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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
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.

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 and 3 devices. The build script launches a Docker container for compilation. Choose between Python 3 and Python 2.7 for the target package:

Python 3

    tensorflow/tools/ci_build/ PI-PYTHON3 \

Python 2.7

tensorflow/tools/ci_build/ PI \

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/ PI \
    tensorflow/tools/ci_build/pi/ 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-cp34-none-linux_armv7l.whl