message: Add a caveat about using early stopping library in distributed mode.
diff: @@ -57,6 +57,13 @@ def make_early_stopping_hook(estimator,
  tf.estimator.train_and_evaluate(estimator, train_spec, ...)
  ```
  + Caveat: Current implementation supports early-stopping both training and
  + evaluation in local mode. In distributed mode, training can be stopped but
  + evaluation (where it's a separate job) will indefinitely wait for new model
  + checkpoints to evaluate, so you will need other means to detect and stop it.
  + Early-stopping evaluation in distributed mode requires changes in
  + `train_and_evaluate` API and will be addressed in a future revision.
  +
  Args:
  estimator: A `tf.estimator.Estimator` instance.
  should_stop_fn: `callable`, function that takes no arguments and returns a
  @@ -109,6 +116,13 @@ def stop_if_higher_hook(estimator,
  tf.estimator.train_and_evaluate(estimator, train_spec, ...)
  ```
  + Caveat: Current implementation supports early-stopping both training and
  + evaluation in local mode. In distributed mode, training can be stopped but
  + evaluation (where it's a separate job) will indefinitely wait for new model
  + checkpoints to evaluate, so you will need other means to detect and stop it.
  + Early-stopping evaluation in distributed mode requires changes in
  + `train_and_evaluate` API and will be addressed in a future revision.
  +
  Args:
  estimator: A `tf.estimator.Estimator` instance.
  metric_name: `str`, metric to track. "loss", "accuracy", etc.
  @@ -158,6 +172,13 @@ def stop_if_lower_hook(estimator,
  tf.estimator.train_and_evaluate(estimator, train_spec, ...)
  ```
  + Caveat: Current implementation supports early-stopping both training and
  + evaluation in local mode. In distributed mode, training can be stopped but
  + evaluation (where it's a separate job) will indefinitely wait for new model
  + checkpoints to evaluate, so you will need other means to detect and stop it.
  + Early-stopping evaluation in distributed mode requires changes in
  + `train_and_evaluate` API and will be addressed in a future revision.
  +
  Args:
  estimator: A `tf.estimator.Estimator` instance.
  metric_name: `str`, metric to track. "loss", "accuracy", etc.
  @@ -207,6 +228,13 @@ def stop_if_no_increase_hook(estimator,
  tf.estimator.train_and_evaluate(estimator, train_spec, ...)
  ```
  + Caveat: Current implementation supports early-stopping both training and
  + evaluation in local mode. In distributed mode, training can be stopped but
  + evaluation (where it's a separate job) will indefinitely wait for new model
  + checkpoints to evaluate, so you will need other means to detect and stop it.
  + Early-stopping evaluation in distributed mode requires changes in
  + `train_and_evaluate` API and will be addressed in a future revision.
  +
  Args:
  estimator: A `tf.estimator.Estimator` instance.
  metric_name: `str`, metric to track. "loss", "accuracy", etc.
  @@ -257,6 +285,13 @@ def stop_if_no_decrease_hook(estimator,
  tf.estimator.train_and_evaluate(estimator, train_spec, ...)
  ```
  + Caveat: Current implementation supports early-stopping both training and
  + evaluation in local mode. In distributed mode, training can be stopped but
  + evaluation (where it's a separate job) will indefinitely wait for new model
  + checkpoints to evaluate, so you will need other means to detect and stop it.
  + Early-stopping evaluation in distributed mode requires changes in
  + `train_and_evaluate` API and will be addressed in a future revision.
  +
  Args:
  estimator: A `tf.estimator.Estimator` instance.
  metric_name: `str`, metric to track. "loss", "accuracy", etc.
  