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
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.
Install Clang (recommended, Linux only)
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:
sudo apt-get update && sudo apt-get install -y llvm-17 clang-17
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):
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
tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
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:cp -r clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04/* /usr
Check the obtained Clang + LLVM 17 binaries version:
clang --version
Now that
/usr/bin/clang
is the actual path to your new clang. You can run the./configure
script or manually set environment variablesCC
andBAZEL_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
from v.2.18.0
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.
before v.2.18.0
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
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
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:
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
To build tensorflow GPU package:
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=cuda_wheel
To build tensorflow TPU package:
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_tpu --config=tpu
To build nightly package, set tf_nightly
instead of tensorflow
, e.g.
to build CPU nightly package:
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly_cpu
As a result, generated wheel will be located in
bazel-bin/tensorflow/tools/pip_package/wheel_house/
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 bazel-bin/tensorflow/tools/pip_package/wheel_house/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:
- Optional: Configure the build—this prompts the user to answer build configuration questions.
- Build the pip package.
- Adjust the ownership permissions of the file for outside the container.
./configure # if necessary
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu --config=opt
`chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/tensorflow-version-tags.whl
Install and verify the package within the container:
pip uninstall tensorflow # remove current version
pip install bazel-bin/tensorflow/tools/pip_package/wheel_house/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 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --config=cuda_wheel --config=opt
chown $HOST_PERMS bazel-bin/tensorflow/tools/pip_package/wheel_house/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 bazel-bin/tensorflow/tools/pip_package/wheel_house/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
Version | Python version | Compiler | Build tools |
---|---|---|---|
tensorflow-2.18.0 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 |
tensorflow-2.17.0 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 |
tensorflow-2.16.1 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 |
tensorflow-2.15.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 |
tensorflow-2.14.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 |
tensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0 |
tensorflow-2.12.0 | 3.8-3.11 | GCC 9.3.1 | Bazel 5.3.0 |
tensorflow-2.11.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.3.0 |
tensorflow-2.10.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.1.1 |
tensorflow-2.9.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.0.0 |
tensorflow-2.8.0 | 3.7-3.10 | GCC 7.3.1 | Bazel 4.2.1 |
tensorflow-2.7.0 | 3.7-3.9 | GCC 7.3.1 | Bazel 3.7.2 |
tensorflow-2.6.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 |
tensorflow-2.5.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 |
tensorflow-2.4.0 | 3.6-3.8 | GCC 7.3.1 | Bazel 3.1.0 |
tensorflow-2.3.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 3.1.0 |
tensorflow-2.2.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 2.0.0 |
tensorflow-2.1.0 | 2.7, 3.5-3.7 | GCC 7.3.1 | Bazel 0.27.1 |
tensorflow-2.0.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 |
tensorflow-1.15.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 |
tensorflow-1.14.0 | 2.7, 3.3-3.7 | GCC 4.8 | Bazel 0.24.1 |
tensorflow-1.13.1 | 2.7, 3.3-3.7 | GCC 4.8 | Bazel 0.19.2 |
tensorflow-1.12.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 |
tensorflow-1.11.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 |
tensorflow-1.10.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 |
tensorflow-1.9.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.11.0 |
tensorflow-1.8.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.10.0 |
tensorflow-1.7.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.10.0 |
tensorflow-1.6.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.9.0 |
tensorflow-1.5.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.8.0 |
tensorflow-1.4.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.5.4 |
tensorflow-1.3.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 |
tensorflow-1.2.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 |
tensorflow-1.1.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 |
tensorflow-1.0.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 |
GPU
Version | Python version | Compiler | Build tools | cuDNN | CUDA |
---|---|---|---|---|---|
tensorflow-2.18.0 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 | 9.3 | 12.5 |
tensorflow-2.17.0 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 | 8.9 | 12.3 |
tensorflow-2.16.1 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 | 8.9 | 12.3 |
tensorflow-2.15.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.9 | 12.2 |
tensorflow-2.14.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.7 | 11.8 |
tensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0 | 8.6 | 11.8 |
tensorflow-2.12.0 | 3.8-3.11 | GCC 9.3.1 | Bazel 5.3.0 | 8.6 | 11.8 |
tensorflow-2.11.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.3.0 | 8.1 | 11.2 |
tensorflow-2.10.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.1.1 | 8.1 | 11.2 |
tensorflow-2.9.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.0.0 | 8.1 | 11.2 |
tensorflow-2.8.0 | 3.7-3.10 | GCC 7.3.1 | Bazel 4.2.1 | 8.1 | 11.2 |
tensorflow-2.7.0 | 3.7-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
tensorflow-2.6.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
tensorflow-2.5.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
tensorflow-2.4.0 | 3.6-3.8 | GCC 7.3.1 | Bazel 3.1.0 | 8.0 | 11.0 |
tensorflow-2.3.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 3.1.0 | 7.6 | 10.1 |
tensorflow-2.2.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 2.0.0 | 7.6 | 10.1 |
tensorflow-2.1.0 | 2.7, 3.5-3.7 | GCC 7.3.1 | Bazel 0.27.1 | 7.6 | 10.1 |
tensorflow-2.0.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 | 7.4 | 10.0 |
tensorflow_gpu-1.15.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 | 7.4 | 10.0 |
tensorflow_gpu-1.14.0 | 2.7, 3.3-3.7 | GCC 4.8 | Bazel 0.24.1 | 7.4 | 10.0 |
tensorflow_gpu-1.13.1 | 2.7, 3.3-3.7 | GCC 4.8 | Bazel 0.19.2 | 7.4 | 10.0 |
tensorflow_gpu-1.12.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 | 7 | 9 |
tensorflow_gpu-1.11.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 | 7 | 9 |
tensorflow_gpu-1.10.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.15.0 | 7 | 9 |
tensorflow_gpu-1.9.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.11.0 | 7 | 9 |
tensorflow_gpu-1.8.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.10.0 | 7 | 9 |
tensorflow_gpu-1.7.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.9.0 | 7 | 9 |
tensorflow_gpu-1.6.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.9.0 | 7 | 9 |
tensorflow_gpu-1.5.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.8.0 | 7 | 9 |
tensorflow_gpu-1.4.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.5.4 | 6 | 8 |
tensorflow_gpu-1.3.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | 6 | 8 |
tensorflow_gpu-1.2.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.5 | 5.1 | 8 |
tensorflow_gpu-1.1.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | 5.1 | 8 |
tensorflow_gpu-1.0.0 | 2.7, 3.3-3.6 | GCC 4.8 | Bazel 0.4.2 | 5.1 | 8 |
macOS
CPU
Version | Python version | Compiler | Build tools |
---|---|---|---|
tensorflow-2.16.1 | 3.9-3.12 | Clang from Xcode 13.6 | Bazel 6.5.0 |
tensorflow-2.15.0 | 3.9-3.11 | Clang from xcode 10.15 | Bazel 6.1.0 |
tensorflow-2.14.0 | 3.9-3.11 | Clang from xcode 10.15 | Bazel 6.1.0 |
tensorflow-2.13.0 | 3.8-3.11 | Clang from xcode 10.15 | Bazel 5.3.0 |
tensorflow-2.12.0 | 3.8-3.11 | Clang from xcode 10.15 | Bazel 5.3.0 |
tensorflow-2.11.0 | 3.7-3.10 | Clang from xcode 10.14 | Bazel 5.3.0 |
tensorflow-2.10.0 | 3.7-3.10 | Clang from xcode 10.14 | Bazel 5.1.1 |
tensorflow-2.9.0 | 3.7-3.10 | Clang from xcode 10.14 | Bazel 5.0.0 |
tensorflow-2.8.0 | 3.7-3.10 | Clang from xcode 10.14 | Bazel 4.2.1 |
tensorflow-2.7.0 | 3.7-3.9 | Clang from xcode 10.11 | Bazel 3.7.2 |
tensorflow-2.6.0 | 3.6-3.9 | Clang from xcode 10.11 | Bazel 3.7.2 |
tensorflow-2.5.0 | 3.6-3.9 | Clang from xcode 10.11 | Bazel 3.7.2 |
tensorflow-2.4.0 | 3.6-3.8 | Clang from xcode 10.3 | Bazel 3.1.0 |
tensorflow-2.3.0 | 3.5-3.8 | Clang from xcode 10.1 | Bazel 3.1.0 |
tensorflow-2.2.0 | 3.5-3.8 | Clang from xcode 10.1 | Bazel 2.0.0 |
tensorflow-2.1.0 | 2.7, 3.5-3.7 | Clang from xcode 10.1 | Bazel 0.27.1 |
tensorflow-2.0.0 | 2.7, 3.5-3.7 | Clang from xcode 10.1 | Bazel 0.27.1 |
tensorflow-2.0.0 | 2.7, 3.3-3.7 | Clang from xcode 10.1 | Bazel 0.26.1 |
tensorflow-1.15.0 | 2.7, 3.3-3.7 | Clang from xcode 10.1 | Bazel 0.26.1 |
tensorflow-1.14.0 | 2.7, 3.3-3.7 | Clang from xcode | Bazel 0.24.1 |
tensorflow-1.13.1 | 2.7, 3.3-3.7 | Clang from xcode | Bazel 0.19.2 |
tensorflow-1.12.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.15.0 |
tensorflow-1.11.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.15.0 |
tensorflow-1.10.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.15.0 |
tensorflow-1.9.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.11.0 |
tensorflow-1.8.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.10.1 |
tensorflow-1.7.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.10.1 |
tensorflow-1.6.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.8.1 |
tensorflow-1.5.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.8.1 |
tensorflow-1.4.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.5.4 |
tensorflow-1.3.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.5 |
tensorflow-1.2.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.5 |
tensorflow-1.1.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 |
tensorflow-1.0.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 |
GPU
Version | Python version | Compiler | Build tools | cuDNN | CUDA |
---|---|---|---|---|---|
tensorflow_gpu-1.1.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | 5.1 | 8 |
tensorflow_gpu-1.0.0 | 2.7, 3.3-3.6 | Clang from xcode | Bazel 0.4.2 | 5.1 | 8 |