jetson-cli 0.6.0

Command-line interface for NVIDIA Jetson setup and configuration

jetson-cli

A comprehensive CLI tool for setting up NVIDIA Jetson devices and building containerized AI/ML applications using the jetson-containers framework.

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

Overview

jetson-cli provides a streamlined interface for:

• Analyzing and configuring Jetson hardware
• Setting up development environments
• Building and running containerized AI/ML applications
• Managing the jetson-containers ecosystem

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

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

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
jetson-cli status --format json         # JSON output format

jetson-containers Integration

This tool integrates with the jetson-containers framework to provide containerized AI/ML packages:

Container Building

# After jetson-cli setup, use jetson-containers directly
jetson-containers build pytorch                    # Build PyTorch container
jetson-containers build pytorch jupyterlab         # Chain multiple packages
jetson-containers build --name=my_app pytorch      # Custom container name

Available Packages

• ML/AI: PyTorch, TensorFlow, ONNX Runtime, transformers
• LLM: SGLang, vLLM, MLC, text-generation-webui, ollama
• VLM: LLaVA, VILA, NanoLLM (vision-language models)
• Robotics: ROS, Genesis, OpenVLA, LeRobot
• Computer Vision: NanoOWL, SAM, CLIP, DeepStream
• Graphics: Stable Diffusion, ComfyUI, NeRF Studio

Running Containers

jetson-containers run $(autotag l4t-pytorch)

Examples

Complete Jetson Setup Workflow

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

# 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. Build and run your first container
jetson-containers build pytorch
jetson-containers run $(autotag l4t-pytorch)

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

Architecture

• CLI Interface (jetson_cli/): User-friendly Click-based commands
• System Scripts (scripts/): Low-level system configuration scripts
• Container Framework (jetson-containers/): Modular container build system
• Package Ecosystem: 100+ pre-built AI/ML container packages

Requirements

• NVIDIA Jetson device (Nano, Xavier, Orin series)
• JetPack 4.6+ or L4T R32.7+
• Python 3.6+
• Docker support

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.