jetson-cli 0.7.0

Command-line interface for NVIDIA Jetson setup and configuration

jetson-cli

A comprehensive Python CLI tool for setting up NVIDIA Jetson devices and building containerized AI/ML applications.

Overview

jetson-cli provides a streamlined, pure-Python interface for:

• Analyzing and configuring Jetson hardware
• Setting up development environments
• Managing Docker, storage, power, and GUI configurations
• Building and running containerized AI/ML applications

Features

✅ Pure Python Implementation: No shell script dependencies
✅ Comprehensive System Analysis: Hardware detection and configuration validation
✅ Modular Architecture: Separate managers for different system components
✅ Rich CLI Interface: Beautiful terminal output with progress indicators
✅ Multiple Output Formats: Table, JSON, and YAML support
✅ Interactive & Non-Interactive Modes: Flexible operation modes
✅ Extensive Testing: Unit tests for all major functionality

Task list

• Best practices compliation for jetson for humans and AI
  • tools
  • commands
  • configurations
• docker images backup and restore scripts
• GenAI integration on device, local or remote
• Model inference setup recommendations
• jetson-containers Package suite recommendations

Installation

From PyPI (Recommended)

pip install jetson-cli

From Source

git clone https://github.com/orinachum/jetson-cli.git
cd jetson-cli
pip install -e .

Quick Start

1. Analyze your system:

   jetson-cli probe
   

2. Initialize environment:

   jetson-cli init
   

3. Complete setup:

   jetson-cli setup
   

Commands

System Analysis

jetson-cli probe                        # Show system configuration
jetson-cli probe --output json          # Output as JSON
jetson-cli probe --save config.yaml     # Save to file
jetson-cli probe --tests docker,swap    # Run specific tests only

Environment Setup

jetson-cli init                         # Create environment profile
jetson-cli init --profile-name dev      # Custom profile name
jetson-cli init --force                 # Overwrite existing profile

System Configuration

jetson-cli setup                        # Complete system setup
jetson-cli setup --skip-docker          # Skip Docker configuration
jetson-cli setup --interactive          # Interactive mode
jetson-cli setup --non-interactive      # Automated mode

Component Management

jetson-cli configure docker             # Configure Docker daemon
jetson-cli configure swap               # Setup swap file
jetson-cli configure ssd                # Configure SSD storage
jetson-cli configure power              # Power management settings
jetson-cli configure gui                # GUI environment setup

Status Monitoring

jetson-cli status                       # Show system status table
jetson-cli status --format json         # JSON output format

Configuration Components

Docker Management

• Automatic Docker installation with NVIDIA runtime
• Data directory migration to NVMe storage
• User group configuration
• Runtime optimization

Storage Management

• NVMe SSD partitioning, formatting, and mounting
• Swap file creation and configuration
• zRAM management
• Automatic size detection and validation

Power Management

• Jetson power mode configuration
• Thermal monitoring
• Power consumption analysis
• Interactive mode selection

GUI Management

• Desktop environment enable/disable
• Display configuration detection
• Session management
• Boot configuration

Python SDK

The CLI is built on a modular SDK that can be used programmatically:

from jetson_cli.sdk import SystemManager, DockerManager, StorageManager

# System analysis
system_manager = SystemManager()
results = system_manager.probe_system()
print(f"Platform: {results['platform']['machine']}")

# Docker configuration
docker_manager = DockerManager()
if not docker_manager.is_docker_installed():
    docker_manager.install_docker()

# Storage management
storage_manager = StorageManager()
storage_info = storage_manager.get_storage_info()

Examples

Complete Jetson Setup Workflow

# 1. Analyze hardware and software configuration
jetson-cli probe --save system-info.json

# 2. Create development environment profile
jetson-cli init --profile-name ml-dev

# 3. Configure the system for AI/ML development
jetson-cli setup

# 4. Verify everything is working
jetson-cli status

# 5. Configure specific components as needed
jetson-cli configure power  # Set optimal power mode
jetson-cli configure ssd    # Setup external storage

Selective Component Configuration

# Configure only Docker (skip other components)
jetson-cli configure docker

# Setup additional swap space
jetson-cli configure swap

# Configure external SSD storage
jetson-cli configure ssd --interactive

# Set power mode for maximum performance
jetson-cli configure power

System Analysis and Monitoring

# Comprehensive system probe
jetson-cli probe --output json --save analysis.json

# Monitor specific components
jetson-cli probe --tests docker_installed,nvme_mount,power_mode

# Check system status
jetson-cli status --format table

Architecture

jetson-cli/
├── jetson_cli/
│   ├── cli.py              # Click-based CLI interface
│   ├── utils.py            # Utility functions
│   └── sdk/                # Python SDK modules
│       ├── system.py       # System management
│       ├── docker.py       # Docker configuration
│       ├── storage.py      # Storage management
│       ├── power.py        # Power management
│       └── gui.py          # GUI configuration
├── tests/                  # Unit tests
└── docs/                   # Documentation

Key Components

• CLI Interface: Rich terminal interface with progress indicators
• SDK Modules: Modular Python classes for system management
• Configuration Management: Environment-based configuration
• Error Handling: Comprehensive error reporting and recovery
• Testing Framework: Unit tests for all major functionality

Requirements

• NVIDIA Jetson device (Nano, Xavier, Orin series)
• JetPack 4.6+ or L4T R32.7+
• Python 3.8+
• Root privileges for system configuration

Development

Running Tests

# Run all tests
python -m unittest tests.test_sdk -v

# Run specific test
python -m unittest tests.test_sdk.TestSystemManager -v

Adding New Features

1. Implement functionality in appropriate SDK module
2. Add CLI command in cli.py
3. Write unit tests in tests/
4. Update documentation

Migrated from Shell Scripts

This version represents a complete migration from shell scripts to pure Python:

• ✅ probe-system.sh → SystemManager.probe_system()
• ✅ configure-docker.sh → DockerManager.setup_docker()
• ✅ configure-swap.sh → StorageManager.setup_swap_file()
• ✅ configure-ssd.sh → StorageManager.configure_nvme_ssd()
• ✅ configure-power-mode.sh → PowerManager.set_power_mode()
• ✅ configure-system-gui.sh → GUIManager.configure_gui()
• ✅ create-env-profile.sh → SystemManager.create_env_profile()

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.