Python API for ML inferencing and transfer-learning on Coral devices
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 sameTENSORFLOW_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/pycoral
If you already cloned without the submodules. You can add them with this:
cd pycoralgit submodule init && git submodule update
Run scripts/build.sh to build pybind11-based native layer for different
Linux architectures. Build is Docker-based, so you need to have it
installed.
Run make wheel to generate Python library wheel and then pip3 install
$(ls dist/*.whl) to install it