wehub-resource-sync
62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
# ruff: noqa
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# This is an example quickstart for Tune.
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# To connect to a cluster, uncomment below:
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# import ray
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# import argparse
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# parser = argparse.ArgumentParser()
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# parser.add_argument("--address")
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# args = parser.parse_args()
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# ray.init(address=args.address)
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# __quick_start_begin__
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from ray import tune
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def objective(config): # <1>
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score = config["a"] ** 2 + config["b"]
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return {"score": score}
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search_space = { # <2>
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"a": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
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"b": tune.choice([1, 2, 3]),
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}
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tuner = tune.Tuner(objective, param_space=search_space) # <3>
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results = tuner.fit()
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print(results.get_best_result(metric="score", mode="min").config)
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# __quick_start_end__
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# __ml_quick_start_begin__
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def objective(step, alpha, beta):
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return (0.1 + alpha * step / 100) ** (-1) + beta * 0.1
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def training_function(config):
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# Hyperparameters
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alpha, beta = config["alpha"], config["beta"]
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for step in range(10):
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# Iterative training function - can be any arbitrary training procedure.
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intermediate_score = objective(step, alpha, beta)
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# Feed the score back back to Tune.
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tune.report({"mean_loss": intermediate_score})
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tuner = tune.Tuner(
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training_function,
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param_space={
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"alpha": tune.grid_search([0.001, 0.01, 0.1]),
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"beta": tune.choice([1, 2, 3]),
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},
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)
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results = tuner.fit()
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print("Best config: ", results.get_best_result(metric="mean_loss", mode="min").config)
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# Get a dataframe for analyzing trial results.
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df = results.get_dataframe()
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# __ml_quick_start_end__
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