site/en/install/source.md
Build a TensorFlow pip package from source and install it on Ubuntu Linux and macOS. While the instructions might work for other systems, it is only tested and supported for Ubuntu and macOS.
Note: Well-tested, pre-built TensorFlow packages for Linux and macOS systems are already provided.
Install the following build tools to configure your development environment.
Install the TensorFlow pip package dependencies (if using a virtual
environment, omit the --user argument):
Note: A pip version >19.0 is required to install the TensorFlow 2 .whl
package. Additional required dependencies are listed in the
<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py.tpl" class="external"><code>setup.py.tpl</code></a>
file under REQUIRED_PACKAGES.
To build TensorFlow, you will need to install Bazel.
Bazelisk is an easy way to install
Bazel and automatically downloads the correct Bazel version for TensorFlow. For
ease of use, add Bazelisk as the bazel executable in your PATH.
If Bazelisk is not available, you can manually install Bazel. Make sure to install the correct Bazel version from TensorFlow's .bazelversion file.
Clang is a C/C++/Objective-C compiler that is compiled in C++ based on LLVM. It is the default compiler to build TensorFlow starting with TensorFlow 2.13. The current supported version is LLVM/Clang 17.
LLVM Debian/Ubuntu nightly packages provide an automatic installation script and packages for manual installation on Linux. Make sure you run the following command if you manually add llvm apt repository to your package sources:
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal">sudo apt-get update && sudo apt-get install -y llvm-17 clang-17</code> </pre>Now that /usr/lib/llvm-17/bin/clang is the actual path to clang in this case.
Alternatively, you can download and unpack the pre-built Clang + LLVM 17.
Below is an example of steps you can take to set up the downloaded Clang + LLVM 17 binaries on Debian/Ubuntu operating systems:
Change to the desired destination directory: cd <desired directory>
Load and extract an archive file...(suitable to your architecture):
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal">wget https://github.com/llvm/llvm-project/releases/download/llvmorg-17.0.2/clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz </code> <code class="devsite-terminal">tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz </code> </pre>Copy the extracted contents (directories and files) to /usr (you may need
sudo permissions, and the correct directory may vary by distribution). This
effectively installs Clang and LLVM, and adds it to the path. You should not
have to replace anything, unless you have a previous installation, in which
case you should replace the files:
Check the obtained Clang + LLVM 17 binaries version:
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal">clang --version</code> </pre>Now that /usr/bin/clang is the actual path to your new clang. You can run
the ./configure script or manually set environment variables CC and
BAZEL_COMPILER to this path.
There is no GPU support for macOS.
Read the GPU support guide to install the drivers and additional software required to run TensorFlow on a GPU.
Note: It is easier to set up one of TensorFlow's GPU-enabled Docker images.
Use Git to clone the TensorFlow repository:
<pre class="devsite-click-to-copy"> <code class="devsite-terminal">git clone https://github.com/tensorflow/tensorflow.git</code> <code class="devsite-terminal">cd tensorflow</code> </pre>The repo defaults to the master development branch. You can also check out a
release branch
to build:
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.
Please run the ./configure script from the repository's root directory. This
script will prompt you for the location of TensorFlow dependencies and asks for
additional build configuration options (compiler flags, for example). Refer to
the Sample session section for details.
There is also a python version of this script, ./configure.py. If using a
virtual environment, python configure.py prioritizes paths
within the environment, whereas ./configure prioritizes paths outside
the environment. In both cases you can change the default.
The following shows a sample run of ./configure script (your
session may differ):
Found possible Python library paths: /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages Please input the desired Python library path to use. Default is [/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: No CUDA support will be enabled for TensorFlow.
Do you want to use Clang to build TensorFlow? [Y/n]: Clang will be used to compile TensorFlow.
Please specify the path to clang executable. [Default is /usr/lib/llvm-16/bin/clang]:
You have Clang 16.0.4 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n Not configuring the WORKSPACE for Android builds.
Do you wish to build TensorFlow with iOS support? [y/N]: n No iOS support will be enabled for TensorFlow.
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>For GPU support, set cuda=Y during configuration and specify the
versions of CUDA and cuDNN if required. Bazel will download CUDA and CUDNN
packages automatically or point to CUDA/CUDNN/NCCL redistributions on local file
system if required.
For GPU support, set cuda=Y during configuration and 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 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.
For compilation optimization flags, the default (-march=native) optimizes the
generated code for your machine's CPU type. However, if building TensorFlow for
a different CPU type, consider a more specific optimization flag. Check the
GCC manual
for examples.
There are some preconfigured build configs available that can be added to the
bazel build command, for example:
--config=dbg —Build with debug info. See
CONTRIBUTING.md
for details.--config=mkl —Support for the
Intel® MKL-DNN.--config=monolithic —Configuration for a mostly static, monolithic build.Refer to 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.
The official TensorFlow packages are built with a Clang toolchain that complies with the manylinux2014 package standard.
To build pip package, you need to specify --repo_env=WHEEL_NAME flag.
depending on the provided name, package will be created, e.g:
To build tensorflow CPU package:
<pre class="devsite-terminal devsite-click-to-copy"> bazel build //tensorflow/tools/pip_package:wheel --repo_env=USE_PYWRAP_RULES=1 --repo_env=WHEEL_NAME=tensorflow_cpu </pre>To build tensorflow GPU package:
<pre class="devsite-terminal devsite-click-to-copy"> bazel build //tensorflow/tools/pip_package:wheel --repo_env=USE_PYWRAP_RULES=1 --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=cuda_wheel </pre>To build tensorflow TPU package:
<pre class="devsite-terminal devsite-click-to-copy"> bazel build //tensorflow/tools/pip_package:wheel --repo_env=USE_PYWRAP_RULES=1 --repo_env=WHEEL_NAME=tensorflow_tpu --config=tpu </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's Docker development images are an easy way to set up an environment to build Linux packages from source. These images already contain the source code and dependencies required to build TensorFlow. Go to the TensorFlow Docker guide for installation instructions and the list of available image tags.
The following example uses the :devel image to build a CPU-only package from
the latest TensorFlow source code. Check the Docker guide for
available TensorFlow -devel tags.
Download the latest development image and start a Docker container that you'll use to build the pip package:
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal">docker pull tensorflow/tensorflow<var>:devel</var></code> <code class="devsite-terminal">docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \ tensorflow/tensorflow<var>:devel</var> bash</code> <code class="devsite-terminal tfo-terminal-root">git pull # within the container, download the latest source code</code> </pre>The above docker run command starts a shell in the /tensorflow_src
directory—the root of the source tree. It mounts the host's current directory in
the container's /mnt directory, and passes the host user's information to the
container through an environmental variable (used to set permissions—Docker can
make this tricky).
Alternatively, to build a host copy of TensorFlow within a container, mount the
host source tree at the container's /tensorflow directory:
With the source tree set up, build the TensorFlow package within the container's virtual environment:
Install and verify the package within the container:
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal tfo-terminal-root">pip uninstall tensorflow # remove current version</code> <code class="devsite-terminal tfo-terminal-root">pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-<var>version</var>-<var>tags</var>.whl</code> <code class="devsite-terminal tfo-terminal-root">cd /tmp # don't import from source directory</code> <code class="devsite-terminal tfo-terminal-root">python -c "import tensorflow as tf; print(tf.__version__)"</code> </pre>Success: TensorFlow is now installed.
On your host machine, the TensorFlow pip package is in the current directory (with host user permissions): <code>./tensorflow-<var>version</var>-<var>tags</var>.whl</code>
Note: Starting from Tensorflow v.2.18.0 the wheels can be built from source on a machine without GPUs and without NVIDIA driver installed.
Docker is the easiest way to build GPU support for TensorFlow since the host machine only requires the NVIDIA® driver (the NVIDIA® CUDA® Toolkit doesn't have to be installed). Refer to the GPU support guide and the TensorFlow Docker guide to set up nvidia-docker (Linux only).
The following example downloads the TensorFlow :devel-gpu image and uses
nvidia-docker to run the GPU-enabled container. This development image is
configured to build a pip package with GPU support:
Then, within the container's virtual environment, build the TensorFlow package with GPU support:
<pre class="devsite-disable-click-to-copy prettyprint lang-bsh"> <code class="devsite-terminal tfo-terminal-root">./configure # if necessary</code> <code class="devsite-terminal tfo-terminal-root"> bazel build //tensorflow/tools/pip_package:wheel \ --repo_env=USE_PYWRAP_RULES=1 --repo_env=WHEEL_NAME=tensorflow --config=cuda \ --config=cuda_wheel --config=opt </code> <code class="devsite-terminal tfo-terminal-root">chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-<var>version</var>-<var>tags</var>.whl</code> </pre>Install and verify the package within the container and check for a GPU:
<pre class="prettyprint lang-bsh"> <code class="devsite-terminal tfo-terminal-root">pip uninstall tensorflow # remove current version</code> <code class="devsite-terminal tfo-terminal-root">pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-<var>version</var>-<var>tags</var>.whl</code> <code class="devsite-terminal tfo-terminal-root">cd /tmp # don't import from source directory</code> <code class="devsite-terminal tfo-terminal-root">python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"</code> </pre>Success: TensorFlow is now installed.
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