https://github.com/adhiiisetiawan/optimization-edgetpu

This repository contains experiments optimization on EdgeTPU
https://github.com/adhiiisetiawan/optimization-edgetpu

coral-tpu edgetpu

Last synced: 3 days ago
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This repository contains experiments optimization on EdgeTPU

• Host: GitHub
• URL: https://github.com/adhiiisetiawan/optimization-edgetpu
• Owner: adhiiisetiawan
• License: apache-2.0
• Created: 2023-12-20T16:49:34.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2023-12-20T17:28:07.000Z (11 months ago)
• Last Synced: 2023-12-21T18:01:21.824Z (11 months ago)
• Topics: coral-tpu, edgetpu
• Language: Jupyter Notebook
• Homepage:
• Size: 6.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# EdgeTPU Optimization

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](https://coral.ai/products/).

To install the prebuilt PyCoral library, see the instructions at

[coral.ai/software/](https://coral.ai/software/#pycoral-api).

**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/](https://coral.ai/software/#debian-packages).

## Documentation and examples

To learn more about how to use the PyCoral API, see our guide to [Run inference

on the Edge TPU with Python](https://coral.ai/docs/edgetpu/tflite-python/) and

check out the [PyCoral API reference](https://coral.ai/docs/reference/py/).

Several Python examples are available in the `examples/` directory. For

instructions, see the [examples README](

https://github.com/google-coral/pycoral/tree/master/examples#pycoral-api-examples).

## Compilation

When building this library yourself, it's critical that you have

version-matching builds of

[libcoral](https://github.com/google-coral/libcoral/tree/master) and

[libedgetpu](https://github.com/google-coral/libedgetpu/tree/master)—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](https://coral.ai/docs/notes/build-coral/).

Or to build just this library, follow these steps:

1.  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 pycoral

    git submodule init && git submodule update

    ```

1.  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.

1.  Run `make wheel` to generate Python library wheel and then `pip3 install

    $(ls dist/*.whl)` to install it

**Note**: This repository is based on [Pycoral](https://github.com/google-coral/pycoral) from Google Coral