diff --git a/python/ray/tune/examples/async_hyperband_example.py b/python/ray/tune/examples/async_hyperband_example.py
index fcd4fda4b..a5a5bb4e0 100644
--- a/python/ray/tune/examples/async_hyperband_example.py
+++ b/python/ray/tune/examples/async_hyperband_example.py
@@ -50,8 +50,11 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument(
+        "--ray-redis-address",
+        help="Address of Ray cluster for seamless distributed execution.")
     args, _ = parser.parse_known_args()
-    ray.init()
+    ray.init(redis_address=args.ray_redis_address)
 
     # asynchronous hyperband early stopping, configured with
     # `episode_reward_mean` as the
diff --git a/python/ray/tune/log_sync.py b/python/ray/tune/log_sync.py
index 6888ae6a8..29c39d113 100644
--- a/python/ray/tune/log_sync.py
+++ b/python/ray/tune/log_sync.py
@@ -55,7 +55,7 @@ class NodeSyncMixin():
 
     def _check_valid_worker_ip(self):
         if not self.worker_ip:
-            logger.info("Worker ip unknown, skipping log sync for {}".format(
+            logger.debug("Worker ip unknown, skipping log sync for {}".format(
                 self._local_dir))
             return False
         if self.worker_ip == self.local_ip:
diff --git a/python/ray/tune/ray_trial_executor.py b/python/ray/tune/ray_trial_executor.py
index 4a17bc581..f2ac6a68f 100644
--- a/python/ray/tune/ray_trial_executor.py
+++ b/python/ray/tune/ray_trial_executor.py
@@ -180,11 +180,8 @@ class RayTrialExecutor(TrialExecutor):
                     logger.debug("Reusing actor for {}".format(trial.runner))
                     self._cached_actor = trial.runner
                 else:
-                    logger.info(
-                        "Destroying actor for trial {}. If your trainable is "
-                        "slow to initialize, consider setting "
-                        "reuse_actors=True to reduce actor creation "
-                        "overheads.".format(trial))
+                    logger.debug(
+                        "Destroying actor for trial {}.".format(trial))
                     trial.runner.stop.remote()
                     trial.runner.__ray_terminate__.remote()
         except Exception:
diff --git a/python/ray/tune/trainable.py b/python/ray/tune/trainable.py
index f5df41095..04ebbf527 100644
--- a/python/ray/tune/trainable.py
+++ b/python/ray/tune/trainable.py
@@ -26,6 +26,8 @@ from ray.tune.util import UtilMonitor
 
 logger = logging.getLogger(__name__)
 
+SETUP_TIME_THRESHOLD = 10
+
 
 class Trainable(object):
     """Abstract class for trainable models, functions, etc.
@@ -93,7 +95,14 @@ class Trainable(object):
         self._timesteps_since_restore = 0
         self._iterations_since_restore = 0
         self._restored = False
+        start_time = time.time()
         self._setup(copy.deepcopy(self.config))
+        setup_time = time.time() - start_time
+        if setup_time > SETUP_TIME_THRESHOLD:
+            logger.info("_setup took {:.3f} seconds. If your trainable is "
+                        "slow to initialize, consider setting "
+                        "reuse_actors=True to reduce actor creation "
+                        "overheads.".format(setup_time))
         self._local_ip = ray.services.get_node_ip_address()
         self._monitor = UtilMonitor(start=log_sys_usage)
 
diff --git a/python/ray/tune/util.py b/python/ray/tune/util.py
index 0c53a14ad..a6c122428 100644
--- a/python/ray/tune/util.py
+++ b/python/ray/tune/util.py
@@ -43,10 +43,10 @@ class UtilMonitor(Thread):
 
     def __init__(self, start=True, delay=0.7):
         self.stopped = True
-        if GPUtil is None:
+        if GPUtil is None and start:
             logger.warning("Install gputil for GPU system monitoring.")
 
-        if psutil is None:
+        if psutil is None and start:
             logger.warning("Install psutil to monitor system performance.")
 
         if GPUtil is None and psutil is None: