Deployment Guide for AI Toolkit

Welcome to the AI Toolkit Deployment Guide! 🚀 This tutorial will walk you through the steps to deploy your AI models efficiently using our toolkit.

Table of Contents

• Getting Started
• Deployment Steps
• Common Issues
• Further Reading

Getting Started

Before deployment, ensure your environment is set up correctly:

• Install the latest version of AI Toolkit
• Verify dependencies with pip install -r requirements.txt
• Test your model locally using the Quick Start Guide

Deployment Steps

1. Prepare Your Model

   • Convert your model to the supported format (ONNX, TensorFlow, PyTorch)
   • Use the Model Conversion Tool for automated processing

2. Configure Deployment Settings

   • Edit the config.yaml file to specify hardware and network parameters
   • Enable GPU acceleration if available

3. Deploy Using CLI
   Run the command:

   ai_toolkit deploy --model_path ./your_model_file --config ./config.yaml
   

Common Issues

• Error: Model not found
  Double-check the model_path in your configuration file.
• High latency during inference
  Optimize your model using the Optimization Guide.

Further Reading

• API Reference for advanced deployment options
• Performance Tuning Tips to maximize efficiency