chore: import upstream snapshot with attribution

python/ray/tune/examples/async_hyperband_example.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+
+import argparse
+import time
+from typing import Any, Dict
+
+from ray import tune
+from ray.tune.schedulers import AsyncHyperBandScheduler
+
+
+def evaluation_fn(step, width, height) -> float:
+    # simulate model evaluation
+    time.sleep(0.1)
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1
+
+
+def easy_objective(config: Dict[str, Any]) -> None:
+    # Config contains the hyperparameters to tune
+    width, height = config["width"], config["height"]
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be an arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Feed the score back back to Tune.
+        tune.report({"iterations": step, "mean_loss": intermediate_score})
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="AsyncHyperBand optimization example")
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    # AsyncHyperBand enables aggressive early stopping of poorly performing trials
+    scheduler = AsyncHyperBandScheduler(
+        grace_period=5,  # Minimum training iterations before stopping
+        max_t=100,  # Maximum training iterations
+    )
+
+    tuner = tune.Tuner(
+        tune.with_resources(easy_objective, {"cpu": 1, "gpu": 0}),
+        run_config=tune.RunConfig(
+            name="asynchyperband_test",
+            stop={"training_iteration": 1 if args.smoke_test else 9999},
+            verbose=1,
+        ),
+        tune_config=tune.TuneConfig(
+            metric="mean_loss",
+            mode="min",
+            scheduler=scheduler,
+            num_samples=20,  # Number of trials to run
+        ),
+        param_space={
+            "steps": 100,
+            "width": tune.uniform(10, 100),
+            "height": tune.uniform(0, 100),
+        },
+    )
+
+    # Run the hyperparameter optimization
+    results = tuner.fit()
+    print(f"Best hyperparameters found: {results.get_best_result().config}")