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DocumentationInstallation
// installation

Installation Guide

Get AlignAIR up and running in minutes. Docker is recommended; local installation is also supported for developers.

// methods

Choose your installation method

Docker

Recommended

The easiest way to run AlignAIR. Everything is pre-configured and ready to use.

  • No manual setup required
  • Consistent environment across platforms
  • Pre-trained models included

Local

Advanced

Install AlignAIR directly. Recommended for developers and advanced users.

  • Full control over environment
  • Easier integration with workflows
  • Requires manual configuration
// docker

Docker installation

1

Verify Docker

Make sure Docker is installed and running.

docker --version
Expected: Docker version 20.10+
2

Pull AlignAIR Image

Download the latest AlignAIR image.

docker pull thomask90/alignair:latest
May take a few minutes.
3

Prepare Data Directory

Create directories for your data.

mkdir -p ~/alignair-data/input ~/alignair-data/output
Place input files in ~/alignair-data/input/
4

Run Container

Start AlignAIR with volume mounting.

docker run -it --rm -v ~/alignair-data:/data thomask90/alignair:latest
You should see the AlignAIR command prompt.
// requirements

System requirements

// recommended
  • NVIDIA GPU
    CUDA 11+ for optimal performance
  • 16GB+ RAM
    For large batch processing
  • 10GB+ Storage
    For models and datasets
// minimum
  • Multi-core CPU
    CPU mode available (slower)
  • 8GB+ RAM
    For basic processing
  • 5GB+ Storage
    Minimum free space
// local

Local installation (advanced)

Prerequisites

Python 3.8+
python --version
Git
git --version

Installation steps

1. Clone the repository:
git clone https://github.com/MuteJester/AlignAIR.git
2. Navigate to directory and install dependencies:
cd AlignAIR
pip install -r requirements.txt
3. Verify installation:
python app.py --help
// note

This method requires a properly configured Python environment and is recommended for developers only. You'll need to manually download model weights and configure paths.

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