Inspired by tensorflow-on-raspberry-pi. Tool to compile tensorflow for ARM.
apt-get install openjdk-8-jdk automake autoconf
apt-get install curl zip unzip libtool swig libpng-dev zlib1g-dev pkg-config git g++ wget xz-utils
# For python2.7
apt-get install python-numpy python-dev python-pip python-mock
# If using a virtual environment, omit the --user argument
pip install -U --user keras_applications==1.0.8 --no-deps
pip install -U --user keras_preprocessing==1.1.0 --no-deps
# For python3
apt-get install python3-numpy python3-dev python3-pip python3-mock
# If using a virtual environment, omit the --user argument
pip3 install -U --user keras_applications==1.0.8 --no-deps
pip3 install -U --user keras_preprocessing==1.1.0 --no-deps
pip3 install portpickerPython wheels for TensorFlow are officially supported. This repository also maintains up-to-date TensorFlow wheels for Raspberry Pi.
Check out the official TensorFlow website for more information.
Make you sure add the ARM architecture to your package manager, see how to add it in Debian flavors:
dpkg --add-architecture armhf
echo "deb [arch=armhf] http://httpredir.debian.org/debian/ buster main contrib non-free" >> /etc/apt/sources.listIf you want compile Python support:
# For python2.7
apt-get install libpython-all-dev:armhf
# For python3
apt-get install libpython3-all-dev:armhfcd build_tensorflow/
docker build -t tf-arm -f Dockerfile .
docker run -it -v /tmp/tensorflow_pkg/:/tmp/tensorflow_pkg/ --env TF_PYTHON_VERSION=3.7 tf-arm ./build_tensorflow.sh configs/<conf-name> # rpi.conf, rk3399.conf ...cd build_tensorflow/
docker build -t tf-arm -f Dockerfile.bullseye .
docker run -it -v /tmp/tensorflow_pkg/:/tmp/tensorflow_pkg/ --env TF_PYTHON_VERSION=3.8 tf-arm ./build_tensorflow.sh configs/<conf-name> # rpi.conf, rk3399.conf ...See configuration file examples in: build_tensorflow/configs/
cd build_tensorflow/
chmod +x build_tensorflow.sh
TF_PYTHON_VERSION=3.5 ./build_tensorflow.sh <path-of-config> [noclean]
# The optional [noclean] argument omits 'bazel clean' before building for debugging purposes.
# If no output errors, the pip package will be in the directory: /tmp/tensorflow_pkg/