chore: import upstream snapshot with attribution

python/ray/tune/examples/bohb_example.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+
+"""This example demonstrates the usage of BOHB with Ray Tune.
+
+Requires the HpBandSter and ConfigSpace libraries to be installed
+(`pip install hpbandster ConfigSpace`).
+"""
+
+import json
+import os
+import time
+
+import numpy as np
+
+import ray
+from ray import tune
+from ray.tune import Trainable
+from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
+from ray.tune.search.bohb import TuneBOHB
+
+
+class MyTrainableClass(Trainable):
+    """Example agent whose learning curve is a random sigmoid.
+
+    The dummy hyperparameters "width" and "height" determine the slope and
+    maximum reward value reached.
+    """
+
+    def setup(self, config):
+        self.timestep = 0
+
+    def step(self):
+        self.timestep += 1
+        v = np.tanh(float(self.timestep) / self.config.get("width", 1))
+        v *= self.config.get("height", 1)
+        time.sleep(0.1)
+        # Here we use `episode_reward_mean`, but you can also report other
+        # objectives such as loss or accuracy.
+        return {"episode_reward_mean": v}
+
+    def save_checkpoint(self, checkpoint_dir):
+        path = os.path.join(checkpoint_dir, "checkpoint")
+        with open(path, "w") as f:
+            f.write(json.dumps({"timestep": self.timestep}))
+
+    def load_checkpoint(self, checkpoint_dir):
+        path = os.path.join(checkpoint_dir, "checkpoint")
+        with open(path, "r") as f:
+            self.timestep = json.loads(f.read())["timestep"]
+
+
+if __name__ == "__main__":
+    import sys
+
+    if sys.version_info >= (3, 12):
+        # TuneBOHB is not compatible with Python 3.12
+        sys.exit(0)
+
+    ray.init(num_cpus=8)
+
+    config = {
+        "iterations": 100,
+        "width": tune.uniform(0, 20),
+        "height": tune.uniform(-100, 100),
+        "activation": tune.choice(["relu", "tanh"]),
+    }
+
+    # Optional: Pass the parameter space yourself
+    # import ConfigSpace as CS
+    # config_space = CS.ConfigurationSpace()
+    # config_space.add_hyperparameter(
+    #     CS.UniformFloatHyperparameter("width", lower=0, upper=20))
+    # config_space.add_hyperparameter(
+    #     CS.UniformFloatHyperparameter("height", lower=-100, upper=100))
+    # config_space.add_hyperparameter(
+    #     CS.CategoricalHyperparameter(
+    #         "activation", choices=["relu", "tanh"]))
+
+    max_iterations = 10
+    bohb_hyperband = HyperBandForBOHB(
+        time_attr="training_iteration",
+        max_t=max_iterations,
+        reduction_factor=2,
+        stop_last_trials=False,
+    )
+
+    bohb_search = TuneBOHB(
+        # space=config_space,  # If you want to set the space manually
+    )
+    bohb_search = tune.search.ConcurrencyLimiter(bohb_search, max_concurrent=4)
+
+    tuner = tune.Tuner(
+        MyTrainableClass,
+        run_config=tune.RunConfig(
+            name="bohb_test", stop={"training_iteration": max_iterations}
+        ),
+        tune_config=tune.TuneConfig(
+            metric="episode_reward_mean",
+            mode="max",
+            scheduler=bohb_hyperband,
+            search_alg=bohb_search,
+            num_samples=32,
+        ),
+        param_space=config,
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)