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This is just an example post. Don't take it too seriously!

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Welcome to the AI Documentation. This site provides detailed guides and resources for developing and deploying AI solutions.

This documentation provides an overview of the tools, libraries, and best practices for working with AI projects, focusing on efficient development workflows and deployment strategies.

Setup

System Requirements

Python: >= 3.8
Frameworks: PyTorch, TensorFlow, ONNX
Dependencies: Docker, CUDA (for GPU)

Installation

Clone the repository:

git clone https://github.com/your-repo/ai-project.git
cd ai-project

Install dependencies:
```
pip install -r requirements.txt
```
Verify installation:
```
python -m your_project --version
```

Features

Model Training

Supports custom dataset loading with PyTorch DataLoader.
Implements distributed training for large-scale datasets.
Auto-saves best-performing models during training.

Model Deployment

Deploy models using FastAPI for RESTful APIs.
Supports ONNX Runtime and TensorRT for optimized inference.
Examples included for deploying on NVIDIA Triton.

Usage

APIs

To start the API server:

uvicorn app.main:app --reload

Access the documentation at http://127.0.0.1:8000/docs.

Examples

Run the following command to test inference:

python scripts/infer.py --input sample_image.jpg

Contributing

Fork the repository.
Create a new branch for your feature:
```
   git checkout -b feature-name
```
Commit and push your changes.
Submit a pull request.