from ray.tune.search.flaml import BlendSearch
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except ImportError:
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print("ray[tune] is not installed, skipping test")
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return
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import numpy as np
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algo = BlendSearch()
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algo = ConcurrencyLimiter(algo, max_concurrent=4)
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scheduler = AsyncHyperBandScheduler()
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analysis = tune.run(
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easy_objective,
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metric="mean_loss",
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mode="min",
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search_alg=algo,
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scheduler=scheduler,
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num_samples=10 if smoke_test else 100,
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config={
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"steps": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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# This is an ignored parameter.
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"activation": tune.choice(["relu", "tanh"]),
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"test4": np.zeros((3, 1)),
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},
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)
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print("Best hyperparameters found were: ", analysis.best_config)
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if __name__ == "__main__":
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test_blendsearch_tune(False)
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