2.0 KiB
Building TensorFlow Lite Standalone Pip
Many users would like to deploy TensorFlow lite interpreter and use it from Python without requiring the rest of TensorFlow.
Steps
To build a binary wheel run this script:
sudo apt install swig libjpeg-dev zlib1g-dev python3-dev python3-numpy
sh tensorflow/lite/tools/pip_package/build_pip_package.sh
That will print out some output and a .whl file. You can then install that
pip install --upgrade <wheel>
You can also build a wheel inside docker container using make tool. For example the following command will cross-compile tflite-runtime package for python2.7 and python3.7 (from Debian Buster) on Raspberry Pi:
make BASE_IMAGE=debian:buster PYTHON=python TENSORFLOW_TARGET=rpi docker-build
make BASE_IMAGE=debian:buster PYTHON=python3 TENSORFLOW_TARGET=rpi docker-build
Another option is to cross-compile for python3.5 (from Debian Stretch) on ARM64 board:
make BASE_IMAGE=debian:stretch PYTHON=python3 TENSORFLOW_TARGET=aarch64 docker-build
To build for python3.6 (from Ubuntu 18.04) on x86_64 (native to the docker image) run:
make BASE_IMAGE=ubuntu:18.04 PYTHON=python3 TENSORFLOW_TARGET=native docker-build
In addition to the wheel there is a way to build Debian package by adding BUILD_DEB=y to the make command (only for python3):
make BASE_IMAGE=debian:buster PYTHON=python3 TENSORFLOW_TARGET=rpi BUILD_DEB=y docker-build
Note, unlike tensorflow this will be installed to a tflite_runtime namespace. You can then use the Tensorflow Lite interpreter as.
from tflite_runtime.interpreter import Interpreter
interpreter = Interpreter(model_path="foo.tflite")
This currently works to build on Linux machines including Raspberry Pi. In the future, cross compilation to smaller SOCs like Raspberry Pi from bigger host will be supported.
Caveats
- You cannot use TensorFlow Select ops, only TensorFlow Lite builtins.
- Currently custom ops and delegates cannot be registered.