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

sudo apt install python3-dev python3-pip

/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 packaging
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  # r2.2, r2.3, 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

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):

./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

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=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.
• --config=v1 —Build TensorFlow 1.x instead of 2.x.

Build the pip package

TensorFlow 2.x

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

GPU support

To build a TensorFlow package builder with GPU support:

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

TensorFlow 1.x

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

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 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.

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 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:

1. 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  # 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 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.list_physical_devices('GPU')))"

Tested build configurations

Linux

CPU

GPU

macOS

CPU

GPU