Add documentation for TensorFlow Windows build with Bazel

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diff --git a/tensorflow/docs_src/install/install_sources_windows.md b/tensorflow/docs_src/install/install_sources_windows.md
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+# Install TensorFlow from Sources on Windows
+
+This guide explains how to build TensorFlow sources into a TensorFlow binary and
+how to install that TensorFlow binary on Windows.
+
+## Determine which TensorFlow to install
+
+You must choose one of the following types of TensorFlow to build and install:
+
+*   **TensorFlow with CPU support only**. If your system does not have a NVIDIA®
+    GPU, build and install this version. Note that this version of TensorFlow is
+    typically easier to build and install, so even if you have an NVIDIA GPU, we
+    recommend building and installing this version first.
+*   **TensorFlow with GPU support**. TensorFlow programs typically run
+    significantly faster on a GPU than on a CPU. Therefore, if your system has a
+    NVIDIA GPU and you need to run performance-critical applications, you should
+    ultimately build and install this version. Beyond the NVIDIA GPU itself,
+    your system must also fulfill the NVIDIA software requirements described in
+    the following document:
+
+    *   [Installing TensorFlow on Windows](install_windows.md#NVIDIARequirements)
+
+## Prepare environment for Windows
+
+Before building TensorFlow on Windows, install the following build tools on your
+system:
+
+*   [MSYS2](#InstallMSYS2)
+*   [Visual C++ build tools](#InstallVCBuildTools)
+*   [Bazel for Windows](#InstallBazel)
+*   [TensorFlow Python dependencies](#InstallPython)
+*   [optionally, NVIDIA packages to support TensorFlow for GPU](#InstallCUDA)
+
+<a name="InstallMSYS2"></a>
+
+### Install MSYS2
+
+Bash bin tools are used in TensorFlow Bazel build, you can install them through [MSYS2](https://www.msys2.org/).
+
+Assume you installed MSYS2 at `C:\msys64`, add `C:\msys64\usr\bin` to your `%PATH%` environment variable.
+
+To install necessary bash bin tools, issue the following command under `cmd.exe`:
+
+<pre>
+C:\> <b>pacman -S git patch unzip</b>
+</pre>
+
+<a name="InstallVCBuildTools"></a>
+
+### Install Visual C++ Build Tools 2015
+
+To build TensorFlow, you need to install Visual C++ build tools 2015. It is a part of Visual Studio 2015.
+But you can install it separately by the following way:
+
+  * Open the [official downloand page](https://visualstudio.microsoft.com/vs/older-downloads/).
+  * Go to <b>Redistributables and Build Tools</b> section.
+  * Find <b>Microsoft Build Tools 2015 Update 3</b> and click download.
+  * Run the installer.
+
+It's possible to build TensorFlow with newer version of Visual C++ build tools,
+but we only test against Visual Studio 2015 Update 3.
+
+<a name="InstallBazel"></a>
+
+### Install Bazel
+
+If bazel is not installed on your system, install it now by following
+[these instructions](https://docs.bazel.build/versions/master/install-windows.html).
+It is recommended to use a Bazel version >= `0.15.0`.
+
+Add the directory where you installed Bazel to your `%PATH%` environment variable.
+
+<a name="InstallPython"></a>
+
+### Install TensorFlow Python dependencies
+
+If you don't have Python 3.5 or Python 3.6 installed, install it now:
+
+  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.6.x 64-bit from python.org](https://www.python.org/downloads/release/python-362/)
+
+To build and install TensorFlow, you must install the following python packages:
+
+*   `six`, which provides simple utilities for wrapping over differences between
+    Python 2 and Python 3.
+*   `numpy`, which is a numerical processing package that TensorFlow requires.
+*   `wheel`, which enables you to manage Python compressed packages in the wheel
+    (.whl) format.
+*   `keras_applications`, the applications module of the Keras deep learning library.
+*   `keras_preprocessing`, the data preprocessing and data augmentation module
+    of the Keras deep learning library.
+
+Assume you already have `pip3` in `%PATH%`, issue the following command:
+
+<pre>
+C:\> <b>pip3 install six numpy wheel</b>
+C:\> <b>pip3 install keras_applications==1.0.4 --no-deps</b>
+C:\> <b>pip3 install keras_preprocessing==1.0.2 --no-deps</b>
+</pre>
+
+<a name="InstallCUDA"></a>
+
+### Optional: install TensorFlow for GPU prerequisites
+
+If you are building TensorFlow without GPU support, skip this section.
+
+The following NVIDIA® _hardware_ must be installed on your system:
+
+*   GPU card with CUDA Compute Capability 3.5 or higher. See
+    [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of
+    supported GPU cards.
+
+The following NVIDIA® _software_ must be installed on your system:
+
+*   [GPU drivers](http://nvidia.com/driver). CUDA 9.0 requires 384.x or higher.
+*   [CUDA Toolkit](http://nvidia.com/cuda) (>= 8.0). We recommend version 9.0.
+*   [cuDNN SDK](http://developer.nvidia.com/cudnn) (>= 6.0). We recommend
+    version 7.1.x.
+*   [CUPTI](http://docs.nvidia.com/cuda/cupti/) ships with the CUDA Toolkit, but
+    you also need to append its path to `%PATH%` environment
+    variable.
+
+Assume you have CUDA Toolkit installed at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`
+and cuDNN at `C:\tools\cuda`, issue the following commands.
+
+<pre>
+C:\> SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin;%PATH%
+C:\> SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64;%PATH%
+C:\> SET PATH=C:\tools\cuda\bin;%PATH%
+</pre>
+
+## Clone the TensorFlow repository
+
+Now you need to clone **the latest** TensorFlow repository,
+thanks to MSYS2 we already have `git` avaiable, issue the following command:
+
+<pre>C:\> <b>git clone https://github.com/tensorflow/tensorflow.git</b> </pre>
+
+The preceding <code>git clone</code> command creates a subdirectory named
+`tensorflow`. After cloning, you may optionally build a **specific branch**
+(such as a release branch) by invoking the following commands:
+
+<pre>
+C:\> <b>cd tensorflow</b>
+C:\> <b>git checkout</b> <i>Branch</i> # where <i>Branch</i> is the desired branch
+</pre>
+
+For example, to work with the `r1.11` release instead of the master release,
+issue the following command:
+
+<pre>C:\> <b>git checkout r1.11</b></pre>
+
+Next, you must now configure the installation.
+
+## Configure the installation
+
+The root of the source tree contains a python script named <code>configure.py</code>.
+This script asks you to identify the pathname of all relevant TensorFlow
+dependencies and specify other build configuration options such as compiler
+flags. You must run this script *prior* to creating the pip package and
+installing TensorFlow.
+
+If you wish to build TensorFlow with GPU, `configure.py` will ask you to specify
+the version numbers of CUDA and cuDNN. If several versions of CUDA or cuDNN are
+installed on your system, explicitly select the desired version instead of
+relying on the default.
+
+One of the questions that `configure.py` will ask is as follows:
+
+<pre>
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]:
+</pre>
+
+Here is an example execution of the `configure.py` script. Note that your own input
+will likely differ from our sample input:
+
+<pre>
+C:\> <b>cd tensorflow</b>  # cd to the top-level directory created
+C:\tensorflow> <b>python ./configure.py</b>
+Starting local Bazel server and connecting to it...
+................
+You have bazel 0.15.0 installed.
+Please specify the location of python. [Default is C:\python36\python.exe]: 
+
+Found possible Python library paths:
+  C:\python36\lib\site-packages
+Please input the desired Python library path to use.  Default is [C:\python36\lib\site-packages]
+
+Do you wish to build TensorFlow with CUDA support? [y/N]: <b>Y</b>
+CUDA support will be enabled for TensorFlow.
+
+Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]:
+
+Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0]:
+
+Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: <b>7.0</b>
+
+Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0]: <b>C:\tools\cuda</b>
+
+Please specify a list of comma-separated Cuda compute capabilities you want to build with.
+You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
+Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 3.5,7.0]: <b>3.7</b>
+
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]: 
+
+Would you like to override eigen strong inline for some C++ compilation to reduce the compilation time? [Y/n]:
+Eigen strong inline overridden.
+
+Configuration finished
+</pre>
+
+## Build the pip package
+
+### CPU-only support
+
+To build a pip package for TensorFlow with CPU-only support:
+
+<pre>
+C:\tensorflow> <b>bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package</b>
+</pre>
+
+### GPU support
+
+To build a pip package for TensorFlow with GPU support:
+
+<pre>
+C:\tensorflow> <b>bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package</b>
+</pre>
+
+**NOTE :** When building with GPU support, you might want to add `--copt=-nvcc_options=disable-warnings`
+to suppress nvcc warning messages.
+
+The `bazel build` command builds a binary named `build_pip_package`
+(an executable binary to launch bash and run a bash script to create the pip package).
+Running this binary as follows will build a `.whl` file within the `C:/tmp/tensorflow_pkg` directory:
+
+<pre>
+C:\tensorflow> <b>bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/tmp/tensorflow_pkg</b>
+</pre>
+
+## Install the pip package
+
+Invoke `pip3 install` to install that pip package. The filename of the `.whl`
+file depends on the TensorFlow version and your platform. For example, the
+following command will install the pip package for TensorFlow 1.11.0rc0:
+
+<pre>
+C:\tensorflow> <b>pip3 install C:/tmp/tensorflow_pkg/tensorflow-1.11.0rc0-cp36-cp36m-win_amd64.whl</b>
+</pre>
+
+## Validate your installation
+
+Validate your TensorFlow installation by doing the following:
+
+Start a terminal.
+
+Change directory (`cd`) to any directory on your system other than the
+`tensorflow` subdirectory from which you invoked the `configure` command.
+
+Invoke python:
+
+<pre>$ <b>python</b></pre>
+
+Enter the following short program inside the python interactive shell:
+
+```python
+# Python
+import tensorflow as tf
+hello = tf.constant('Hello, TensorFlow!')
+sess = tf.Session()
+print(sess.run(hello))
+```
+
+If the system outputs the following, then you are ready to begin writing
+TensorFlow programs:
+
+<pre>Hello, TensorFlow!</pre>
+
+To learn more, see the [TensorFlow tutorials](../tutorials/).
+
+## Build under MSYS shell
+The above instruction assumes you are building under the Windows native command line (`cmd.exe`), but you can also
+build TensorFlow from MSYS shell. There are a few things to notice:
+
+*   Disable the path conversion heuristic in MSYS. MSYS automatically converts arguments that look
+    like a Unix path to Windows path when running a program, this will confuse Bazel.
+    (eg. A Bazel label `//foo/bar:bin` is considered a Unix absolute path, only because it starts with a slash)
+
+  ```sh
+$ export MSYS_NO_PATHCONV=1
+$ export MSYS2_ARG_CONV_EXCL="*"
+```
+
+*   Add the directory where you install Bazel in `$PATH`. Assume you have Bazel
+    installed at `C:\tools\bazel.exe`, issue the following command:
+
+  ```sh
+# `:` is used as path separator, so we have to convert the path to Unix style.
+$ export PATH="/c/tools:$PATH"
+```
+
+*   Add the directory where you install Python in `$PATH`. Assume you have
+    Python installed at `C:\Python36\python.exe`, issue the following command:
+
+  ```sh
+$ export PATH="/c/Python36:$PATH"
+```
+
+*   If you have Python in `$PATH`, you can run configure script just by
+    `./configure`, a shell script will help you invoke python.
+
+*   (For GPU build only) Add Cuda and cuDNN bin directories in `$PATH` in the following way:
+
+  ```sh
+$ export PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/bin:$PATH"
+$ export PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/extras/CUPTI/libx64:$PATH"
+$ export PATH="/c/tools/cuda/bin:$PATH"
+```
+
+The rest steps should be the same as building under `cmd.exe`.
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index e9061bf3c1..0bb0e5aeb9 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -24,6 +24,8 @@ You must choose one of the following types of TensorFlow to install:
     and you need to run performance-critical applications, you should
     ultimately install this version.
 
+<a name="NVIDIARequirements"></a>
+
 ### Requirements to run TensorFlow with GPU support
 
 If you are installing TensorFlow with GPU support using one of the mechanisms
diff --git a/tensorflow/docs_src/install/leftnav_files b/tensorflow/docs_src/install/leftnav_files
index ace275c0e8..59292f7121 100644
--- a/tensorflow/docs_src/install/leftnav_files
+++ b/tensorflow/docs_src/install/leftnav_files
@@ -6,6 +6,7 @@ install_mac.md: MacOS
 install_windows.md: Windows
 install_raspbian.md: Raspbian
 install_sources.md: From source
+install_sources_windows.md: From source on Windows
 >>>
 migration.md