rustytorch/memory-bank/projectbrief.md

RustyTorch++ Project Brief

Project Overview

RustyTorch++ is a GPU-native, high-performance machine learning framework written entirely in Rust, designed as a memory-safe replacement for PyTorch. It leverages custom GPU compilation, distributed training, and agent-driven development to achieve superior performance and safety guarantees.

Core Mission

Build a production-ready ML framework that combines:

• Rust's memory safety with GPU-native performance
• PyTorch-like ergonomics with zero-cost abstractions
• Agent-driven development for continuous optimization
• Seamless scaling from single GPU to multi-region clusters

Target Audience

1. ML Engineers seeking better performance and safety
2. Systems Engineers requiring predictable resource usage
3. Research Teams needing scalable distributed training
4. Production Teams wanting robust inference serving

Key Requirements

Functional Requirements

• Complete tensor operations library with autograd
• GPU kernel compilation via custom rustg compiler
• Distributed training with Stratoswarm orchestration
• Model import/export compatibility (ONNX, PyTorch)
• Optimized inference runtime with quantization
• Cross-language bindings (Python, C++, WASM)

Non-Functional Requirements

• Performance: Sub-50ms inference latency, linear multi-GPU scaling
• Safety: MIRI-verified memory safety, no data races
• Reliability: 99.99% uptime for inference serving
• Scalability: Support 10,000+ node clusters
• Developer Experience: Intuitive APIs, comprehensive docs

Success Criteria

1. Match or exceed PyTorch performance benchmarks
2. Achieve zero memory safety violations in production
3. Support major model architectures (transformers, CNNs, RNNs)
4. Enable seamless migration from existing PyTorch codebases
5. Build active open-source community with 1000+ contributors

Project Boundaries

In Scope

• Core ML framework implementation
• GPU compiler integration
• Distributed training infrastructure
• Inference serving stack
• Python/C++ bindings
• Documentation and examples

Out of Scope (Phase 1) → NOW IMPLEMENTED ✅

• Mobile/edge deployment → ✅ rtx-edge crate
• Custom silicon support → ✅ Metal backend for Apple Silicon
• AutoML capabilities → ✅ rtx-auto, rtx-automeasure crates
• Federated learning → ✅ rtx-federated crate

Development Philosophy

• Safety First: Every unsafe block must be justified and verified
• Performance Without Compromise: Zero-cost abstractions everywhere
• Agent-Augmented: Leverage AI agents for optimization and evolution
• Community-Driven: Open development with transparent roadmap
• Production-Ready: Every feature must be battle-tested

Phase-Based Delivery ✅ ALL COMPLETE

Development followed 14 phases from foundation to ecosystem:

• Phases 0-2: Core framework and GPU integration ✅
• Phases 3-5: Distributed training and data pipelines ✅
• Phases 6-8: Inference, quantization, monitoring ✅
• Phases 9-10: Auto-tuning and 1.0 release ✅
• Phase 11: PyTorch feature parity ✅
• Phase 12: Superset expansion (GNN, Diffusion, RL, Multimodal) ✅
• Phase 13: Classical ML (sklearn compatibility) ✅
• Post-1.0: Rust 2024 Edition Migration ✅

Each phase delivered working software with clear value propositions.

Current Status (December 2024)

Build Health

• Compilation: ✅ cargo check --workspace passes
• Total Crates: 56+
• Rust Edition: 2024 (Rust 1.92+)

Recent Completion: Rust 2024 Migration

• rtx-nlg compilation fixed (245+ errors → 0)
• nom 3.2.1 removed from dependency tree
• Float comparisons updated to total_cmp() (200+ files)
• integration_tests excluded (future work)

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Project Brief Last Updated: 2025-12-16