chore: import upstream snapshot with attribution

python/ray/tune/examples/optuna_define_by_run_example.py

@@ -0,0 +1,93 @@
+"""This example demonstrates the usage of Optuna define-by-run with Ray Tune.
+
+It also checks that it is usable with a separate scheduler.
+
+Requires the Optuna library to be installed (`pip install optuna`).
+
+For an example of using a Tune search space, see
+:doc:`/tune/examples/optuna_example`.
+"""
+
+import time
+from typing import Any, Dict, Optional
+
+import ray
+from ray import tune
+from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.search import ConcurrencyLimiter
+from ray.tune.search.optuna import OptunaSearch
+
+
+def evaluation_fn(step, width, height, mult=1):
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1 * mult
+
+
+def easy_objective(config):
+    # Hyperparameters
+    width, height, mult = config["width"], config["height"], config.get("mult", 1)
+    print(config)
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height, mult)
+        # Feed the score back back to Tune.
+        tune.report({"iterations": step, "mean_loss": intermediate_score})
+        time.sleep(0.1)
+
+
+def define_by_run_func(trial) -> Optional[Dict[str, Any]]:
+    """Define-by-run function to create the search space.
+
+    Ensure no actual computation takes place here. That should go into
+    the trainable passed to ``Tuner`` (in this example, that's
+    ``easy_objective``).
+
+    For more information, see https://optuna.readthedocs.io/en/stable\
+/tutorial/10_key_features/002_configurations.html
+
+    This function should either return None or a dict with constant values.
+    """
+    # This param is not used in the objective function.
+    activation = trial.suggest_categorical("activation", ["relu", "tanh"])
+    trial.suggest_float("width", 0, 20)
+    trial.suggest_float("height", -100, 100)
+
+    # Define-by-run allows for conditional search spaces.
+    if activation == "relu":
+        trial.suggest_float("mult", 1, 2)
+
+    # Return all constants in a dictionary.
+    return {"steps": 100}
+
+
+def run_optuna_tune(smoke_test=False):
+    algo = OptunaSearch(space=define_by_run_func, metric="mean_loss", mode="min")
+    algo = ConcurrencyLimiter(algo, max_concurrent=4)
+    scheduler = AsyncHyperBandScheduler()
+    tuner = tune.Tuner(
+        easy_objective,
+        tune_config=tune.TuneConfig(
+            metric="mean_loss",
+            mode="min",
+            search_alg=algo,
+            scheduler=scheduler,
+            num_samples=10 if smoke_test else 100,
+        ),
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    ray.init(configure_logging=False)
+
+    run_optuna_tune(smoke_test=args.smoke_test)