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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Kinesis Dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.kinesis.python.ops import kinesis_op_loader # pylint: disable=unused-import
from tensorflow.contrib.kinesis.python.ops import gen_dataset_ops
from tensorflow.python.data.ops.dataset_ops import Dataset
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
class KinesisDataset(Dataset):
"""A Kinesis Dataset that consumes the message.
Kinesis is a managed service provided by AWS for data streaming.
This dataset reads messages from Kinesis with each message presented
as a `tf.string`.
For example, we can construct and use the KinesisDataset as follows:
```python
dataset = tf.contrib.kinesis.KinesisDataset(
"kinesis_stream_name", read_indefinitely=False)
next = dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
while True:
try:
print(sess.run(nxt))
except tf.errors.OutOfRangeError:
break
```
Since Kinesis is a data streaming service, data may not be available
at the time it is being read. The argument `read_indefinitely` is
used to control the behavior in this situation. If `read_indefinitely`
is `True`, then `KinesisDataset` will keep retrying to retrieve data
from the stream. If `read_indefinitely` is `False`, an `OutOfRangeError`
is returned immediately instead.
"""
def __init__(self,
stream,
shard="",
read_indefinitely=True,
interval=100000):
"""Create a KinesisDataset.
Args:
stream: A `tf.string` tensor containing the name of the stream.
shard: A `tf.string` tensor containing the id of the shard.
read_indefinitely: If `True`, the Kinesis dataset will keep retry
again on `EOF` after the `interval` period. If `False`, then
the dataset will stop on `EOF`. The default value is `True`.
interval: The interval for the Kinesis Client to wait before
it tries to get records again (in millisecond).
"""
super(KinesisDataset, self).__init__()
self._stream = ops.convert_to_tensor(
stream, dtype=dtypes.string, name="stream")
self._shard = ops.convert_to_tensor(
shard, dtype=dtypes.string, name="shard")
self._read_indefinitely = ops.convert_to_tensor(
read_indefinitely, dtype=dtypes.bool, name="read_indefinitely")
self._interval = ops.convert_to_tensor(
interval, dtype=dtypes.int64, name="interval")
def _as_variant_tensor(self):
return gen_dataset_ops.kinesis_dataset(
self._stream, self._shard, self._read_indefinitely, self._interval)
@property
def output_classes(self):
return ops.Tensor
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.string
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