site/en/install/source_windows.md
Build a TensorFlow pip package from the source and install it on Windows.
Note: We already provide well-tested, pre-built TensorFlow packages for Windows systems.
Install the following build tools to configure your Windows development environment.
Install a
Python 3.9+ 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:
<pre class="devsite-click-to-copy"> <code class="devsite-terminal tfo-terminal-windows">pip3 install -U pip</code> <code class="devsite-terminal tfo-terminal-windows">pip3 install -U six numpy wheel packaging</code> <code class="devsite-terminal tfo-terminal-windows">pip3 install -U keras_preprocessing --no-deps</code> </pre>The dependencies are listed in the
<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py" class="external"><code>setup.py</code></a>
file under REQUIRED_PACKAGES.
Install Bazel, the build tool used to compile TensorFlow. For Bazel version, see the tested build configurations for Windows. Configure Bazel to <a href="https://docs.bazel.build/versions/master/windows.html#build-c" class="external">build C++</a>.
Add the location of the Bazel executable to your %PATH% environment variable.
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:
Note: Clang will be the preferred compiler to build TensorFlow CPU wheels on the Windows Platform starting with TF 2.16.1 The currently supported version is LLVM/clang 17.0.6.
Note: To build with Clang on Windows, it is required to install both LLVM and Visual C++ Build tools as although Windows uses clang-cl.exe as the compiler, Visual C++ Build tools are needed to link to Visual C++ libraries
Install the Visual C++ build tools 2022. This comes with Visual Studio Community 2022 but can be installed separately:
Note: TensorFlow is tested against the Visual Studio Community 2022.
See the Windows GPU support guide to install the drivers and additional software required to run TensorFlow on a GPU.
Note: GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin
Use Git to clone the
TensorFlow repository
(git is installed with MSYS2):
The repo defaults to the master development branch. You can also check out a
release branch
to build:
Key Point: If you're having build problems on the latest development branch, try a release branch that is known to work.
Run the following commands before running the build command to avoid issues with package creation:
(If the below commands were set up while installing the packages, please ignore them). Run set to check if all the paths were set correctly, run echo %Environmental Variable% e.g., echo %BAZEL_VC% to check the path set up for a specific Environmental Variable
Python path set up issue tensorflow:issue#59943,tensorflow:issue#9436,tensorflow:issue#60083
<pre class="devsite-terminal tfo-terminal-windows devsite-click-to-copy"> set PATH=path/to/python;%PATH% # [e.g. (C:/Python311)] set PATH=path/to/python/Scripts;%PATH% # [e.g. (C:/Python311/Scripts)] set PYTHON_BIN_PATH=path/to/python_virtualenv/Scripts/python.exe set PYTHON_LIB_PATH=path/to/python virtualenv/lib/site-packages set PYTHON_DIRECTORY=path/to/python_virtualenv/Scripts </pre>Bazel/MSVC/CLANG path set up issue tensorflow:issue#54578
<pre class="devsite-terminal tfo-terminal-windows devsite-click-to-copy"> set BAZEL_SH=C:/msys64/usr/bin/bash.exe set BAZEL_VS=C:/Program Files/Microsoft Visual Studio/2022/BuildTools set BAZEL_VC=C:/Program Files/Microsoft Visual Studio/2022/BuildTools/VC set Bazel_LLVM=C:/Program Files/LLVM (explicitly tell Bazel where LLVM is installed by BAZEL_LLVM, needed while using CLANG) set PATH=C:/Program Files/LLVM/bin;%PATH% (Optional, needed while using CLANG as Compiler) </pre>TensorFlow builds are configured by the .bazelrc file in the repository's
root directory. The ./configure or ./configure.py scripts can be used to
adjust common settings.
If you need to change the configuration, run the ./configure script from
the repository's root directory.
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):
Found possible Python library paths: C:\Python311\lib\site-packages Please input the desired Python library path to use. Default is [C:\Python311\lib\site-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow.
WARNING: Cannot build with CUDA support on Windows. Starting in TF 2.11, CUDA build is not supported for Windows. To use TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2.
Do you want to use Clang to build TensorFlow? [Y/n]: Add "--config=win_clang" to compile TensorFlow with CLANG.
Please specify the path to clang executable. [Default is C:\Program Files\LLVM\bin\clang.EXE]:
You have Clang 17.0.6 installed.
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.
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details. --config=mkl # Build with MKL support. --config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL). --config=monolithic # Config for mostly static monolithic build. --config=numa # Build with NUMA support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. --config=v1 # Build with TensorFlow 1 API instead of TF 2 API. Preconfigured Bazel build configs to DISABLE default on features: --config=nogcp # Disable GCP support. --config=nonccl # Disable NVIDIA NCCL support. </pre>
</section>The pip package is built in two steps. A bazel build command creates a
"package-builder" program. You then run the package-builder to create the
package.
tensorflow:master repo has been updated to build 2.x by default.
Install Bazel and use
bazel build to create the TensorFlow package-builder.
Use bazel to make the TensorFlow package builder with CPU-only support:
Use --config=win_clang to build TenorFlow with the CLANG Compiler:
Note: GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin
To make the TensorFlow package builder with GPU support:
<pre class="devsite-terminal tfo-terminal-windows devsite-click-to-copy"> bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package </pre>Commands to clean the bazel cache to resolve errors due to invalid or outdated cached data, bazel clean with --expunge flag removes files permanently
<pre class="devsite-terminal tfo-terminal-windows devsite-click-to-copy"> bazel clean bazel clean --expunge </pre>Use this option when building to avoid issues with package creation: tensorflow:issue#22390
<pre class="devsite-terminal tfo-terminal-windows devsite-click-to-copy"> --define=no_tensorflow_py_deps=true </pre>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.
To build a pip package, you need to specify the --repo_env=WHEEL_NAME flag. Depending on the provided name, the package will be created. For example:
To build tensorflow CPU package:
<pre class="devsite-terminal devsite-click-to-copy"> bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu </pre>To build nightly package, set tf_nightly instead of tensorflow, e.g.
to build CPU nightly package:
As a result, generated wheel will be located in
<pre class="devsite-terminal devsite-click-to-copy"> bazel-bin/tensorflow/tools/pip_package/wheel_house/ </pre>The filename of the generated .whl file depends on the TensorFlow version and
your platform. Use pip install to install the package, for example:
Success: TensorFlow is now installed.
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).
MSYS automatically converts arguments that look like Unix paths to Windows
paths, and this doesn't work with bazel. (The label //path/to:bin is
considered a Unix absolute path since it starts with a slash.)
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:\Python\python.exe, set your PATH with:
For GPU support, add the CUDA and cuDNN bin directories to your $PATH:
Note: Starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin
<a name="tested_build_configurations"></a>
Note: GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin
<table> <tr><th>Version</th><th>Python version</th><th>Compiler</th><th>Build tools</th><th>cuDNN</th><th>CUDA</th></tr> <tr><td>tensorflow_gpu-2.10.0</td><td>3.7-3.10</td><td>MSVC 2019</td><td>Bazel 5.1.1</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.9.0</td><td>3.7-3.10</td><td>MSVC 2019</td><td>Bazel 5.0.0</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.8.0</td><td>3.7-3.10</td><td>MSVC 2019</td><td>Bazel 4.2.1</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.7.0</td><td>3.7-3.9</td><td>MSVC 2019</td><td>Bazel 3.7.2</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.6.0</td><td>3.6-3.9</td><td>MSVC 2019</td><td>Bazel 3.7.2</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.5.0</td><td>3.6-3.9</td><td>MSVC 2019</td><td>Bazel 3.7.2</td><td>8.1</td><td>11.2</td></tr> <tr><td>tensorflow_gpu-2.4.0</td><td>3.6-3.8</td><td>MSVC 2019</td><td>Bazel 3.1.0</td><td>8.0</td><td>11.0</td></tr> <tr><td>tensorflow_gpu-2.3.0</td><td>3.5-3.8</td><td>MSVC 2019</td><td>Bazel 3.1.0</td><td>7.6</td><td>10.1</td></tr> <tr><td>tensorflow_gpu-2.2.0</td><td>3.5-3.8</td><td>MSVC 2019</td><td>Bazel 2.0.0</td><td>7.6</td><td>10.1</td></tr> <tr><td>tensorflow_gpu-2.1.0</td><td>3.5-3.7</td><td>MSVC 2019</td><td>Bazel 0.27.1-0.29.1</td><td>7.6</td><td>10.1</td></tr> <tr><td>tensorflow_gpu-2.0.0</td><td>3.5-3.7</td><td>MSVC 2017</td><td>Bazel 0.26.1</td><td>7.4</td><td>10</td></tr> <tr><td>tensorflow_gpu-1.15.0</td><td>3.5-3.7</td><td>MSVC 2017</td><td>Bazel 0.26.1</td><td>7.4</td><td>10</td></tr> <tr><td>tensorflow_gpu-1.14.0</td><td>3.5-3.7</td><td>MSVC 2017</td><td>Bazel 0.24.1-0.25.2</td><td>7.4</td><td>10</td></tr> <tr><td>tensorflow_gpu-1.13.0</td><td>3.5-3.7</td><td>MSVC 2015 update 3</td><td>Bazel 0.19.0-0.21.0</td><td>7.4</td><td>10</td></tr> <tr><td>tensorflow_gpu-1.12.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Bazel 0.15.0</td><td>7.2</td><td>9.0</td></tr> <tr><td>tensorflow_gpu-1.11.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Bazel 0.15.0</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.10.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.9.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.8.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.7.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.6.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.5.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow_gpu-1.4.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>6</td><td>8</td></tr> <tr><td>tensorflow_gpu-1.3.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>6</td><td>8</td></tr> <tr><td>tensorflow_gpu-1.2.0</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>5.1</td><td>8</td></tr> <tr><td>tensorflow_gpu-1.1.0</td><td>3.5</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>5.1</td><td>8</td></tr> <tr><td>tensorflow_gpu-1.0.0</td><td>3.5</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>5.1</td><td>8</td></tr> </table>