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: We already provide well-tested, pre-built TensorFlow packages for Linux and macOS systems.
Install the following build tools to configure your development environment.
sudo apt install python3-dev python3-pip
Requires Xcode 9.2 or later.
Install using the Homebrew package manager:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
export PATH="/usr/local/opt/python/libexec/bin:$PATH"
# if you are on macOS 10.12 (Sierra) use `export PATH="/usr/local/bin:/usr/local/sbin:$PATH"`
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
pip install -U --user keras_preprocessing --no-deps
Note: A pip
version >19.0 is required to install the TensorFlow 2 .whl
package. Additional required dependencies are listed in the
setup.py
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 a supported Bazel version: any version between
_TF_MIN_BAZEL_VERSION
and _TF_MAX_BAZEL_VERSION
as specified in
tensorflow/configure.py
.
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{:.external} to clone the TensorFlow repository{:.external}:
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{:.external}
to build:
git checkout branch_name # r2.2, r2.3, etc.
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
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):
./configure You have bazel 3.0.0 installed. Please specify the location of python. [Default is /usr/bin/python3]:Found possible Python library paths: /usr/lib/python3/dist-packages /usr/local/lib/python3.6/dist-packages Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: No OpenCL SYCL support will be enabled for TensorFlow.
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]: Y CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: No TensorRT support will be enabled for TensorFlow.
Found CUDA 10.1 in: /usr/local/cuda-10.1/targets/x86_64-linux/lib /usr/local/cuda-10.1/targets/x86_64-linux/include Found cuDNN 7 in: /usr/lib/x86_64-linux-gnu /usr/include
Please specify a list of comma-separated CUDA compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as "x.y" or "compute_xy" to include both virtual and binary GPU code, or as "sm_xy" to only include the binary code. Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]: 6.1
Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:
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=monolithic # Config for mostly static monolithic build. --config=ngraph # Build with Intel nGraph support. --config=numa # Build with NUMA support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. --config=v2 # Build TensorFlow 2.x instead of 1.x. Preconfigured Bazel build configs to DISABLE default on features: --config=noaws # Disable AWS S3 filesystem support. --config=nogcp # Disable GCP support. --config=nohdfs # Disable HDFS support. --config=nonccl # Disable NVIDIA NCCL support. Configuration finished
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. See the
GCC manual{:.external}
for examples.
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{:.external}.--config=monolithic
—Configuration for a mostly static, monolithic build.--config=v1
—Build TensorFlow 1.x instead of 2.x.
Note: Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.
Install Bazel and use
bazel build
to create the TensorFlow 2.x package with CPU-only support:
bazel build [--config=option] //tensorflow/tools/pip_package:build_pip_package
Note: GPU support can be enabled with cuda=Y
during the ./configure
stage.
To build a TensorFlow package builder with GPU support:
bazel build --config=cuda [--config=option] //tensorflow/tools/pip_package:build_pip_package
To build an older TensorFlow 1.x package, use the --config=v1
option:
bazel build --config=v1 [--config=option] //tensorflow/tools/pip_package:build_pip_package
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 a GCC 7.3 toolchain that complies with the manylinux2010 package standard.
For GCC 5 and later, compatibility with the older ABI can be built 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.
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.
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
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. See the TensorFlow Docker guide for installation and the list of available image tags{:.external}.
The following example uses the :devel
image to build a CPU-only 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_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:
- 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__)"
Success: TensorFlow is now installed.
On your host machine, the TensorFlow pip package is in the current directory
(with host user permissions):
./tensorflow-version-tags.whl
Docker is the easiest way to build GPU support for TensorFlow since the host machine only requires the NVIDIA® driver{:.external} (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{:.external} (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 # 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(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))"
Success: TensorFlow is now installed.
Version | Python version | Compiler | Build tools |
---|---|---|---|
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 |
Version | Python version | Compiler | Build tools | cuDNN | CUDA |
---|---|---|---|---|---|
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 |
Version | Python version | Compiler | Build tools |
---|---|---|---|
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 |
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 |