Raspberry Pi Builds

TensorFlow's old official docs for building on Raspberry Pi. Needs an owner.

Maintainer: @angerson (TensorFlow, SIG Build)

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Important: TensorFlow for the Raspberry Pi is no longer supported by the TensorFlow team. (last tested on 2.3.0rc2). See the Build TensorFlow Lite for Raspberry Pi guide.

This guide is a mirror of the old official documentation and may not work. If you'd like to own this and keep it up-to-date, please file a PR!

Build from source for the Raspberry Pi

This guide builds a TensorFlow package for a Raspberry Pi device running Raspbian 9.0. While the instructions might work for other Raspberry Pi variants, it is only tested and supported for this configuration.

We recommend cross-compiling the TensorFlow Raspbian package. Cross-compilation is using a different platform to build the package than deploy to. Instead of using the Raspberry Pi's limited RAM and comparatively slow processor, it's easier to build TensorFlow on a more powerful host machine running Linux, macOS, or Windows.

Setup for host

Install Docker

To simplify dependency management, the build script uses Docker to create a virtual Linux development environment for compilation. Verify your Docker install by executing: docker run --rm hello-world

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.

Key Point: If you're having build problems on the latest development branch, try a release branch that is known to work.

Build from source

Cross-compile the TensorFlow source code to build a Python pip package with ARMv7 NEON instructions that works on Raspberry Pi 2, 3 and 4 devices. The build script launches a Docker container for compilation. You can also build ARM 64-bit binary (aarch64) by providing AARCH64 parameter to the build_raspberry_pi.sh script. Choose among Python 3.8, Python 3.7, Python 3.5 and Python 2.7 for the target package:

Python 3.5

tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 \
    tensorflow/tools/ci_build/pi/build_raspberry_pi.sh

Python 3.7

tensorflow/tools/ci_build/ci_build.sh PI-PYTHON37 \
    tensorflow/tools/ci_build/pi/build_raspberry_pi.sh

Python 3.8 (64bit)

tensorflow/tools/ci_build/ci_build.sh PI-PYTHON38 \
    tensorflow/tools/ci_build/pi/build_raspberry_pi.sh AARCH64

Python 2.7

tensorflow/tools/ci_build/ci_build.sh PI \
    tensorflow/tools/ci_build/pi/build_raspberry_pi.sh

To build a package that supports all Raspberry Pi devices—including the Pi 1 and Zero—pass the PI_ONE argument, for example:

tensorflow/tools/ci_build/ci_build.sh PI \
    tensorflow/tools/ci_build/pi/build_raspberry_pi.sh PI_ONE

When the build finishes (~30 minutes), a .whl package file is created in the output-artifacts directory of the host's source tree. Copy the wheel file to the Raspberry Pi and install with pip:

pip install tensorflow-<version>-cp35-none-linux_armv7l.whl

Success: TensorFlow is now installed on Raspbian.