Refer to cuDNN v2 by its proper name, not CUDNN 6.5 V2, as requested by NVIDIA.

Change: 112120651

Author: martinwicke

configure

@@ -64,12 +64,12 @@ while true; do
   # Retry
 done
 
-# Find out where the CUDNN library is installed
+# Find out where the cuDNN library is installed
 while true; do
   fromuser=""
   if [ -z "$CUDNN_INSTALL_PATH" ]; then
     default_cudnn_path=${CUDA_TOOLKIT_PATH}
-    read -p "Please specify the location where CUDNN 6.5 V2 library is installed. Refer to README.md for more details. [Default is $default_cudnn_path]: " CUDNN_INSTALL_PATH
+    read -p "Please specify the location where cuDNN v2 library is installed. Refer to README.md for more details. [Default is $default_cudnn_path]: " CUDNN_INSTALL_PATH
     fromuser="1"
     if [ -z "$CUDNN_INSTALL_PATH" ]; then
       CUDNN_INSTALL_PATH=$default_cudnn_path
@@ -81,7 +81,7 @@ while true; do
   if [ -e "$CUDNN_INSTALL_PATH/libcudnn.so.6.5" -o -e "$CUDNN_INSTALL_PATH/lib64/libcudnn.so.6.5" ]; then
     break
   fi
-  echo "Invalid path to CUDNN 6.5 V2 toolkit. Neither of the following two files can be found:"
+  echo "Invalid path to cuDNN v2 toolkit. Neither of the following two files can be found:"
   echo "$CUDNN_INSTALL_PATH/lib64/libcudnn.so.6.5"
   echo "$CUDNN_INSTALL_PATH/libcudnn.so.6.5"
   if [ -z "$fromuser" ]; then
@@ -96,7 +96,7 @@ cat > third_party/gpus/cuda/cuda.config <<EOF
 # at the moment.
 CUDA_TOOLKIT_PATH="$CUDA_TOOLKIT_PATH"
 
-# CUDNN_INSTALL_PATH refers to the CUDNN toolkit. The cudnn header and library
+# CUDNN_INSTALL_PATH refers to the cuDNN toolkit. The cuDNN header and library
 # files can be either in this directory, or under include/ and lib64/
 # directories separately.
 CUDNN_INSTALL_PATH="$CUDNN_INSTALL_PATH"

tensorflow/g3doc/get_started/os_setup.md

@@ -8,8 +8,8 @@ github source.
 The TensorFlow Python API currently supports Python 2.7 and Python 3.3+ from
 source.
 
-The GPU version (Linux only) currently requires the Cuda Toolkit 7.0 and CUDNN
-6.5 V2.  Please see [Cuda installation](#optional-install-cuda-gpus-on-linux).
+The GPU version (Linux only) currently requires the Cuda Toolkit 7.0 and cuDNN
+v2.  Please see [Cuda installation](#optional-install-cuda-gpus-on-linux).
 
 ## Overview
 
@@ -30,7 +30,7 @@ images are listed in the corresponding installation sections.
 If you encounter installation errors, see
 [common problems](#common-problems) for some solutions.
 
-## Pip Installation 
+## Pip Installation
 
 [Pip](https://en.wikipedia.org/wiki/Pip_(package_manager)) is a package
 management system used to install and manage software packages written in
@@ -219,12 +219,12 @@ $ path/to/repo/tensorflow/tools/docker/docker_run_gpu.sh b.gcr.io/tensorflow/ten
 
 You can now [test your installation](#test-the-tensorflow-installation) within the Docker container.
 
-## Test the TensorFlow installation 
+## Test the TensorFlow installation
 
 ### (Optional, Linux) Enable GPU Support
 
 If you installed the GPU version of TensorFlow, you must also install the Cuda
-Toolkit 7.0 and CUDNN 6.5 V2.  Please see [Cuda installation](#optional-install-cuda-gpus-on-linux).
+Toolkit 7.0 and cuDNN v2.  Please see [Cuda installation](#optional-install-cuda-gpus-on-linux).
 
 You also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment
 variables.  Consider adding the commands below to your `~/.bash_profile`.  These
@@ -331,7 +331,7 @@ binary path.
 $ sudo apt-get install python-numpy swig python-dev
 ```
 
-#### Configure the installation 
+#### Configure the installation
 
 Run the `configure` script at the root of the tree.  The configure script
 asks you for the path to your python interpreter and allows (optional)
@@ -344,10 +344,10 @@ $ ./configure
 Please specify the location of python. [Default is /usr/bin/python]:
 ```
 
-#### Optional: Install CUDA (GPUs on Linux) 
+#### Optional: Install CUDA (GPUs on Linux)
 
 In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and
-CUDNN 6.5 V2 from NVIDIA need to be installed.
+cuDNN v2 from NVIDIA need to be installed.
 
 TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5.
 Supported cards include but are not limited to:
@@ -363,11 +363,11 @@ https://developer.nvidia.com/cuda-toolkit-70
 
 Install the toolkit into e.g. `/usr/local/cuda`
 
-##### Download and install CUDNN Toolkit 6.5
+##### Download and install cuDNN v2
 
 https://developer.nvidia.com/rdp/cudnn-archive
 
-Uncompress and copy the cudnn files into the toolkit directory.  Assuming the
+Uncompress and copy the cuDNN files into the toolkit directory.  Assuming the
 toolkit is installed in `/usr/local/cuda`:
 
 ``` bash
@@ -376,7 +376,7 @@ sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include
 sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64
 ```
 
-##### Configure TensorFlow's canonical view of Cuda libraries 
+##### Configure TensorFlow's canonical view of Cuda libraries
 When running the `configure` script from the root of your source tree, select
 the option `Y` when asked to build TensorFlow with GPU support.
 
@@ -389,7 +389,7 @@ GPU support will be enabled for TensorFlow
 Please specify the location where CUDA 7.0 toolkit is installed. Refer to
 README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda
 
-Please specify the location where CUDNN 6.5 V2 library is installed. Refer to
+Please specify the location where the cuDNN v2 library is installed. Refer to
 README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda
 
 Setting up Cuda include