Roadmap

Raven is currently in alpha. We've focused on the core infrastructure to train large language models. We're successfully training GPT2 on CPU using the full Raven stack (Kaun → Rune → Nx).

Alpha Releases ✅

alpha1 has been released with three new libraries (Talon, Saga, Fehu) and major enhancements to Nx, Rune, and Kaun. This represents the complete scope for alpha.

Future alpha releases will focus exclusively on bug fixes and stability improvements. No new features are planned for the alpha cycle.

Key achievements:

  • Complete Nx numerical computing capabilities (linear algebra, FFT, extended dtypes)
  • Expanded Kaun with high-level training APIs inspired by PyTorch and Flax
  • Successfully trained GPT2 using the full Raven stack on CPU
  • Added DataFrame processing (Talon), NLP (Saga), and reinforcement learning (Fehu)

Beta: JIT Compilation & Performance (Current Stage)

The beta cycle will have a single focus: 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
  • Achieve competitive performance on common deep learning tasks

V1: Developer Experience

Once performance is competitive using JIT compilation, V1 will focus on developer experience and documentation:

Developer Tooling

  • Complete Hugin (plotting library) with publication-quality visualizations
  • Complete Quill (notebook environment) for interactive data science
  • Integrated workflows for data scientists coming from Python

Documentation

  • Comprehensive tutorials and getting-started guides
  • Complete API reference documentation
  • Migration guides for NumPy/PyTorch users
  • Real-world examples and case studies

API Stability

  • Finalize and stabilize all public APIs
  • Ensure backward compatibility guarantees

Post-V1: Production Scale

After V1, we'll focus on scaling for real-world production constraints:

Distributed Training

  • Multi-GPU training on a single machine
  • Distributed training across multiple machines
  • Efficient data parallelism and model parallelism

Deployment

  • Model serving infrastructure
  • Optimization for inference workloads
  • Integration with deployment platforms

Production Readiness

  • Monitoring and observability tools
  • Performance profiling and optimization
  • Enterprise support and stability guarantees

The path is clear: alpha proves the concept, beta matches Python's performance, V1 delivers great developer experience, and post-V1 enables production ML at scale.