94 lines
3.8 KiB
Markdown
94 lines
3.8 KiB
Markdown
# RustyTorch++ Project Brief
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## Project Overview
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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.
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## Core Mission
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Build a production-ready ML framework that combines:
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- **Rust's memory safety** with **GPU-native performance**
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- **PyTorch-like ergonomics** with **zero-cost abstractions**
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- **Agent-driven development** for continuous optimization
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- **Seamless scaling** from single GPU to multi-region clusters
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## Target Audience
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1. **ML Engineers** seeking better performance and safety
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2. **Systems Engineers** requiring predictable resource usage
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3. **Research Teams** needing scalable distributed training
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4. **Production Teams** wanting robust inference serving
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## Key Requirements
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### Functional Requirements
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- Complete tensor operations library with autograd
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- GPU kernel compilation via custom `rustg` compiler
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- Distributed training with Stratoswarm orchestration
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- Model import/export compatibility (ONNX, PyTorch)
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- Optimized inference runtime with quantization
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- Cross-language bindings (Python, C++, WASM)
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### Non-Functional Requirements
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- **Performance**: Sub-50ms inference latency, linear multi-GPU scaling
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- **Safety**: MIRI-verified memory safety, no data races
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- **Reliability**: 99.99% uptime for inference serving
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- **Scalability**: Support 10,000+ node clusters
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- **Developer Experience**: Intuitive APIs, comprehensive docs
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## Success Criteria
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1. Match or exceed PyTorch performance benchmarks
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2. Achieve zero memory safety violations in production
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3. Support major model architectures (transformers, CNNs, RNNs)
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4. Enable seamless migration from existing PyTorch codebases
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5. Build active open-source community with 1000+ contributors
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## Project Boundaries
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### In Scope
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- Core ML framework implementation
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- GPU compiler integration
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- Distributed training infrastructure
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- Inference serving stack
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- Python/C++ bindings
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- Documentation and examples
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### Out of Scope (Phase 1) → NOW IMPLEMENTED ✅
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- ~~Mobile/edge deployment~~ → ✅ rtx-edge crate
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- ~~Custom silicon support~~ → ✅ Metal backend for Apple Silicon
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- ~~AutoML capabilities~~ → ✅ rtx-auto, rtx-automeasure crates
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- ~~Federated learning~~ → ✅ rtx-federated crate
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## Development Philosophy
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- **Safety First**: Every unsafe block must be justified and verified
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- **Performance Without Compromise**: Zero-cost abstractions everywhere
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- **Agent-Augmented**: Leverage AI agents for optimization and evolution
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- **Community-Driven**: Open development with transparent roadmap
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- **Production-Ready**: Every feature must be battle-tested
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## Phase-Based Delivery ✅ ALL COMPLETE
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Development followed 14 phases from foundation to ecosystem:
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- **Phases 0-2**: Core framework and GPU integration ✅
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- **Phases 3-5**: Distributed training and data pipelines ✅
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- **Phases 6-8**: Inference, quantization, monitoring ✅
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- **Phases 9-10**: Auto-tuning and 1.0 release ✅
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- **Phase 11**: PyTorch feature parity ✅
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- **Phase 12**: Superset expansion (GNN, Diffusion, RL, Multimodal) ✅
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- **Phase 13**: Classical ML (sklearn compatibility) ✅
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- **Post-1.0**: Rust 2024 Edition Migration ✅
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Each phase delivered working software with clear value propositions.
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## Current Status (December 2024)
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### Build Health
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- **Compilation**: ✅ `cargo check --workspace` passes
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- **Total Crates**: 56+
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- **Rust Edition**: 2024 (Rust 1.92+)
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### Recent Completion: Rust 2024 Migration
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- rtx-nlg compilation fixed (245+ errors → 0)
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- nom 3.2.1 removed from dependency tree
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- Float comparisons updated to `total_cmp()` (200+ files)
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- integration_tests excluded (future work)
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---
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*Project Brief Last Updated: 2025-12-16* |