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diff --git a/tensorflow/docs_src/install/install_sources_windows.md b/tensorflow/docs_src/install/install_sources_windows.md deleted file mode 100644 index 40dce106d6..0000000000 --- a/tensorflow/docs_src/install/install_sources_windows.md +++ /dev/null @@ -1,320 +0,0 @@ -# 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==1.0.5 --no-deps</b> -C:\> <b>pip3 install keras_preprocessing==1.0.3 --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`. |