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 python-dev python-pip # or python3-dev python3-pip
mac OS
Requires Xcode 9.2 or later.
Install using the Homebrew package manager:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
export PATH="/usr/local/bin:/usr/local/sbin:$PATH"
brew install python@2 # or python (Python 3)
Install the TensorFlow pip package dependencies (if using a virtual environment,
omit the --user
argument):
pip install -U --user pip six numpy wheel setuptools mock 'future>=0.17.1'
pip install -U --user keras_applications --no-deps
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 a supported Bazel version: any version between
_TF_MIN_BAZEL_VERSION
and _TF_MAX_BAZEL_VERSION
as specified in
tensorflow/configure.py
.
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 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 ./configure
at the root of your
TensorFlow source tree. This script prompts you for the location of TensorFlow
dependencies and asks for additional build configuration options (compiler
flags, for example).
./configure
Sample session
The ./configure
script The following shows a sample run of ./configure
(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. See 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=mkl
—Support for the Intel® MKL-DNN.--config=monolithic
—Configuration for a mostly static, monolithic build.--config=v1
—Build TensorFlow 1.x instead of 2.x.
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 //tensorflow/tools/pip_package:build_pip_package
Bazel build options
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
.
The official TensorFlow packages are built with GCC 4 and use the
older ABI. For GCC 5 and later, make your build compatible with the older ABI
using: --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"
. ABI compatibility ensures that
custom ops built against the official TensorFlow package continue to work with
the GCC 5 built package.
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. See the TensorFlow Docker guide for installation and the list of available image tags.
CPU-only
The following example uses the :devel
image to build a CPU-only
Python 2 package from the latest TensorFlow source code. See the
Docker guide for available TensorFlow -devel
tags.
Download the latest development image and start a Docker container that we'll use to build the pip package:
docker pull tensorflow/tensorflow:devel
docker run -it -w /tensorflow -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
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:
- Configure the build—this prompts the user to answer build configuration questions.
- Build the tool used to create the pip package.
- Run the tool to create the pip package.
- Adjust the ownership permissions of the file for outside the container.
./configure # answer prompts or use defaults
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). See the GPU support guide and the TensorFlow Docker guide to set up nvidia-docker (Linux only).
The following example downloads the TensorFlow :devel-gpu-py3
image
and uses nvidia-docker
to run the GPU-enabled container. This development image
is configured to build a Python 3 pip package with GPU support:
docker pull tensorflow/tensorflow:devel-gpu-py3
docker run --runtime=nvidia -it -w /tensorflow -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \ tensorflow/tensorflow:devel-gpu-py3 bash
Then, within the container's virtual environment, build the TensorFlow package with GPU support:
./configure # answer prompts or use defaults
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(tf.contrib.eager.num_gpus())"
Tested build configurations
Linux
CPU
Version | Python version | Compiler | Build tools |
---|---|---|---|
tensorflow-2.0.0 | 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-2.0.0 | 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-2.0.0 | 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 |