Micropython integration for the emlearn Machine Learning library for microcontrollers.

The goal is to enable applications to run ML inference on the microcontroller, without having to touch any C code.

Proof of Concept

• Can run RandomForest/DecisionTree
• Pre-built modules are available for the most common
• Has been tested on armv6m (RP2040) and x64 (Unix port)

Or contribute yourself!

Minimally you will need

• Python 3.10+ on host
• MicroPython running onto your device

Download the repository with examples etc

git clone https://github.com/emlearn/emlearn-micropython

Identify which CPU architecture your device uses. You need to specify ARCH to install the correct module version.

Download from releases.

Copy the .mpy file for the correct ARCH to your device.

mpremote cp emltrees-$ARCH.mpy :emltrees.mpy

NOTE: If there is no ready-made build for your device/architecture, then you will need to build the .mpy module yourself.

NOTE: Make sure to install the module first.

Train a model with scikit-learn

pip install emlearn scikit-learn
python examples/xor_train.py

Copy model file to device

mpremote cp xor_model.csv :xor_model.csv

Run program that uses the model

mpremote run examples/xor_run.py

These come in addition to the prequisites described above.

Make sure you have the dependencies needed to build for your platform. See MicroPython: Building native modules.

We assume that micropython is installed in the same place as this repository. If using another location, adjust MPY_DIR accordingly.

Build the .mpy native module

make -C eml_trees/ ARCH=x64 MPY_DIR=../../micropython

Install it on device

mpremote cp emltrees/emltrees.mpy :emltrees.mpy

TODO: implement and document