From 4dc13f1aa7bdfb9fc21fc0fc4961c69cb8cb1d20 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 13 Aug 2018 02:09:37 -0700 Subject: Add documentation for TensorFlow Windows build with Bazel PiperOrigin-RevId: 208447033 --- .../docs_src/install/install_sources_windows.md | 320 +++++++++++++++++++++ tensorflow/docs_src/install/install_windows.md | 2 + tensorflow/docs_src/install/leftnav_files | 1 + 3 files changed, 323 insertions(+) create mode 100644 tensorflow/docs_src/install/install_sources_windows.md (limited to 'tensorflow/docs_src') diff --git a/tensorflow/docs_src/install/install_sources_windows.md b/tensorflow/docs_src/install/install_sources_windows.md new file mode 100644 index 0000000000..a1da122317 --- /dev/null +++ b/tensorflow/docs_src/install/install_sources_windows.md @@ -0,0 +1,320 @@ +# 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) + + + +### 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`: + +
+C:\> pacman -S git patch unzip
+
+ + + +### 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 Redistributables and Build Tools section. + * Find Microsoft Build Tools 2015 Update 3 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. + + + +### 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. + + + +### 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: + +
+C:\> pip3 install six numpy wheel
+C:\> pip3 install keras_applications==1.0.4 --no-deps
+C:\> pip3 install keras_preprocessing==1.0.2 --no-deps
+
+ + + +### 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. + +
+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%
+
+ +## 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: + +
C:\> git clone https://github.com/tensorflow/tensorflow.git
+ +The preceding git clone 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: + +
+C:\> cd tensorflow
+C:\> git checkout Branch # where Branch is the desired branch
+
+ +For example, to work with the `r1.11` release instead of the master release, +issue the following command: + +
C:\> git checkout r1.11
+ +Next, you must now configure the installation. + +## Configure the installation + +The root of the source tree contains a python script named configure.py. +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: + +
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]:
+
+ +Here is an example execution of the `configure.py` script. Note that your own input +will likely differ from our sample input: + +
+C:\> cd tensorflow  # cd to the top-level directory created
+C:\tensorflow> python ./configure.py
+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]: Y
+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]: 7.0
+
+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]: C:\tools\cuda
+
+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]: 3.7
+
+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
+
+ +## Build the pip package + +### CPU-only support + +To build a pip package for TensorFlow with CPU-only support: + +
+C:\tensorflow> bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+
+ +### GPU support + +To build a pip package for TensorFlow with GPU support: + +
+C:\tensorflow> bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
+
+ +**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: + +
+C:\tensorflow> bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/tmp/tensorflow_pkg
+
+ +## 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: + +
+C:\tensorflow> pip3 install C:/tmp/tensorflow_pkg/tensorflow-1.11.0rc0-cp36-cp36m-win_amd64.whl
+
+ +## 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: + +
$ python
+ +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: + +
Hello, TensorFlow!
+ +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. + + ### 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 -- cgit v1.2.3