Install heliaEDGE

This site follows the repository’s main branch. The generated API and guides can include changes that are not yet in a PyPI release. Use a source checkout to follow the examples against the same API; use PyPI when you need a released package.

Choose your installation

Use Python 3.12.3 or newer: 3.12–3.13 for TensorFlow and LiteRT, or 3.12–3.14 for Torch and framework-independent helpers.

Source checkout

Use this path for the API documented on this site. Clone once, then select your backend:

git clone https://github.com/AmbiqAI/helia-edge.git
cd helia-edge

TensorFlow

uv sync --python 3.12 --extra tensorflow

PyTorch

uv sync --python 3.12 --extra torch

Run scripts with uv run. Record git rev-parse HEAD with your experiments.

PyPI release

Release 0.6.2 uses the earlier TensorFlow-based API and includes plotting and AWS dependencies by default. Use the source checkout for the portable TensorFlow/PyTorch APIs and optional capability extras described on this site.

pip

pip install 'helia-edge==0.6.2'

uv

uv add 'helia-edge==0.6.2'

The 0.6.2 release offers a litert extra; it has no torch, tensorflow, plotting or aws extras.

Add plotting, S3 access or model conversion

In a source checkout, choose only the capabilities your workflow uses. Combine extras with uv sync:

uv sync --python 3.12 --extra tensorflow --extra plotting --extra aws

Extra	Adds
plotting	Training history, confusion matrices and patch visualization
aws	S3 downloads and model retrieval
litert	TensorFlow conversion and LiteRT host prediction

The source package without backend extras installs framework-independent helpers without a training backend. JAX is outside the tested matrix.

Check the environment

Run this in a fresh Python process in the environment you installed:

TensorFlow

import os
os.environ["KERAS_BACKEND"] = "tensorflow"

from importlib.metadata import version
import keras
import helia_edge

print("heliaEDGE:", version("helia-edge"))
print("Keras backend:", keras.backend.backend())

PyTorch

import os
os.environ["KERAS_BACKEND"] = "torch"

from importlib.metadata import version
import keras
import helia_edge

print("heliaEDGE:", version("helia-edge"))
print("Keras backend:", keras.backend.backend())

The backend should match your selection. A source checkout can retain the package version from the last release, so use the Git revision to identify source behavior.

Troubleshoot installation and imports

Symptom	Next step
Missing TensorFlow or Torch	Install the matching extra in the environment running the script.
The wrong backend is active	Set KERAS_BACKEND before importing Keras, then restart the Python process or notebook kernel.
A documented import is missing	Check whether you installed a release or the source revision used by the docs.
A saved model reports an unknown custom class	Follow the registration steps in the save/load guide.

Next step

Build your first model→

Construct and save a small TCN using typed configuration.

Train a CIFAR-10 model→

Follow a complete notebook from data preparation to training.