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tf.gfile is now tf.io.gfile.

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PiperOrigin-RevId: 293818901
Change-Id: I51746a055ca608a2a8b0cfb8696f7f33ca84fd53
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A. Unique TensorFlower 2020-02-07 08:29:21 -08:00 committed by TensorFlower Gardener
parent bc1459f360
commit 925be1049d
1 changed files with 3 additions and 3 deletions
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6
tensorflow/python/platform/gfile.py
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@ -41,21 +41,21 @@ from tensorflow.python.util.tf_export import tf_export
class GFile(_FileIO):
"""File I/O wrappers without thread locking.
The main roles of the `tf.gfile` module are:
The main roles of the `tf.io.gfile` module are:
1. To provide an API that is close to Python's file I/O objects, and
2. To provide an implementation based on TensorFlow's C++ FileSystem API.
The C++ FileSystem API supports multiple file system implementations,
including local files, Google Cloud Storage (using a `gs://` prefix, and
HDFS (using an `hdfs://` prefix). TensorFlow exports these as `tf.gfile`,
HDFS (using an `hdfs://` prefix). TensorFlow exports these as `tf.io.gfile`,
so that you can use these implementations for saving and loading checkpoints,
writing to TensorBoard logs, and accessing training data (among other uses).
However, if all your files are local, you can use the regular Python file
API without any problem.
*Note*: though similar to Python's I/O implementation, there are semantic
differences to make `tf.gfile` more efficient for backing filesystems. For
differences to make `tf.io.gfile` more efficient for backing filesystems. For
example, a write mode file will not be opened until the first write call, to
minimize RPC invocations in network filesystems.
"""