Metadata-Version: 2.1
Name: nengo-edge
Version: 23.7.30
Summary: Tools for working with NengoEdge
Home-page: https://github.com/nengo/nengo-edge
Author: Applied Brain Research
Author-email: edge-info@appliedbrainresearch.com
License: MIT license
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: Nengo
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Provides-Extra: all
Provides-Extra: docs
Provides-Extra: optional
Provides-Extra: tests
License-File: LICENSE.rst

***************
NengoEdge Tools
***************

`NengoEdge <https://edge.nengo.ai/>`_ is a cloud-based platform for training and
deploying high accuracy, low power audio AI models on edge devices. This package
contains tools and examples to assist in taking a trained model exported from
NengoEdge and deploying it in your own application.

To get started running NengoEdge models locally,
set up a Python environment using the installation instructions below.
Then download the
`live microphone demo notebook
<https://www.nengo.ai/nengo-edge/examples/microphone-demo/microphone-demo.ipynb>`_
and open it with::

  jupyter notebook /path/to/microphone-demo.ipynb

.. image:: https://www.nengo.ai/nengo-edge/_static/demo.png
   :target: https://www.youtube.com/watch?v=sccLaootrGk

Installation
============

NengoEdge models use the `TensorFlow <https://www.tensorflow.org/>`_
machine learning library. If you already have TensorFlow installed,
then all you need is to::

  pip install nengo-edge

If you do not have TensorFlow installed, see the see the full
`installation instructions <https://www.nengo.ai/nengo-edge/installation>`_
for more details.

***************
Release history
***************

.. Changelog entries should follow this format:

   version (release date)
   ======================

   **section**

   - One-line description of change (link to Github issue/PR)

.. Changes should be organized in one of several sections:

   - Added
   - Changed
   - Deprecated
   - Removed
   - Fixed

23.7.30 (July 30, 2023)
=======================

**Added**

- Added ``CoralRunner`` for running models exported for the Coral board. (`#4`_)
- Added ``DiscoRunner`` for running models exported for the Disco board. (`#4`_)
- Added ``NordicRunner`` for running models exported for the Nordic board. (`#4`_)
- Added on-device MFCC extraction code
  (``device_modules.np_mfcc.LogMelFeatureExtractor``). (`#4`_)
- Added two new examples demonstrating how to run models exported for the
  Coral/Disco/Nordic devices. (`#4`_)

**Changed**

- Renamed ``tflite_runner.Runner`` to ``TFLiteRunner``. (`#4`_)
- Renamed ``saved_model_runner.Runner`` to ``SavedModelRunner``. (`#4`_)
- ``TFLiteRunner.reset_state`` now takes a ``batch_size`` argument, which can be used
  to prepare the model to run with different batch sizes. (`#5`_)

.. _#4: https://github.com/nengo/nengo-edge/pull/4
.. _#5: https://github.com/nengo/nengo-edge/pull/5

23.2.23 (February 23, 2023)
===========================

**Fixed**

- Fixed an issue causing pip to refuse to install ``nengo-edge``. (`#3`_)

.. _#3: https://github.com/nengo/nengo-edge/pull/3

23.1.31 (January 31, 2023)
==========================

Initial release
