Roadmap
Current Status
Raven is in alpha. The core stack (Nx → Rune → Kaun) works end-to-end: we have successfully trained GPT-2 on CPU using the full Raven stack.
| Library | Status | What works |
|---|---|---|
| nx | Alpha | Full NumPy-like API, linear algebra, FFT, I/O (npy, images) |
| rune | Alpha | Reverse and forward-mode AD, vmap, gradient checking |
| kaun | Alpha | Layers, optimizers, training loops, HuggingFace Hub, MNIST/GPT-2 examples |
| brot | Alpha | All 5 algorithms, full pipeline, HF tokenizer.json compat, training |
| talon | Alpha | DataFrames, row operations, aggregations, CSV I/O |
| hugin | Alpha | 2D/3D plots, scatter, bar, contour, images |
| fehu | Alpha | Environments (CartPole, GridWorld, MountainCar), vectorized envs, GAE |
| sowilo | Alpha | Geometric transforms, filters, edge detection, morphological ops |
| quill | Alpha | TUI, batch eval, markdown notebook format |
APIs will change. Bug reports and feedback are welcome.
Beta: JIT Compilation & Performance
The beta cycle will focus on JIT compilation with performance close to PyTorch.
- Complete LLVM-based JIT compiler for Rune
- Target CPU, CUDA, and Metal for hardware acceleration
- Optimize compilation pipeline and runtime performance
- Benchmark against PyTorch on standard workloads
V1: Developer Experience & Production Scale
Once performance is competitive, V1 will focus on developer experience and production readiness:
- Comprehensive documentation and tutorials
- Finalize and stabilize all public APIs
- Delightful developer tooling (Quill notebooks, Kaun-board training dashboard)
- Migration guides for NumPy/PyTorch users
- Multi-GPU and distributed training
- Model serving infrastructure
- Deployment through MirageOS