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README.md

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    Clone RPI docs from the TF official documentation (#20)
    Austin Anderson authored and GitHub committed
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    README.md 3.54 KiB

    Raspberry Pi Builds

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

    Maintainer: @angerson (TensorFlow, SIG Build)

    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.

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