[tune] refactor and add examples (#11931)

python/ray/tune/examples/zoopt_example.py

@@ -1,10 +1,9 @@
-"""This test checks that ZOOptSearch is functional.
+"""This example demonstrates the usage of ZOOptSearch.
 
 It also checks that it is usable with a separate scheduler.
 """
 import time
 
-import ray
 from ray import tune
 from ray.tune.suggest.zoopt import ZOOptSearch
 from ray.tune.schedulers import AsyncHyperBandScheduler
@@ -12,6 +11,7 @@ from zoopt import ValueType  # noqa: F401
 
 
 def evaluation_fn(step, width, height):
+    time.sleep(0.1)
     return (0.1 + width * step / 100)**(-1) + height * 0.1
 
 
@@ -24,7 +24,6 @@ def easy_objective(config):
         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__":
@@ -34,16 +33,8 @@ if __name__ == "__main__":
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
     args, _ = parser.parse_known_args()
-    ray.init()
 
-    tune_kwargs = {
-        "num_samples": 10 if args.smoke_test else 1000,
-        "config": {
-            "steps": 10,
-            "height": tune.quniform(-10, 10, 1e-2),
-            "width": tune.randint(0, 10)
-        }
-    }
+    num_samples = 10 if args.smoke_test else 1000
 
     # Optional: Pass the parameter space yourself
     # space = {
@@ -61,17 +52,23 @@ if __name__ == "__main__":
 
     zoopt_search = ZOOptSearch(
         algo="Asracos",  # only support ASRacos currently
-        budget=tune_kwargs["num_samples"],
+        budget=num_samples,
         # dim_dict=space,  # If you want to set the space yourself
-        metric="mean_loss",
-        mode="min",
         **zoopt_search_config)
 
-    scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
+    scheduler = AsyncHyperBandScheduler()
 
-    tune.run(
+    analysis = tune.run(
         easy_objective,
+        metric="mean_loss",
+        mode="min",
         search_alg=zoopt_search,
         name="zoopt_search",
         scheduler=scheduler,
-        **tune_kwargs)
+        num_samples=num_samples,
+        config={
+            "steps": 10,
+            "height": tune.quniform(-10, 10, 1e-2),
+            "width": tune.randint(0, 10)
+        })
+    print("Best config found: ", analysis.best_config)