Build from source

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.

Setup for Linux and macOS

Install the following build tools to configure your development environment.

Install Python and the TensorFlow package dependencies

Ubuntu

sudo apt install python3-dev python3-pip

macOS

Requires Xcode 9.2 or later.

Install using the Homebrew package manager:

brew install python

Install the TensorFlow pip package dependencies (if using a virtual environment, omit the --user argument):

pip install -U --user pip numpy wheel packaging requests opt_einsum
pip install -U --user keras_preprocessing --no-deps

Install Bazel

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 16.

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:

sudo apt-get update && sudo apt-get install -y llvm-16 clang-16

Alternatively, you can download and unpack the pre-built Clang + LLVM 16.

Below is an example of steps you can take to set up the downloaded Clang + LLVM 16 binaries:

  1. Change to the desired destination directory:

    ```cd <desired directory>```

    1. Load and extract an archive file...(suitable to your architecture): 

      
wget https://github.com/llvm/llvm-project/releases/download/llvmorg-16.0.0/clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz

tar -xvf clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04.tar.xz

    2. Check the obtained Clang + LLVM 16 binaries version: 

      
./clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/clang-16 --version

    3. Directory /clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/bin/clang-16 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.

    Install GPU support (optional, Linux only)

    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.

    Download the TensorFlow source code

    Use Git to clone the TensorFlow repository:

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

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

    git checkout branch_name  # r2.2, r2.3, etc.

    Configure the 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.

    ./configure

    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.

    Sample session

    The following shows a sample run of ./configure script (your session may differ):

    Configuration options

    GPU support

    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.

    Optimizations

    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.

    Preconfigured configurations

    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.

    Build and install the pip package

    The pip package is build in two steps. A bazel build commands creates a "package-builder" program. You then run the package-builder to create the package.

    Build the package-builder

    Use bazel build to create the TensorFlow 2.x package-builder:

    bazel build [--config=option] //tensorflow/tools/pip_package:build_pip_package

    Bazel build options

    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.

    Build the package

    The bazel build command creates an executable named build_pip_package—this is the program that builds the pip package. Run the executable as shown below to build a .whl package in the /tmp/tensorflow_pkg directory.

    To build from a release branch:

    ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

    To build from master, use --nightly_flag to get the right dependencies:

    ./bazel-bin/tensorflow/tools/pip_package/build_pip_package --nightly_flag /tmp/tensorflow_pkg

    Although it is possible to build both CUDA and non-CUDA configurations under the same source tree, it's recommended to run 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 pip install to install the package, for example:

    pip install /tmp/tensorflow_pkg/tensorflow-version-tags.whl

    Docker Linux builds

    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.

    CPU-only

    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:

    docker pull tensorflow/tensorflow:devel
docker run -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
    tensorflow/tensorflow:devel bash

git pull  # within the container, download the latest source code

    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:

    docker run -it -w /tensorflow -v /path/to/tensorflow:/tensorflow -v $PWD:/mnt \
    -e HOST_PERMS="\\((id -u):\\)(id -g)" tensorflow/tensorflow:devel bash

    With the source tree set up, build the TensorFlow package within the container's virtual environment:

    1. Optional: Configure the build—this prompts the user to answer build configuration questions.
    2. Build the tool used to create the pip package.
    3. Run the tool to create the pip package.
    4. Adjust the ownership permissions of the file for outside the container.
    ./configure  # if necessary

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

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt  # create package

chown $HOST_PERMS /mnt/tensorflow-version-tags.whl

    Install and verify the package within the container:

    pip uninstall tensorflow  # remove current version

pip install /mnt/tensorflow-version-tags.whl
cd /tmp  # don't import from source directory
python -c "import tensorflow as tf; print(tf.__version__)"

    On your host machine, the TensorFlow pip package is in the current directory (with host user permissions): ./tensorflow-version-tags.whl

    GPU support

    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:

    docker pull tensorflow/tensorflow:devel-gpu
docker run --gpus all -it -w /tensorflow -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
    tensorflow/tensorflow:devel-gpu bash
git pull  # within the container, download the latest source code

    Then, within the container's virtual environment, build the TensorFlow package with GPU support:

    ./configure  # if necessary

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

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /mnt  # create package

chown $HOST_PERMS /mnt/tensorflow-version-tags.whl

    Install and verify the package within the container and check for a GPU:

    pip uninstall tensorflow  # remove current version

pip install /mnt/tensorflow-version-tags.whl
cd /tmp  # don't import from source directory
python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"

    Tested build configurations

    Linux

    CPU

    VersionPython versionCompilerBuild tools
    tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.0
    tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.0
    tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.0
    tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.0
    tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.0
    tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.1
    tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.0
    tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.1
    tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.2
    tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.2
    tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.2
    tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.0
    tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.0
    tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.0
    tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.1
    tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
    tensorflow-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.1
    tensorflow-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.1
    tensorflow-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.2
    tensorflow-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
    tensorflow-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
    tensorflow-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.0
    tensorflow-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.0
    tensorflow-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
    tensorflow-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.10.0
    tensorflow-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.0
    tensorflow-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.0
    tensorflow-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.4
    tensorflow-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
    tensorflow-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.5
    tensorflow-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2
    tensorflow-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.2

    GPU

    VersionPython versionCompilerBuild toolscuDNNCUDA
    tensorflow-2.15.03.9-3.11Clang 16.0.0Bazel 6.1.08.812.2
    tensorflow-2.14.03.9-3.11Clang 16.0.0Bazel 6.1.08.711.8
    tensorflow-2.13.03.8-3.11Clang 16.0.0Bazel 5.3.08.611.8
    tensorflow-2.12.03.8-3.11GCC 9.3.1Bazel 5.3.08.611.8
    tensorflow-2.11.03.7-3.10GCC 9.3.1Bazel 5.3.08.111.2
    tensorflow-2.10.03.7-3.10GCC 9.3.1Bazel 5.1.18.111.2
    tensorflow-2.9.03.7-3.10GCC 9.3.1Bazel 5.0.08.111.2
    tensorflow-2.8.03.7-3.10GCC 7.3.1Bazel 4.2.18.111.2
    tensorflow-2.7.03.7-3.9GCC 7.3.1Bazel 3.7.28.111.2
    tensorflow-2.6.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
    tensorflow-2.5.03.6-3.9GCC 7.3.1Bazel 3.7.28.111.2
    tensorflow-2.4.03.6-3.8GCC 7.3.1Bazel 3.1.08.011.0
    tensorflow-2.3.03.5-3.8GCC 7.3.1Bazel 3.1.07.610.1
    tensorflow-2.2.03.5-3.8GCC 7.3.1Bazel 2.0.07.610.1
    tensorflow-2.1.02.7, 3.5-3.7GCC 7.3.1Bazel 0.27.17.610.1
    tensorflow-2.0.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
    tensorflow_gpu-1.15.02.7, 3.3-3.7GCC 7.3.1Bazel 0.26.17.410.0
    tensorflow_gpu-1.14.02.7, 3.3-3.7GCC 4.8Bazel 0.24.17.410.0
    tensorflow_gpu-1.13.12.7, 3.3-3.7GCC 4.8Bazel 0.19.27.410.0
    tensorflow_gpu-1.12.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
    tensorflow_gpu-1.11.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
    tensorflow_gpu-1.10.02.7, 3.3-3.6GCC 4.8Bazel 0.15.079
    tensorflow_gpu-1.9.02.7, 3.3-3.6GCC 4.8Bazel 0.11.079
    tensorflow_gpu-1.8.02.7, 3.3-3.6GCC 4.8Bazel 0.10.079
    tensorflow_gpu-1.7.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
    tensorflow_gpu-1.6.02.7, 3.3-3.6GCC 4.8Bazel 0.9.079
    tensorflow_gpu-1.5.02.7, 3.3-3.6GCC 4.8Bazel 0.8.079
    tensorflow_gpu-1.4.02.7, 3.3-3.6GCC 4.8Bazel 0.5.468
    tensorflow_gpu-1.3.02.7, 3.3-3.6GCC 4.8Bazel 0.4.568
    tensorflow_gpu-1.2.02.7, 3.3-3.6GCC 4.8Bazel 0.4.55.18
    tensorflow_gpu-1.1.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18
    tensorflow_gpu-1.0.02.7, 3.3-3.6GCC 4.8Bazel 0.4.25.18

    macOS

    CPU

    VersionPython versionCompilerBuild tools
    tensorflow-2.15.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
    tensorflow-2.14.03.9-3.11Clang from xcode 10.15Bazel 6.1.0
    tensorflow-2.13.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
    tensorflow-2.12.03.8-3.11Clang from xcode 10.15Bazel 5.3.0
    tensorflow-2.11.03.7-3.10Clang from xcode 10.14Bazel 5.3.0
    tensorflow-2.10.03.7-3.10Clang from xcode 10.14Bazel 5.1.1
    tensorflow-2.9.03.7-3.10Clang from xcode 10.14Bazel 5.0.0
    tensorflow-2.8.03.7-3.10Clang from xcode 10.14Bazel 4.2.1
    tensorflow-2.7.03.7-3.9Clang from xcode 10.11Bazel 3.7.2
    tensorflow-2.6.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
    tensorflow-2.5.03.6-3.9Clang from xcode 10.11Bazel 3.7.2
    tensorflow-2.4.03.6-3.8Clang from xcode 10.3Bazel 3.1.0
    tensorflow-2.3.03.5-3.8Clang from xcode 10.1Bazel 3.1.0
    tensorflow-2.2.03.5-3.8Clang from xcode 10.1Bazel 2.0.0
    tensorflow-2.1.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
    tensorflow-2.0.02.7, 3.5-3.7Clang from xcode 10.1Bazel 0.27.1
    tensorflow-2.0.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
    tensorflow-1.15.02.7, 3.3-3.7Clang from xcode 10.1Bazel 0.26.1
    tensorflow-1.14.02.7, 3.3-3.7Clang from xcodeBazel 0.24.1
    tensorflow-1.13.12.7, 3.3-3.7Clang from xcodeBazel 0.19.2
    tensorflow-1.12.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
    tensorflow-1.11.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
    tensorflow-1.10.02.7, 3.3-3.6Clang from xcodeBazel 0.15.0
    tensorflow-1.9.02.7, 3.3-3.6Clang from xcodeBazel 0.11.0
    tensorflow-1.8.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
    tensorflow-1.7.02.7, 3.3-3.6Clang from xcodeBazel 0.10.1
    tensorflow-1.6.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
    tensorflow-1.5.02.7, 3.3-3.6Clang from xcodeBazel 0.8.1
    tensorflow-1.4.02.7, 3.3-3.6Clang from xcodeBazel 0.5.4
    tensorflow-1.3.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
    tensorflow-1.2.02.7, 3.3-3.6Clang from xcodeBazel 0.4.5
    tensorflow-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2
    tensorflow-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.2

    GPU

    VersionPython versionCompilerBuild toolscuDNNCUDA
    tensorflow_gpu-1.1.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18
    tensorflow_gpu-1.0.02.7, 3.3-3.6Clang from xcodeBazel 0.4.25.18