Introduces the placeholder for _TrainingExecutor, which serves the implementation of tf.estimator.train_and_evaluate.

PiperOrigin-RevId: 168240151

tensorflow/python/estimator/BUILD

@@ -76,6 +76,7 @@ py_library(
     srcs = ["training.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":estimator",
         "//tensorflow/python:training",
         "@six_archive//:six",
     ],

tensorflow/python/estimator/training.py

@@ -23,6 +23,7 @@ import collections
 
 import six
 
+from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.training import session_run_hook
 
 
@@ -177,3 +178,51 @@ class EvalSpec(
         delay_secs=delay_secs,
         throttle_secs=throttle_secs)
 
+
+class UnimplementedError(Exception):
+  pass
+
+
+class _TrainingExecutor(object):
+  """The executor to run `Estimator` training and evaluation.
+
+  This implementation supports both distributed and non-distributed (aka local)
+  training and evaluation based on the setting in `tf.estimator.RunConfig`.
+  """
+
+  def __init__(self, estimator, train_spec, eval_spec):
+    if not isinstance(estimator, estimator_lib.Estimator):
+      raise TypeError('`estimator` must have type `tf.estimator.Estimator`.')
+    self._estimator = estimator
+
+    if not isinstance(train_spec, TrainSpec):
+      raise TypeError('`train_spec` must have type `tf.estimator.TrainSpec`.')
+    self._train_spec = train_spec
+
+    if not isinstance(eval_spec, EvalSpec):
+      raise TypeError('`eval_spec` must have type `tf.estimator.EvalSpec`.')
+    self._eval_spec = eval_spec
+
+  @property
+  def estimator(self):
+    return self._estimator
+
+  def run_chief(self):
+    """Runs task chief."""
+    raise UnimplementedError('Method run_chief has not been implemented.')
+
+  def run_worker(self):
+    """Runs task (training) worker."""
+    raise UnimplementedError('Method run_worker has not been implemented.')
+
+  def run_evaluator(self):
+    """Runs task evaluator."""
+    raise UnimplementedError('Method run_evaluator has not been implemented.')
+
+  def run_ps(self):
+    """Runs task parameter server (in training cluster spec)."""
+    raise UnimplementedError('Method run_ps has not been implemented.')
+
+  def run_local(self):
+    """Runs training and evaluation locally (non-distributed)."""
+    raise UnimplementedError('Method run_local has not been implemented.')

tensorflow/python/estimator/training_test.py

@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.estimator import training
 from tensorflow.python.platform import test
 from tensorflow.python.training import session_run_hook
@@ -33,6 +34,9 @@ _INVALID_STEPS_MSG = 'Must specify steps > 0'
 _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`'
+_INVALID_TRAIN_SPEC_MSG = '`train_spec` must have type `tf.estimator.TrainSpec`'
+_INVALID_EVAL_SPEC_MSG = '`eval_spec` must have type `tf.estimator.EvalSpec`'
 
 
 class _FakeHook(session_run_hook.SessionRunHook):
@@ -129,5 +133,40 @@ class EvalSpecTest(test.TestCase):
       training.EvalSpec(input_fn=lambda: 1, throttle_secs=-1)
 
 
+class TrainingExecutorTest(test.TestCase):
+  """Tests _TrainingExecutor."""
+
+  def testRequiredArgumentsSet(self):
+    estimator = estimator_lib.Estimator(model_fn=lambda features: features)
+    train_spec = training.TrainSpec(input_fn=lambda: 1)
+    eval_spec = training.EvalSpec(input_fn=lambda: 1)
+
+    executor = training._TrainingExecutor(estimator, train_spec, eval_spec)
+    self.assertEqual(estimator, executor.estimator)
+
+  def test_invalid_estimator(self):
+    invalid_estimator = object()
+    train_spec = training.TrainSpec(input_fn=lambda: 1)
+    eval_spec = training.EvalSpec(input_fn=lambda: 1)
+
+    with self.assertRaisesRegexp(TypeError, _INVALID_ESTIMATOR_MSG):
+      training._TrainingExecutor(invalid_estimator, train_spec, eval_spec)
+
+  def test_invalid_train_spec(self):
+    estimator = estimator_lib.Estimator(model_fn=lambda features: features)
+    invalid_train_spec = object()
+    eval_spec = training.EvalSpec(input_fn=lambda: 1)
+
+    with self.assertRaisesRegexp(TypeError, _INVALID_TRAIN_SPEC_MSG):
+      training._TrainingExecutor(estimator, invalid_train_spec, eval_spec)
+
+  def test_invalid_eval_spec(self):
+    estimator = estimator_lib.Estimator(model_fn=lambda features: features)
+    train_spec = training.TrainSpec(input_fn=lambda: 1)
+    invalid_eval_spec = object()
+
+    with self.assertRaisesRegexp(TypeError, _INVALID_EVAL_SPEC_MSG):
+      training._TrainingExecutor(estimator, train_spec, invalid_eval_spec)
+
 if __name__ == '__main__':
   test.main()