2.2. User Installation

This guide covers the quick installation method for users who want to use Tiny ML Tensorlab without modifying the source code.

Note

This installation method provides read-only access to the toolchain. If you need to customize models or add new features, use Developer Installation instead.

2.2.1. Quick Install

Install Tiny ML Tensorlab directly from GitHub:

pip install git+https://github.com/TexasInstruments/tinyml-tensorlab.git@main#subdirectory=tinyml-modelmaker

This installs:

  • tinyml-modelmaker - The main orchestration tool

  • tinyml-tinyverse - Core training infrastructure

  • tinyml-modelzoo - Model definitions

  • tinyml-modeloptimization - Quantization toolkit

2.2.2. Running Your First Example

After installation, you can run the hello world example:

# Clone just the examples
git clone --depth 1 https://github.com/TexasInstruments/tinyml-tensorlab.git
cd tinyml-tensorlab/tinyml-modelzoo

# Run the example
python -m tinyml_modelmaker examples/generic_timeseries_classification/config.yaml

Output will be saved to ../tinyml-modelmaker/data/projects/.

2.2.3. Verifying Installation

Verify the installation by importing the packages:

import tinyml_modelmaker
import tinyml_tinyverse
import tinyml_modelzoo
import tinyml_torchmodelopt

print("Installation successful!")

2.2.4. Updating

To update to the latest version:

pip install --upgrade git+https://github.com/TexasInstruments/tinyml-tensorlab.git@main#subdirectory=tinyml-modelmaker

2.2.5. Uninstalling

To remove Tiny ML Tensorlab:

pip uninstall tinyml-modelmaker tinyml-tinyverse tinyml-modelzoo tinyml-torchmodelopt

2.2.6. Limitations of User Install

The pip install method has some limitations:

  • Cannot modify model architectures

  • Cannot add custom feature extractors

  • Cannot debug training scripts

  • Updates require reinstallation

For full access, use Developer Installation.

2.2.7. Troubleshooting

“No module named tinyml_modelmaker”

Ensure you’re using the correct Python environment:

which python  # Should point to your Python 3.10 installation
python --version  # Should show 3.10.x

Version conflicts

If you have dependency conflicts, try installing in a virtual environment:

python -m venv tensorlab_env
source tensorlab_env/bin/activate  # Linux
# or: tensorlab_env\Scripts\activate  # Windows

pip install git+https://github.com/TexasInstruments/tinyml-tensorlab.git@main#subdirectory=tinyml-modelmaker

2.2.8. Next Steps