Build from source on Windows

Build a TensorFlow pip package from source and install it on Windows.

Setup for Windows

Install the following build tools to configure your Windows development environment.

Install Python and the TensorFlow package dependencies

Install a Python 3.5.x or Python 3.6.x 64-bit release for Windows. Select pip as an optional feature and add it to your %PATH% environmental variable.

Install the TensorFlow pip package dependencies:

pip3 install six numpy wheel
pip3 install keras_applications==1.0.6 --no-deps
pip3 install keras_preprocessing==1.0.5 --no-deps

The dependencies are listed in the setup.py file under REQUIRED_PACKAGES.

Install Bazel

Install Bazel 0.24.1, the build tool used to compile TensorFlow. Set up Bazel to build C++.

Add the location of the Bazel executable to your %PATH% environment variable.

Ensure you install Bazel 0.23.0 or lower.

Install MSYS2

Install MSYS2 for the bin tools needed to build TensorFlow. If MSYS2 is installed to C:\msys64, add C:\msys64\usr\bin to your %PATH% environment variable. Then, using cmd.exe, run:

pacman -S git patch unzip

Install Visual C++ Build Tools 2017

Install the Visual C++ build tools 2017. This comes with Visual Studio 2017 but can be installed separately:

1. Go to the Visual Studio downloads,
2. Select Redistributables and Build Tools,
3. Download and install:
   • Microsoft Visual C++ 2017 Redistributable
   • Microsoft Build Tools 2017

Install GPU support (optional)

See the Windows GPU support guide to install the drivers and additional software required to run TensorFlow on a GPU.

Download the TensorFlow source code

Use Git to clone the TensorFlow repository (git is installed with MSYS2):

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow

The repo defaults to the master development branch. You can also checkout a release branch to build:

git checkout branch_name  # r1.9, r1.10, etc.

Configure the build

Configure your system build by running the following at the root of your TensorFlow source tree:

python ./configure.py

This script prompts you for the location of TensorFlow dependencies and asks for additional build configuration options (compiler flags, for example). The following shows a sample run of python ./configure.py (your session may differ):

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

Configuration options

For GPU support, specify the versions of CUDA and cuDNN. If your system has multiple versions of CUDA or cuDNN installed, explicitly set the version instead of relying on the default. ./configure.py creates symbolic links to your system's CUDA libraries—so if you update your CUDA library paths, this configuration step must be run again before building.

Build the pip package

TensorFlow 2.x

tensorflow:master repo has been updated to build 2.x by default. Install Bazel and use bazel build to create the TensorFlow package.

bazel build //tensorflow/tools/pip_package:build_pip_package

TensorFlow 1.x

To build the 1.x version of TensorFlow from master, use bazel build --config=v1 to create a TensorFlow 1.x package.

bazel build --config=v1 //tensorflow/tools/pip_package:build_pip_package

CPU-only

Use bazel to make the TensorFlow package builder with CPU-only support:

bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package

GPU support

To make the TensorFlow package builder with GPU support:

bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package

Bazel build options

Use this option when building to avoid issue with package creation: https://github.com/tensorflow/tensorflow/issues/22390

--define=no_tensorflow_py_deps=true

See the Bazel command-line reference for build options.

Building TensorFlow from source can use a lot of RAM. If your system is memory-constrained, limit Bazel's RAM usage with: --local_ram_resources=2048.

If building with GPU support, add --copt=-nvcc_options=disable-warnings to suppress nvcc warning messages.

Build the package

The bazel build command creates an executable named build_pip_package—this is the program that builds the pip package. For example, the following builds a .whl package in the C:/tmp/tensorflow_pkg directory:

bazel-bin\tensorflow\tools\pip_package\build_pip_package C:/tmp/tensorflow_pkg

Although it is possible to build both CUDA and non-CUDA configs under the same source tree, we recommend running bazel clean when switching between these two configurations in the same source tree.

Install the package

The filename of the generated .whl file depends on the TensorFlow version and your platform. Use pip3 install to install the package, for example:

pip3 install C:/tmp/tensorflow_pkg/tensorflow-version-cp36-cp36m-win_amd64.whl

Build using the MSYS shell

TensorFlow can also be built using the MSYS shell. Make the changes listed below, then follow the previous instructions for the Windows native command line (cmd.exe).

Disable MSYS path conversion

MSYS automatically converts arguments that look like Unix paths to Windows paths, and this doesn't work with bazel. (The label //foo/bar:bin is considered a Unix absolute path since it starts with a slash.)

export MSYS_NO_PATHCONV=1
export MSYS2_ARG_CONV_EXCL="*"

Set your PATH

Add the Bazel and Python installation directories to your $PATH environmental variable. If Bazel is installed to C:\tools\bazel.exe, and Python to C:\Python36\python.exe, set your PATH with:

# Use Unix-style with ':' as separator
export PATH="/c/tools:$PATH"
export PATH="/c/Python36:$PATH"

For GPU support, add the CUDA and cuDNN bin directories to your $PATH:

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"

Tested build configurations

CPU

GPU