Automated g4 rollback of changelist 170892257

PiperOrigin-RevId: 170919783

2 files changed, 0 insertions, 26 deletions

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index f3d1aca717..f4ccea6806 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -392,10 +392,6 @@ class _TrainingExecutor(object):
 
       metrics = evaluator.evaluate_and_export()
 
-      if not metrics:
-        #  This is unexpected. Training should always end with a new checkpoint.
-        raise RuntimeError('There was no new checkpoint after the training.')
-
       if _should_stop_local_train(metrics[ops.GraphKeys.GLOBAL_STEP]):
         break
 
diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py
index 39c8bffb04..f5b4f88479 100644
--- a/tensorflow/python/estimator/training_test.py
+++ b/tensorflow/python/estimator/training_test.py
@@ -50,7 +50,6 @@ _INVALID_NAME_MSG = '`name` must be string'
 _INVALID_EVAL_DELAY_SECS_MSG = 'Must specify delay_secs >= 0'
 _INVALID_EVAL_THROTTLE_SECS_MSG = 'Must specify throttle_secs >= 0'
 _INVALID_ESTIMATOR_MSG = '`estimator` must have type `tf.estimator.Estimator`'
-_STALE_CHECKPOINT_MSG = 'There was no new checkpoint after the training.'
 _INVALID_EXPORT_STRATEGY_MSG = '`export_strategies` must be an ExportStrategy'
 _DUPLICATE_STRATEGY_NAMES_MSG = '`export_strategies` must have unique names.'
 _INVALID_TRAIN_SPEC_MSG = '`train_spec` must have type `tf.estimator.TrainSpec`'
@@ -1025,27 +1024,6 @@ class TrainingExecutorRunLocalTest(test.TestCase):
     self.assertEqual(3, mock_est.evaluate.call_count)
     self.assertEqual(3, mock_est.times_export_fn_was_called)
 
-  def test_handles_no_new_checkpoint_found(self):
-    mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
-    mock_est.latest_checkpoint.return_value = (
-        'no_new_checkpoints_after_the_first_train_step')
-    train_spec = training.TrainSpec(
-        input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()])
-    eval_spec = training.EvalSpec(
-        input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100)
-    # It was going to be called 3 times.
-    mock_est.evaluate.side_effect = [{
-        _GLOBAL_STEP_KEY: train_spec.max_steps - 100
-    }, {
-        _GLOBAL_STEP_KEY: train_spec.max_steps - 50
-    }, {
-        _GLOBAL_STEP_KEY: train_spec.max_steps
-    }]
-
-    executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
-    with self.assertRaisesRegexp(RuntimeError, _STALE_CHECKPOINT_MSG):
-      executor.run_local()
-
   def test_train_and_evaluate_args(self):
     mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
     mock_est.latest_checkpoint.return_value = 'checkpoint_path/'