chore: import upstream snapshot with attribution

python/ray/tune/examples/tune_basic_example.py

@@ -0,0 +1,57 @@
+"""This example demonstrates basic Ray Tune random search and grid search."""
+import time
+
+import ray
+from ray import tune
+
+
+def evaluation_fn(step, width, height):
+    time.sleep(0.1)
+    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})
+
+
+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)
+
+    # This will do a grid search over the `activation` parameter. This means
+    # that each of the two values (`relu` and `tanh`) will be sampled once
+    # for each sample (`num_samples`). We end up with 2 * 50 = 100 samples.
+    # The `width` and `height` parameters are sampled randomly.
+    # `steps` is a constant parameter.
+
+    tuner = tune.Tuner(
+        easy_objective,
+        tune_config=tune.TuneConfig(
+            metric="mean_loss",
+            mode="min",
+            num_samples=5 if args.smoke_test else 50,
+        ),
+        param_space={
+            "steps": 5 if args.smoke_test else 100,
+            "width": tune.uniform(0, 20),
+            "height": tune.uniform(-100, 100),
+            "activation": tune.grid_search(["relu", "tanh"]),
+        },
+    )
+    results = tuner.fit()
+
+    print("Best hyperparameters found were: ", results.get_best_result().config)