wehub-resource-sync
109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
#!/usr/bin/env python
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"""This example demonstrates the usage of BOHB with Ray Tune.
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Requires the HpBandSter and ConfigSpace libraries to be installed
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(`pip install hpbandster ConfigSpace`).
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"""
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import json
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import os
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import time
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import numpy as np
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import ray
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from ray import tune
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from ray.tune import Trainable
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from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
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from ray.tune.search.bohb import TuneBOHB
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class MyTrainableClass(Trainable):
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"""Example agent whose learning curve is a random sigmoid.
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The dummy hyperparameters "width" and "height" determine the slope and
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maximum reward value reached.
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"""
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def setup(self, config):
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self.timestep = 0
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def step(self):
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self.timestep += 1
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v = np.tanh(float(self.timestep) / self.config.get("width", 1))
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v *= self.config.get("height", 1)
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time.sleep(0.1)
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# Here we use `episode_reward_mean`, but you can also report other
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# objectives such as loss or accuracy.
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return {"episode_reward_mean": v}
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def save_checkpoint(self, checkpoint_dir):
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path = os.path.join(checkpoint_dir, "checkpoint")
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with open(path, "w") as f:
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f.write(json.dumps({"timestep": self.timestep}))
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def load_checkpoint(self, checkpoint_dir):
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path = os.path.join(checkpoint_dir, "checkpoint")
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with open(path, "r") as f:
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self.timestep = json.loads(f.read())["timestep"]
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if __name__ == "__main__":
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import sys
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if sys.version_info >= (3, 12):
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# TuneBOHB is not compatible with Python 3.12
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sys.exit(0)
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ray.init(num_cpus=8)
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config = {
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"iterations": 100,
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"width": tune.uniform(0, 20),
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"height": tune.uniform(-100, 100),
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"activation": tune.choice(["relu", "tanh"]),
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}
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# Optional: Pass the parameter space yourself
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# import ConfigSpace as CS
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# config_space = CS.ConfigurationSpace()
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# config_space.add_hyperparameter(
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# CS.UniformFloatHyperparameter("width", lower=0, upper=20))
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# config_space.add_hyperparameter(
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# CS.UniformFloatHyperparameter("height", lower=-100, upper=100))
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# config_space.add_hyperparameter(
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# CS.CategoricalHyperparameter(
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# "activation", choices=["relu", "tanh"]))
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max_iterations = 10
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bohb_hyperband = HyperBandForBOHB(
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time_attr="training_iteration",
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max_t=max_iterations,
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reduction_factor=2,
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stop_last_trials=False,
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)
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bohb_search = TuneBOHB(
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# space=config_space, # If you want to set the space manually
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)
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bohb_search = tune.search.ConcurrencyLimiter(bohb_search, max_concurrent=4)
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tuner = tune.Tuner(
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MyTrainableClass,
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run_config=tune.RunConfig(
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name="bohb_test", stop={"training_iteration": max_iterations}
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),
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tune_config=tune.TuneConfig(
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metric="episode_reward_mean",
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mode="max",
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scheduler=bohb_hyperband,
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search_alg=bohb_search,
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num_samples=32,
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),
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param_space=config,
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)
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results = tuner.fit()
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print("Best hyperparameters found were: ", results.get_best_result().config)
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