[AIR-3][AIS-3][BPC-3][RES-3]

AI/ML Development Guide

Overview

Add a brief overview of this document here.

Table of Contents

This guide provides instructions for developing and contributing to AI/ML components in Anya Core.

Development Environment Setup

Prerequisites

  • Rust 1.70+ (nightly recommended for some features)
  • Python 3.9+ (for model training and data processing)
  • CUDA 11.8+ (for GPU acceleration)
  • Docker (for containerized development)

Installation

  1. Clone the repository: bash git clone https://github.com/anya-org/anya-core.git cd anya-core

  2. Install Rust toolchain: bash curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh rustup toolchain install nightly rustup default nightly

  3. Install Python dependencies: bash pip install -r requirements-ai.txt

Project Structure

anyan-core/
├── src/
│   ├── ai/                  # Core AI/ML functionality
│   │   ├── models/          # Model implementations
│   │   ├── training/        # Training pipelines
│   │   └── inference/       # Inference services
│   └── ...
├── docs/
│   └── ai/                 # AI/ML documentation
│       ├── ARCHITECTURE.md  # System architecture
│       ├── METRICS.md      # Performance metrics
│       └── COMPLIANCE.md   # Compliance requirements
└── ...

Adding a New Model

  1. Create a new module in src/ai/models/
  2. Implement the required traits: ```rust use anya_ai::traits::Model;

pub struct MyModel { // Model state }

#[async_trait::async_trait] impl Model for MyModel { async fn infer(&self, input: Value) -> anyhow::Result { // Implementation Ok(Value::Null) } } ```

  1. Register the model in src/ai/mod.rs
  2. Add unit tests and documentation

Testing

Run the test suite:

# Run all tests
cargo test

# Run AI/ML specific tests
cargo test -p anya-ai

# Run with GPU support
CUDA_VISIBLE_DEVICES=0 cargo test --features=cuda

Performance Optimization

  • Use #[inline] for small, frequently called functions
  • Pre-allocate memory when possible
  • Use batch processing for inference
  • Profile with cargo flamegraph

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Open a pull request

Code Style

Follow the Rust API Guidelines and the project's coding standards.

License

This project is licensed under the MIT License.

See Also