This repository contains an easy-to-use Python API that helps you run inferences and perform on-device transfer learning with TensorFlow Lite models on Coral devices.
To install the prebuilt PyCoral library, see the instructions at coral.ai/software/.
Note: If you're on a Debian system, be sure to install this library from
apt-get and not from pip. Using pip install is not guaranteed compatible with
the other Coral libraries that you must install from apt-get. For details, see
coral.ai/software/.
To learn more about how to use the PyCoral API, see our guide to Run inference on the Edge TPU with Python and check out the PyCoral API reference.
Several Python examples are available in the examples/ directory. For
instructions, see the examples README.
When building this library yourself, it's critical that you have
version-matching builds of
libcoral and
libedgetpu—notice
these are submodules of the pycoral repo, and they all share the same
TENSORFLOW_COMMIT value. So just be sure if you change one, you must change
them all.
For complete details about how to build all these libraries, read Build Coral for your platform. Or to build just this library, follow these steps:
-
Clone this repo and include submodules:
git clone --recurse-submodules https://github.com/google-coral/pycoralIf you already cloned without the submodules. You can add them with this:
cd pycoral git submodule init && git submodule update -
Run
scripts/build.shto build pybind11-based native layer for different Linux architectures. Build is Docker-based, so you need to have it installed. -
Run
make wheelto generate Python library wheel and thenpip3 install $(ls dist/*.whl)to install it