chore: import upstream snapshot with attribution

python/ray/tune/examples/nevergrad_example.py

@@ -0,0 +1,77 @@
+"""This example demonstrates the usage of Nevergrad with Ray Tune.
+
+It also checks that it is usable with a separate scheduler.
+
+Requires the Nevergrad library to be installed (`pip install nevergrad`).
+"""
+
+import time
+
+from ray import tune
+from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.search import ConcurrencyLimiter
+from ray.tune.search.nevergrad import NevergradSearch
+
+
+def evaluation_fn(step, width, height):
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1
+
+
+def easy_objective(config):
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be any 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})
+        time.sleep(0.1)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    import nevergrad as ng
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    # Optional: Pass the parameter space yourself
+    # space = ng.p.Dict(
+    #     width=ng.p.Scalar(lower=0, upper=20),
+    #     height=ng.p.Scalar(lower=-100, upper=100),
+    #     activation=ng.p.Choice(choices=["relu", "tanh"])
+    # )
+
+    algo = NevergradSearch(
+        optimizer=ng.optimizers.OnePlusOne,
+        # space=space,  # If you want to set the space manually
+    )
+    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 args.smoke_test else 50,
+        ),
+        run_config=tune.RunConfig(name="nevergrad"),
+        param_space={
+            "steps": 100,
+            "width": tune.uniform(0, 20),
+            "height": tune.uniform(-100, 100),
+            "activation": tune.choice(["relu", "tanh"]),
+        },
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)