[tune] Simplify API (#4234)

Uses `tune.run` to execute experiments as preferred API.

@noahgolmant

This does not break backwards compat, but will slowly internalize `Experiment`. 

In a separate PR, Tune schedulers should only support 1 running experiment at a time.

python/ray/tune/examples/hyperopt_example.py

@@ -7,7 +7,7 @@ from __future__ import division
 from __future__ import print_function
 
 import ray
-from ray.tune import run_experiments, register_trainable
+from ray.tune import run
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest import HyperOptSearch
 
@@ -35,8 +35,6 @@ if __name__ == "__main__":
     args, _ = parser.parse_known_args()
     ray.init()
 
-    register_trainable("exp", easy_objective)
-
     space = {
         'width': hp.uniform('width', 0, 20),
         'height': hp.uniform('height', -100, 100),
@@ -57,16 +55,13 @@ if __name__ == "__main__":
     ]
 
     config = {
-        "my_exp": {
-            "run": "exp",
-            "num_samples": 10 if args.smoke_test else 1000,
-            "config": {
-                "iterations": 100,
-            },
-            "stop": {
-                "timesteps_total": 100
-            },
-        }
+        "num_samples": 10 if args.smoke_test else 1000,
+        "config": {
+            "iterations": 100,
+        },
+        "stop": {
+            "timesteps_total": 100
+        },
     }
     algo = HyperOptSearch(
         space,
@@ -74,4 +69,4 @@ if __name__ == "__main__":
         reward_attr="neg_mean_loss",
         points_to_evaluate=current_best_params)
     scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
-    run_experiments(config, search_alg=algo, scheduler=scheduler)
+    run(easy_objective, search_alg=algo, scheduler=scheduler, **config)