doc/develop/manifest/external/emlearn.rst
.. _external_module_emlearn:
emlearn #######
Introduction
emlearn_ is an open source library for deploying machine learning models on micro-controllers
and embedded systems. It provides portable C code generation from models trained with
scikit-learn or Keras.
A Python library allows converting complex machine learning models to a minimal C code representation, which enables running ML inference on resource-constrained embedded devices.
emlearn is licensed under the MIT license.
Usage with Zephyr
The emlearn repository is a Zephyr :ref:module <modules> which provides TinyML capabilities to
Zephyr applications, allowing machine learning models to be run directly on Zephyr-powered devices.
To pull in emlearn as a Zephyr module, either add it as a West project in the west.yaml
file or pull it in by adding a submanifest (e.g. zephyr/submanifests/emlearn.yaml) file
with the following content and run west update:
.. code-block:: yaml
manifest: projects: - name: emlearn url: https://github.com/emlearn/emlearn.git revision: master path: modules/lib/emlearn # adjust the path as needed
For more detailed instructions and API documentation, refer to the emlearn documentation, and in
particular the Getting Started on Zephyr RTOS section.
References
.. target-notes::
.. _emlearn: https://github.com/emlearn/emlearn
.. _emlearn documentation: https://emlearn.readthedocs.io/en/latest/
.. _Getting Started on Zephyr RTOS: https://emlearn.readthedocs.io/en/latest/getting_started_zephyr.html