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Starter Kit Tutorial

This guide will walk you through setting up the NaLamKI SDK and running the example service. By the end, you'll have a working example that you can use as a foundation for building your own AI services for the NaLamKI platform.

Installation​

Prerequisites​

Before installing the SDK, make sure you have the following software installed on your system:

  • Operating System: Windows, macOS, or Linux
  • Python: Version 3.7 or higher
  • Docker: Version 20.10 or higher
  • Git: For cloning repositories (optional but recommended)

Step 1: Install Python​

If you don't have Python installed, download and install it from the official Python website

Verify the installation:

python --version
# Output should be Python 3.7.x or higher

Step 2: Install Docker​

Download and install Docker from the official Docker website

Verify the installation:

docker --version
# Output should be Docker version 20.10.x or higher

Step 3: Install the NaLamKI SDK​

Install the SDK using pip:

pip install git+https://github.com/NaLamKI/SDK

Verify the installation:

pip show nalamkisdk
# Should display information about the installed SDK

System Requirements​

Ensure your system meets the following requirements to avoid any installation or runtime issues:

  • Operating System: Windows 10 or higher, macOS Catalina or higher, or a recent Linux distribution
  • Python: Version 3.7 or higher
  • Docker: Version 20.10 or higher
  • Disk Space: At least 20 GB of free space for installations and Docker containers
  • Memory: Minimum 4 GB RAM (8 GB recommended)
  • Internet Connection: Required for downloading packages and dependencies and deployment of services

Running the Example Service​

Step 1: Clone the Starter Kit Repository​

The Starter Kit provides a template for setting up your AI service. Clone the repository to your local machine:

git clone git@github.com:NaLamKI/StarterKit.git
cd StarterKit

Step 2: Set Up a Virtual Environment​

It's recommended to use a virtual environment to manage dependencies:

python -m venv venv
# Activate the virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

Step 3: Install Dependencies​

Install the required packages using pip:

pip install -r src/requirements.txt

Step 4: Run the Example Service​

Execute the example script to ensure everything is set up correctly:

python test/test.py

This will run the example service that detects green colors in images. The test data must be located in test/action/input, and all results are saved into test/action/output.

To visualize the results, execute:

python src/visualize_outputs.py

What's Happening?​

The example service demonstrates a simple AI model that detects green colors in images. When you run the test script:

  1. The service loads images from the test/action/input directory
  2. It processes each image to detect green colors
  3. It saves the results to the test/action/output directory
  4. The visualization script displays the results

This is a basic example to get you started. In a real-world scenario, you would replace the green color detection with your own AI model for agricultural applications.

Project Structure​

The StarterKit has the following structure:

StarterKit/
├── src/                # Source code - This is where you'll develop your service
│   ├── service.py      # Main service class - Your primary development file
│   ├── requirements.txt # Project dependencies
│   └── visualize_outputs.py # Visualization utilities
├── test/               # Test implementation (for testing purposes only)
│   ├── service.py      # Test service implementation
│   ├── test.py         # Test runner
│   └── action/
│       ├── input/      # Test input data
│       └── output/     # Test output data
└── Dockerfile          # Container configuration

Next Steps​

Now that you have a working example, you can:

  1. Learn About Data Structures

  2. Build Your Own Service

Resources​