[tune] Trial reporter fix (#3951)

Fixes #3949.

python/ray/tune/function_runner.py

@@ -8,7 +8,7 @@ import threading
 
 from ray.tune import TuneError
 from ray.tune.trainable import Trainable
-from ray.tune.result import TIMESTEPS_TOTAL
+from ray.tune.result import TIMESTEPS_TOTAL, TRAINING_ITERATION
 
 logger = logging.getLogger(__name__)
 
@@ -28,6 +28,7 @@ class StatusReporter(object):
         self._lock = threading.Lock()
         self._error = None
         self._done = False
+        self._iteration = 0
 
     def __call__(self, **kwargs):
         """Report updated training status.
@@ -44,6 +45,7 @@ class StatusReporter(object):
 
         with self._lock:
             self._latest_result = self._last_result = kwargs.copy()
+        self._iteration += 1
 
     def _get_and_clear_status(self):
         if self._error:
@@ -55,10 +57,13 @@ class StatusReporter(object):
                            "last result. To avoid this, include done=True "
                            "upon the last reporter call.")
             self._last_result.update(done=True)
+            self._last_result.setdefault(TRAINING_ITERATION, self._iteration)
             return self._last_result
         with self._lock:
             res = self._latest_result
             self._latest_result = None
+            if res:
+                res.setdefault(TRAINING_ITERATION, self._iteration)
             return res
 
     def _stop(self):

python/ray/tune/suggest/bayesopt.py

@@ -96,7 +96,8 @@ class BayesOptSearch(SuggestionAlgorithm):
             self.optimizer.register(
                 params=self._live_trial_mapping[trial_id],
                 target=result[self._reward_attr])
-            del self._live_trial_mapping[trial_id]
+
+        del self._live_trial_mapping[trial_id]
 
     def _num_live_trials(self):
         return len(self._live_trial_mapping)

python/ray/tune/test/trial_runner_test.py

@@ -19,7 +19,7 @@ from ray.tune.ray_trial_executor import RayTrialExecutor
 from ray.tune.schedulers import TrialScheduler, FIFOScheduler
 from ray.tune.registry import _global_registry, TRAINABLE_CLASS
 from ray.tune.result import (DEFAULT_RESULTS_DIR, TIMESTEPS_TOTAL, DONE,
-                             EPISODES_TOTAL)
+                             EPISODES_TOTAL, TRAINING_ITERATION)
 from ray.tune.logger import Logger
 from ray.tune.util import pin_in_object_store, get_pinned_object
 from ray.tune.experiment import Experiment
@@ -560,6 +560,28 @@ class TrainableFunctionApiTest(unittest.TestCase):
             self.assertEqual(trial.status, Trial.TERMINATED)
             self.assertTrue(trial.has_checkpoint())
 
+    def testIterationCounter(self):
+        def train(config, reporter):
+            for i in range(100):
+                reporter(itr=i, done=i == 99)
+
+        register_trainable("exp", train)
+        config = {
+            "my_exp": {
+                "run": "exp",
+                "config": {
+                    "iterations": 100,
+                },
+                "stop": {
+                    "timesteps_total": 100
+                },
+            }
+        }
+        [trial] = run_experiments(config)
+        self.assertEqual(trial.status, Trial.TERMINATED)
+        self.assertEqual(trial.last_result[TRAINING_ITERATION], 100)
+        self.assertEqual(trial.last_result["itr"], 99)
+
 
 class RunExperimentTest(unittest.TestCase):
     def setUp(self):

python/ray/tune/trainable.py

@@ -18,9 +18,9 @@ import uuid
 
 import ray
 from ray.tune.logger import UnifiedLogger
-from ray.tune.result import (DEFAULT_RESULTS_DIR, TIME_THIS_ITER_S,
-                             TIMESTEPS_THIS_ITER, DONE, TIMESTEPS_TOTAL,
-                             EPISODES_THIS_ITER, EPISODES_TOTAL)
+from ray.tune.result import (
+    DEFAULT_RESULTS_DIR, TIME_THIS_ITER_S, TIMESTEPS_THIS_ITER, DONE,
+    TIMESTEPS_TOTAL, EPISODES_THIS_ITER, EPISODES_TOTAL, TRAINING_ITERATION)
 from ray.tune.trial import Resources
 
 logger = logging.getLogger(__name__)
@@ -181,6 +181,7 @@ class Trainable(object):
         # self._timesteps_total should not override user-provided total
         result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
         result.setdefault(EPISODES_TOTAL, self._episodes_total)
+        result.setdefault(TRAINING_ITERATION, self._iteration)
 
         # Provides auto-filled neg_mean_loss for avoiding regressions
         if result.get("mean_loss"):
@@ -191,7 +192,6 @@ class Trainable(object):
             experiment_id=self._experiment_id,
             date=now.strftime("%Y-%m-%d_%H-%M-%S"),
             timestamp=int(time.mktime(now.timetuple())),
-            training_iteration=self._iteration,
             time_this_iter_s=time_this_iter,
             time_total_s=self._time_total,
             pid=os.getpid(),