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Richard Liaw
75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""This example demonstrates the usage of ZOOptSearch.
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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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from ray import tune
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from ray.tune.suggest.zoopt import ZOOptSearch
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from ray.tune.schedulers import AsyncHyperBandScheduler
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from zoopt import ValueType # noqa: F401
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def evaluation_fn(step, width, height):
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time.sleep(0.1)
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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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if __name__ == "__main__":
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import argparse
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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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num_samples = 10 if args.smoke_test else 1000
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# Optional: Pass the parameter space yourself
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# space = {
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# # for continuous dimensions: (continuous, search_range, precision)
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# "height": (ValueType.CONTINUOUS, [-10, 10], 1e-2),
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# # for discrete dimensions: (discrete, search_range, has_order)
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# "width": (ValueType.DISCRETE, [0, 10], True)
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# # for grid dimensions: (grid, grid_list)
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# "layers": (ValueType.GRID, [4, 8, 16])
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# }
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zoopt_search_config = {
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"parallel_num": 8,
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}
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zoopt_search = ZOOptSearch(
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algo="Asracos", # only support ASRacos currently
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budget=num_samples,
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# dim_dict=space, # If you want to set the space yourself
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**zoopt_search_config)
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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=zoopt_search,
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name="zoopt_search",
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scheduler=scheduler,
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num_samples=num_samples,
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config={
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"steps": 10,
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"height": tune.quniform(-10, 10, 1e-2),
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"width": tune.randint(0, 10)
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})
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print("Best config found: ", analysis.best_config)
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