From 925be1049d191f25aa41c26db03205935b2e8582 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" <gardener@tensorflow.org> Date: Fri, 7 Feb 2020 08:29:21 -0800 Subject: [PATCH] tf.gfile is now tf.io.gfile. PiperOrigin-RevId: 293818901 Change-Id: I51746a055ca608a2a8b0cfb8696f7f33ca84fd53 --- tensorflow/python/platform/gfile.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/platform/gfile.py b/tensorflow/python/platform/gfile.py index 74f76595c7a..028c983ac33 100644 --- a/tensorflow/python/platform/gfile.py +++ b/tensorflow/python/platform/gfile.py @@ -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. """