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

Warning

IMPORTANT: Environment Variables Required for Model Compilation

For AI model compilation to work, you MUST set environment variables specific to your target device before running examples.

The variables you need depend on which device you’re targeting:

  • C2000 devices (F28P55, F28P65, etc.): Set C2000_CG_ROOT and C2000WARE_ROOT

  • F29 devices (F29H85X, etc.): Set CG_TOOL_ROOT

  • MSPM0 devices: Set ARM_LLVM_CGT_PATH

  • AM13E devices: Set ARM_LLVM_CGT_PATH

  • AM26x devices: Set ARM_LLVM_CGT_PATH

  • Connectivity devices (CC2755, CC1352, etc.): Set ARM_LLVM_CGT_PATH

See Environment Variables for complete device-specific setup instructions.

After setting environment variables for your target device, 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 (trains and compiles for the device specified in config.yaml)
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 and checking versions:

import tinyml_modelmaker
import tinyml_tinyverse
import tinyml_torchmodelopt
import tinyml_modelzoo

print(f"TI Tiny ML ModelMaker: {tinyml_modelmaker.__version__}")
print(f"TI Tiny ML Tinyverse: {tinyml_tinyverse.__version__}")
print(f"TI Tiny ML Model Optimization toolkit: {tinyml_torchmodelopt.__version__}")
print(f"TI Tiny ML Model Zoo: {tinyml_modelzoo.__version__}")

If all packages import without errors and versions are displayed, your installation is complete.

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