message: PSv2: Docstring minor rephrase and typo/example corrections.
diff: @@ -291,11 +291,11 @@ class PerWorkerValues(object):
  """A container that holds a list of values, one value per worker.
  `tf.distribute.experimental.coordinator.PerWorkerValues` contains a collection
  - of values, where each of the value is located one worker respectively, and
  - upon being used as one of the `args` or `kwargs` of
  + of values, where each of the values is located on its corresponding worker,
  + and upon being used as one of the `args` or `kwargs` of
  `tf.distribute.experimental.coordinator.ClusterCoordinator.schedule()`, the
  value specific to a worker will be passed into the function being executed at
  - that particular worker.
  + that corresponding worker.
  Currently, the only supported path to create an object of
  `tf.distribute.experimental.coordinator.PerWorkerValues` is through calling
  @@ -948,14 +948,13 @@ class ClusterCoordinator(object):
  failed worker, it will be added for function execution after datasets created
  by `create_per_worker_dataset` are re-built on it.
  - When a parameter server the coordinator fails, a
  - `tf.errors.UnavailableError` is raised by `schedule`, `join` or `done`. In
  - this case, in addition to bringing back the failed parameter server, users
  - should restart the coordinator to so that it reconnects to the parameter
  - server, re-creates the variables and loads checkpoints. If the coordinator
  - fails, users need to bring it back as well. The program will automatically
  - connect to the parameter servers and workers, and continue the progress from a
  - checkpoint.
  + When a parameter server fails, a `tf.errors.UnavailableError` is raised by
  + `schedule`, `join` or `done`. In this case, in addition to bringing back the
  + failed parameter server, users should restart the coordinator so that it
  + reconnects to workers and parameter servers, re-creates the variables, and
  + loads checkpoints. If the coordinator fails, after the user brings it back,
  + the program will automatically connect to workers and parameter servers, and
  + continue the progress from a checkpoint.
  It is thus essential that in user's program, a checkpoint file is periodically
  saved, and restored at the start of the program. If an
  @@ -1137,7 +1136,7 @@ class ClusterCoordinator(object):
  def per_worker_dataset_fn():
  return strategy.distribute_datasets_from_function(
  - lambda x: tf.data.from_tensor_slices([3] * 3)
  + lambda x: tf.data.Dataset.from_tensor_slices([3] * 3))
  per_worker_dataset = coordinator.create_per_worker_dataset(
  per_worker_dataset_fn)
  