[tune] Fix performance issue and fix reuse tests (#4379)

* fix tests

* better name

* reduce warnings

* better resource tracking

* oops

* revertmessage

* fix_executor

python/ray/tune/ray_trial_executor.py

@@ -18,6 +18,7 @@ from ray.tune.util import warn_if_slow
 
 logger = logging.getLogger(__name__)
 
+RESOURCE_REFRESH_PERIOD = 0.5  # Refresh resources every 500 ms
 BOTTLENECK_WARN_PERIOD_S = 60
 NONTRIVIAL_WAIT_TIME_THRESHOLD_S = 1e-3
 
@@ -34,18 +35,24 @@ class _LocalWrapper(object):
 class RayTrialExecutor(TrialExecutor):
     """An implemention of TrialExecutor based on Ray."""
 
-    def __init__(self, queue_trials=False, reuse_actors=False):
+    def __init__(self,
+                 queue_trials=False,
+                 reuse_actors=False,
+                 refresh_period=RESOURCE_REFRESH_PERIOD):
         super(RayTrialExecutor, self).__init__(queue_trials)
         self._running = {}
         # Since trial resume after paused should not run
         # trial.train.remote(), thus no more new remote object id generated.
         # We use self._paused to store paused trials here.
         self._paused = {}
+        self._reuse_actors = reuse_actors
+        self._cached_actor = None
+
         self._avail_resources = Resources(cpu=0, gpu=0)
         self._committed_resources = Resources(cpu=0, gpu=0)
         self._resources_initialized = False
-        self._reuse_actors = reuse_actors
+        self._refresh_period = refresh_period
-        self._cached_actor = None
+        self._last_resource_refresh = float("-inf")
         self._last_nontrivial_wait = time.time()
         if ray.is_initialized():
             self._update_avail_resources()
@@ -370,11 +377,19 @@ class RayTrialExecutor(TrialExecutor):
 
         self._avail_resources = Resources(
             int(num_cpus), int(num_gpus), custom_resources=custom_resources)
+        self._last_resource_refresh = time.time()
         self._resources_initialized = True
 
     def has_resources(self, resources):
-        """Returns whether this runner has at least the specified resources."""
+        """Returns whether this runner has at least the specified resources.
-        self._update_avail_resources()
+
+        This refreshes the Ray cluster resources if the time since last update
+        has exceeded self._refresh_period. This also assumes that the
+        cluster is not resizing very frequently.
+        """
+        if time.time() - self._last_resource_refresh > self._refresh_period:
+            self._update_avail_resources()
+
         currently_available = Resources.subtract(self._avail_resources,
                                                  self._committed_resources)
 
@@ -445,7 +460,6 @@ class RayTrialExecutor(TrialExecutor):
 
     def on_step_begin(self):
         """Before step() called, update the available resources."""
-
         self._update_avail_resources()
 
     def save(self, trial, storage=Checkpoint.DISK):

python/ray/tune/tests/test_actor_reuse.py

@@ -15,27 +15,30 @@ class FrequentPausesScheduler(FIFOScheduler):
         return TrialScheduler.PAUSE
 
 
-class MyResettableClass(Trainable):
+def create_resettable_class():
-    def _setup(self, config):
+    class MyResettableClass(Trainable):
-        self.config = config
+        def _setup(self, config):
-        self.num_resets = 0
+            self.config = config
-        self.iter = 0
+            self.num_resets = 0
+            self.iter = 0
 
-    def _train(self):
+        def _train(self):
-        self.iter += 1
+            self.iter += 1
-        return {"num_resets": self.num_resets, "done": self.iter > 1}
+            return {"num_resets": self.num_resets, "done": self.iter > 1}
 
-    def _save(self, chkpt_dir):
+        def _save(self, chkpt_dir):
-        return {"iter": self.iter}
+            return {"iter": self.iter}
 
-    def _restore(self, item):
+        def _restore(self, item):
-        self.iter = item["iter"]
+            self.iter = item["iter"]
 
-    def reset_config(self, new_config):
+        def reset_config(self, new_config):
-        if "fake_reset_not_supported" in self.config:
+            if "fake_reset_not_supported" in self.config:
-            return False
+                return False
-        self.num_resets += 1
+            self.num_resets += 1
-        return True
+            return True
+
+    return MyResettableClass
 
 
 class ActorReuseTest(unittest.TestCase):
@@ -49,7 +52,7 @@ class ActorReuseTest(unittest.TestCase):
         trials = run_experiments(
             {
                 "foo": {
-                    "run": MyResettableClass,
+                    "run": create_resettable_class(),
                     "num_samples": 4,
                     "config": {},
                 }
@@ -63,7 +66,7 @@ class ActorReuseTest(unittest.TestCase):
         trials = run_experiments(
             {
                 "foo": {
-                    "run": MyResettableClass,
+                    "run": create_resettable_class(),
                     "num_samples": 4,
                     "config": {},
                 }
@@ -78,7 +81,7 @@ class ActorReuseTest(unittest.TestCase):
             run_experiments(
                 {
                     "foo": {
-                        "run": MyResettableClass,
+                        "run": create_resettable_class(),
                         "max_failures": 1,
                         "num_samples": 4,
                         "config": {