chore: import upstream snapshot with attribution

python/ray/tune/examples/hyperband_example.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+
+import argparse
+
+import ray
+from ray import tune
+from ray.tune.schedulers import HyperBandScheduler
+from ray.tune.utils.mock_trainable import MyTrainableClass
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    ray.init(num_cpus=4 if args.smoke_test else None)
+
+    # Hyperband early stopping, configured with `episode_reward_mean` as the
+    # objective and `training_iteration` as the time unit,
+    # which is automatically filled by Tune.
+    hyperband = HyperBandScheduler(time_attr="training_iteration", max_t=200)
+
+    tuner = tune.Tuner(
+        MyTrainableClass,
+        run_config=tune.RunConfig(
+            name="hyperband_test",
+            stop={"training_iteration": 1 if args.smoke_test else 200},
+            verbose=1,
+            failure_config=tune.FailureConfig(
+                fail_fast=True,
+            ),
+        ),
+        tune_config=tune.TuneConfig(
+            num_samples=20 if args.smoke_test else 200,
+            metric="episode_reward_mean",
+            mode="max",
+            scheduler=hyperband,
+        ),
+        param_space={"width": tune.randint(10, 90), "height": tune.randint(0, 100)},
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)