ODA (On Device AI)

Turn your Linux system into a fully-configured, sleek, and modern on-device AI development system by running a single command.

Radicle: rad://z2ELVemM12PrcCMfi7dQCTHfsWPNh

Overview

ODA is a comprehensive setup script that transforms a fresh Linux installation into a complete AI development environment. It automates the installation and configuration of essential tools, libraries, and environments commonly used in AI development.

Features

• One-command setup
• Command-line options for flexible installation
• Enhanced logging system with multiple levels
• Automatic distribution detection and configuration (Ubuntu, Debian, RedHat, Fedora, CentOS)
• Configuration backup and resume capability
• Essential development tools
• Python environment with AI/ML libraries (version-pinned)
• Docker configuration
• GPU drivers and CUDA setup (if available)
• Development environment configurations
• Performance optimizations
• Strict error handling and validation
• Version compatibility checks

Quick Start

# Option 1: Direct execution (recommended)
curl -sSL https://raw.githubusercontent.com/mitkox/oda/main/oda.sh | bash

# Option 2: Download and inspect before running
curl -O https://raw.githubusercontent.com/mitkox/oda/main/oda.sh
chmod +x oda.sh
./oda.sh [OPTIONS]

Command-Line Options

Usage: ./oda.sh [OPTIONS]
Options:
  -y, --yes        Non-interactive mode, assume yes for all prompts
  -d, --dry-run    Show what would be installed without making changes
  -v, --verbose    Enable verbose logging
  -h, --help       Show this help message
  --no-gpu         Skip GPU-related installations
  --resume         Resume from last failed step

Examples

# Non-interactive installation
./oda.sh -y

# Verbose installation without GPU support
./oda.sh -v --no-gpu

# Resume a failed installation
./oda.sh --resume

# Show what would be installed without making changes
./oda.sh --dry-run

After installation, activate the Python virtual environment:

source ~/.oda-venv/bin/activate

What Gets Installed

• System essentials (git, curl, build tools)
• Python 3.10 with virtual environment
• Version-pinned AI/ML libraries:
  • PyTorch 2.1.0
  • TensorFlow 2.14.0
  • NumPy 1.24.3
  • pandas 2.1.1
  • scikit-learn 1.3.1
• llama.cpp with CUDA support (when GPU available)
• Docker and NVIDIA Container Toolkit
• Development tools (VS Code)
• Shell improvements (zsh, oh-my-zsh)
• NVIDIA driver 535 and CUDA (if GPU available)

Requirements

Supported Distributions

• Ubuntu 20.04 LTS or newer
• Red Hat Enterprise Linux 8 or newer
• CentOS 8 or newer
• Rocky Linux 8 or newer
• AlmaLinux 8 or newer

System Requirements

• Internet connection
• Sudo privileges
• 20GB free disk space
• NVIDIA GPU (optional, for GPU acceleration)

Features

Error Handling

• Strict error checking with detailed error messages
• Disk space validation
• System compatibility checks
• Detailed installation logs at /tmp/oda-install.log

Python Environment

• Isolated virtual environment at ~/.oda-venv
• Version-pinned dependencies for stability
• Pre-configured for AI/ML development

GPU Support

• Automatic NVIDIA GPU detection
• CUDA-enabled builds when GPU is available
• Containerized GPU support with NVIDIA Docker
• Fallback to CPU-only versions when no GPU is present

Distribution Support

• Automatic distribution detection
• Distribution-specific optimizations
• Automatic package manager detection
• Distribution-specific repository management

Security Features

• HTTPS-only downloads
• GPG key verification for repositories
• Secure temporary file handling
• Principle of least privilege
• Comprehensive error checking

On-Device AI Development Tools

Model Serving and Inference

• NVIDIA Triton Inference Server ${TRITON_VERSION}
• TensorRT ${TENSORRT_VERSION}
• ONNX Runtime with GPU support
• TensorFlow Lite
• OpenVINO
• Apache TVM ${TVM_VERSION}

Edge AI and Mobile

• MediaPipe
• Edge Impulse CLI
• NCNN for mobile devices
• ARM NN SDK (on ARM devices)
• PyTorch Mobile
• TensorFlow Lite

Model Optimization

• Neural Network Distiller
• TensorFlow Model Optimization
• Intel Neural Compressor
• Torch2TRT
• NVIDIA TensorRT

Performance Tools

• MLPerf for benchmarking
• NVIDIA Nsight Systems
• perf for profiling
• CUDA Toolkit

Additional Frameworks

• PaddlePaddle Lite
• MXNet
• ARM Compute Library (on ARM)

Installation Directory Structure

$HOME/
├── .oda/                    # Main installation directory
│   └── llama.cpp/          # llama.cpp installation
└── .oda-venv/              # Python virtual environment

Troubleshooting

Installation Logs

Check the installation log for detailed information:

cat /tmp/oda-install.log

Common Issues

1. GPU Not Detected: Ensure NVIDIA drivers are properly installed
2. Package Installation Fails: Check internet connection and try again
3. Permission Issues: Ensure you have sudo privileges
4. Space Issues: Ensure you have at least 20GB free space

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

MIT License - see LICENSE for details

Security

For security concerns, please see our Security Policy