2. Installation

This section guides you through installing Tiny ML Tensorlab on your system. Choose the installation path that best fits your needs.

Contents

• 2.1. Prerequisites
  • 2.1.1. System Requirements
  • 2.1.2. Software Requirements
  • 2.1.3. For Compilation (Optional)
  • 2.1.4. For Device Deployment (Optional)
  • 2.1.5. CUDA (Optional)
  • 2.1.6. Verification Checklist
• 2.2. User Installation
  • 2.2.1. Quick Install
  • 2.2.2. Running Your First Example
  • 2.2.3. Verifying Installation
  • 2.2.4. Updating
  • 2.2.5. Uninstalling
  • 2.2.6. Limitations of User Install
  • 2.2.7. Troubleshooting
  • 2.2.8. Next Steps
• 2.3. Developer Installation
  • 2.3.1. Overview
  • 2.3.2. Step 1: Clone the Repository
  • 2.3.3. Step 2: Set Up Python Environment
  • 2.3.4. Step 3: Verify Installation
  • 2.3.5. Step 4: Configure Environment Variables
  • 2.3.6. Directory Structure After Installation
  • 2.3.7. Updating
  • 2.3.8. Common Developer Tasks
  • 2.3.9. Troubleshooting
  • 2.3.10. Next Steps
• 2.4. Windows Setup
  • 2.4.1. Option 1: Native Windows Installation
  • 2.4.2. Option 2: WSL2 (Recommended for Full Compatibility)
  • 2.4.3. Path Configuration
  • 2.4.4. Common Windows Issues
  • 2.4.5. GPU Support on Windows
  • 2.4.6. WSL2 vs Native Windows Comparison
  • 2.4.7. Next Steps
• 2.5. Linux Setup
  • 2.5.1. System Preparation
  • 2.5.2. Installing Python 3.10
  • 2.5.3. Installation
  • 2.5.4. Verification
  • 2.5.5. GPU Setup (Optional)
  • 2.5.6. Shell Configuration
  • 2.5.7. Permission Issues
  • 2.5.8. Multiple Python Versions
  • 2.5.9. System Service (Optional)
  • 2.5.10. Troubleshooting
  • 2.5.11. Next Steps
• 2.6. Environment Variables
  • 2.6.1. Required Variables by Device Family
  • 2.6.2. Setting Environment Variables
  • 2.6.3. Installing TI Tools
  • 2.6.4. Verifying Configuration
  • 2.6.5. Troubleshooting
  • 2.6.6. Next Steps

2.7. Which Install Should I Choose?

Before diving into the prerequisites or detailed guides, use this table to pick the right installation type for your situation:

Scenario	Use This Install
I want to train models on my own data using TI-provided models	User Install (User Installation)
I want to run the provided examples without modifying source code	User Install (User Installation)
I want to use Tensorlab as a library in my own Python scripts	User Install (User Installation)
I want to add custom model architectures to the model zoo	Developer Install (Developer Installation)
I want to modify or debug the training pipeline itself	Developer Install (Developer Installation)
I want to contribute to the Tensorlab codebase	Developer Install (Developer Installation)
I want to integrate new feature extraction transforms	Developer Install (Developer Installation)

In summary:

• User Install – Install and use. No source code changes. Best for the majority of users who want to train models on their own data.

• Developer Install – Clone and edit. Gives full access to source code. Required only if you need to extend or modify the framework itself.

2.8. Quick Start

For Users (Quick Install)

If you want to use Tiny ML Tensorlab without modifying the source code:

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

For Developers (Full Install)

If you want to customize models, add features, or contribute:

LinuxWindows

git clone https://github.com/TexasInstruments/tinyml-tensorlab.git
cd tinyml-tensorlab/tinyml-modelmaker
./setup_all.sh

See the detailed guides in this section for complete instructions.