# RustyTorch++ User Guide

Welcome to RustyTorch++, **The Complete Rust AI/ML Platform** - 62 crates, 300K+ lines of code, spanning from scientific computing to production LLM inference.

## What is RustyTorch++?

RustyTorch++ is not just a deep learning framework - it's a **complete AI/ML platform** that provides:

- **LLM-First Architecture**: Flash Attention (FP16 + FP8), Ring Attention for 16M+ tokens, speculative decoding with 6 strategies
- **GPU-Native Tensors**: Zero-copy operations with CUDA, ROCm, Metal, WebGPU support
- **Production Ready**: Continuous batching, serving API, WASM browser inference, monitoring
- **Memory Safe**: Rust's ownership model prevents common GPU programming errors
- **Scientific Computing**: CFD, FEA, Physics-Informed Neural Networks
- **Enterprise Features**: Byzantine-robust federated learning, multi-tenant platform

> **RustyTorch++ vs Burn**: Burn is a research framework with ~20 crates. RustyTorch++ is an enterprise platform with 62 crates, 2.8x faster LLM inference, and capabilities Burn doesn't have (Ring Attention, CFD, federated learning, production serving).

## Framework Architecture

The framework is organized into **62 specialized crates** across 8 categories:

| Category | Crates | Key Features |
|----------|--------|--------------|
| **Core** | 13 | Tensor, runtime, autograd, Metal, bindings, SAE |
| **Training** | 13 | Flash Attention (FP8), distributed, RLHF, federated |
| **Models** | 8 | Vision, NLG, diffusion, multimodal, TTS |
| **Production** | 7 | Inference (speculative), serving, WASM+WebGPU, streaming |
| **Specialized** | 11 | CFD, FEA, PINNs, GNN, classical ML |
| **Data** | 3 | ETL pipelines, feature store, validation |
| **Tooling** | 6 | Benchmarks, evaluation, meta crates |
| **Integration** | 1 | Jupyter/Rustybooks |

## Quick Example

```rust
use rtx_tensor::{Tensor, Device};
use rtx_nn::{Linear, Module};

fn main() -> anyhow::Result<()> {
    // Create tensors on GPU
    let device = Device::cuda(0)?;
    let input = Tensor::randn([32, 784], &device)?;

    // Build a simple model
    let layer = Linear::new(784, 10, true, &device)?;
    let output = layer.forward(&input)?;

    println!("Output shape: {:?}", output.shape());
    Ok(())
}
```

## Who Should Use This Guide?

This guide is designed for:

- **ML Engineers** migrating from Python/PyTorch to Rust
- **Systems Engineers** building high-performance inference systems
- **Researchers** needing GPU-accelerated scientific computing
- **DevOps Engineers** deploying ML models to production

## Prerequisites

- Rust 1.92+ (nightly recommended for Rust 2024 features)
- CUDA 12.0+ for NVIDIA GPU support
- Basic familiarity with machine learning concepts

## Getting Help

- [API Documentation](https://rustytorch.github.io/rustytorch)
- [GitHub Issues](https://github.com/rustytorch/rustytorch/issues)
- [Examples](https://github.com/rustytorch/rustytorch/tree/main/examples)

## Next Steps

- [Installation](./getting-started/installation.md) - Set up your environment
- [Quick Start](./getting-started/quick-start.md) - Run your first example
- [Architecture Overview](./architecture/overview.md) - Understand the framework design