Some docstring twists and argument validations.

PiperOrigin-RevId: 171037949

1 files changed, 25 insertions, 18 deletions

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index df0b602309..166b7b20ed 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -75,6 +75,7 @@ def _validate_exporters(exporters):
   try:
     for exporter in exporters:
       if not isinstance(exporter, exporter_lib.Exporter):
+        # Error message will be printed out by the outer try/except.
         raise TypeError
 
       if not exporter.name:
@@ -83,6 +84,10 @@ def _validate_exporters(exporters):
                          ' empty. All exporter names:'
                          ' {}'.format(full_list_of_names))
 
+      if not isinstance(exporter.name, six.string_types):
+        raise ValueError('An Exporter must have a string name. Given: '
+                         '{}'.format(type(exporter.name)))
+
       if exporter.name in unique_names:
         full_list_of_names = [e.name for e in exporters]
         raise ValueError(
@@ -163,7 +168,7 @@ class TrainSpec(
 class EvalSpec(
     collections.namedtuple('EvalSpec', [
         'input_fn', 'steps', 'name', 'hooks', 'exporters',
-        'delay_secs', 'throttle_secs'
+        'start_delay_secs', 'throttle_secs'
     ])):
   """Configuration for the "eval" part for the `train_and_evaluate` call.
 
@@ -179,7 +184,7 @@ class EvalSpec(
               name=None,
               hooks=None,
               exporters=None,
-              delay_secs=120,
+              start_delay_secs=120,
               throttle_secs=600):
     """Creates a validated `EvalSpec` instance.
 
@@ -197,7 +202,8 @@ class EvalSpec(
         on all workers (including chief) during training.
       exporters: Iterable of `Exporter`s, or a single one, or `None`.
         `exporters` will be invoked after each evaluation.
-      delay_secs: Int. Start evaluating after waiting for this many seconds.
+      start_delay_secs: Int. Start evaluating after waiting for this many
+        seconds.
       throttle_secs: Int. Do not re-evaluate unless the last evaluation was
         started at least this many seconds ago. Of course, evaluation does not
         occur if no new checkpoints are available, hence, this is the minimum.
@@ -226,10 +232,10 @@ class EvalSpec(
     # Validate exporters.
     exporters = _validate_exporters(exporters)
 
-    # Validate delay_secs.
-    if delay_secs < 0:
-      raise ValueError(
-          'Must specify delay_secs >= 0, given: {}'.format(delay_secs))
+    # Validate start_delay_secs.
+    if start_delay_secs < 0:
+      raise ValueError('Must specify start_delay_secs >= 0, given: {}'.format(
+          start_delay_secs))
 
     # Validate throttle_secs.
     if throttle_secs < 0:
@@ -243,7 +249,7 @@ class EvalSpec(
         name=name,
         hooks=hooks,
         exporters=exporters,
-        delay_secs=delay_secs,
+        start_delay_secs=start_delay_secs,
         throttle_secs=throttle_secs)
 
 
@@ -606,15 +612,16 @@ class _TrainingExecutor(object):
     # Delay worker to start. For asynchronous training, this usually helps model
     # to converge faster.  Chief starts the training immediately, so, worker
     # with task id x (0-based) should wait (x+1) * _DELAY_SECS_PER_WORKER.
-    delay_secs = 0
+    start_delay_secs = 0
     if config.task_type == run_config_lib.TaskType.WORKER:
       # TODO(xiejw): Replace the hard code logic (task_id + 1) with unique id in
       # training cluster.
-      delay_secs = min(_MAX_DELAY_SECS,
-                       (config.task_id + 1) * _DELAY_SECS_PER_WORKER)
-    if delay_secs > 0:
-      logging.info('Waiting %d secs before starting training.', delay_secs)
-      time.sleep(delay_secs)
+      start_delay_secs = min(_MAX_DELAY_SECS,
+                             (config.task_id + 1) * _DELAY_SECS_PER_WORKER)
+    if start_delay_secs > 0:
+      logging.info('Waiting %d secs before starting training.',
+                   start_delay_secs)
+      time.sleep(start_delay_secs)
 
     self._estimator.train(input_fn=self._train_spec.input_fn,
                           max_steps=self._train_spec.max_steps,
@@ -623,10 +630,10 @@ class _TrainingExecutor(object):
 
   def _start_continuous_evaluation(self):
     """Repeatedly calls `Estimator` evaluate and export until training ends."""
-    delay_secs = self._eval_spec.delay_secs
-    if delay_secs:
-      logging.info('Waiting %f secs before starting eval.', delay_secs)
-      time.sleep(delay_secs)
+    start_delay_secs = self._eval_spec.start_delay_secs
+    if start_delay_secs:
+      logging.info('Waiting %f secs before starting eval.', start_delay_secs)
+      time.sleep(start_delay_secs)
 
     latest_eval_result = None
     evaluator = _TrainingExecutor._Evaluator(self._estimator, self._eval_spec)