experiments/STT-tensorflow
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[tf.data] Remove unused job_token parameter.

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This was originally introduced when we explicitly managed data service job tokens in Python. Job tokens are now managed in c++ instead, so this parameter is no longer unused.

This CL also calls super(OwnedIterator, self).__init__() to prevent a lint error.

PiperOrigin-RevId: 328549745
Change-Id: I1df491f646f39aaf7c6b99e0e7257cd6cd94cc73
This commit is contained in:
Andrew Audibert 2020-08-26 09:46:59 -07:00 committed by TensorFlower Gardener
parent 40a314cab8
commit ea51dc00f0
1 changed files with 3 additions and 15 deletions
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18
tensorflow/python/data/ops/iterator_ops.py
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@ -36,7 +36,6 @@ from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import type_spec
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.ops import gen_experimental_dataset_ops
from tensorflow.python.training.saver import BaseSaverBuilder
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import deprecation
@ -656,11 +655,7 @@ class OwnedIterator(IteratorBase):
in eager mode and inside of tf.functions.
"""
def __init__(self,
dataset=None,
components=None,
element_spec=None,
job_token=None):
def __init__(self, dataset=None, components=None, element_spec=None):
"""Creates a new iterator from the given dataset.
If `dataset` is not specified, the iterator will be created from the given
@ -673,20 +668,17 @@ class OwnedIterator(IteratorBase):
components: Tensor components to construct the iterator from.
element_spec: A nested structure of `TypeSpec` objects that
represents the type specification of elements of the iterator.
job_token: A token to use for reading from a tf.data service job. Data
will be partitioned among all iterators using the same token. If `None`,
the iterator will not read from the tf.data service.
Raises:
ValueError: If `dataset` is not provided and either `components` or
`element_spec` is not provided. Or `dataset` is provided and either
`components` and `element_spec` is provided.
"""
super(OwnedIterator, self).__init__()
error_message = ("Either `dataset` or both `components` and "
"`element_spec` need to be provided.")
self._device = context.context().device_name
self._job_token = job_token
if dataset is None:
if (components is None or element_spec is None):
@ -729,11 +721,7 @@ class OwnedIterator(IteratorBase):
gen_dataset_ops.anonymous_iterator_v2(
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes))
if self._job_token is None:
gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
else:
gen_experimental_dataset_ops.make_data_service_iterator(
ds_variant, self._job_token, self._iterator_resource)
gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
# Delete the resource when this object is deleted
self._resource_deleter = IteratorResourceDeleter(
handle=self._iterator_resource,