Silicon Labs Machine Learning Toolkit (MLTK)

Warning

This package is considered EXPERIMENTAL - SILICON LABS DOES NOT OFFER ANY WARRANTIES AND DISCLAIMS ALL IMPLIED WARRANTIES CONCERNING THIS SOFTWARE. This package is made available as a self-serve reference supported only by the on-line documentation, and community support. There are no Silicon Labs support services for this software at this time.

This is a Python package with command-line utilities and scripts to aid the development of machine learning models for Silicon Lab’s embedded platforms.

The features of this Python package include:

Refer to Why MLTK? for more details on the benefits of using the MLTK.

Hint

Just want to quickly profile a model to see how fast it can run on an embedded target?
See the Model Profiler Utility

Overview

Installation

Install the pre-build Python package:

pip  install silabs-mltk
pip3 install silabs-mltk

Or, build and install Python package from Github:

pip  install git+https://github.com/siliconlabs/mltk.git
pip3 install git+https://github.com/siliconlabs/mltk.git

Refer to Installation Guide for more details on how to install the MLTK.

Other Information

License

SPDX-License-Identifier: Zlib

The licensor of this software is Silicon Laboratories Inc.

This software is provided ‘as-is’, without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software.

Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions:

  1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required.

  2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software.

  3. This notice may not be removed or altered from any source distribution.