The goal of the project is to bring open ABI and FFI for machine learning systems.
- Stable, minimal C ABI designed for kernels, DSLs, and runtime extensibility.
- Zero-copy interop across PyTorch, JAX, and CuPy using DLPack protocol.
- Compact value and call convention covering common data types for ultra low-overhead ML applications.
- Multi-language support out of the box: Python, C++, and Rust (with a path towards more languages).
- Stable, minimal C ABI designed for kernels, DSLs, and runtime extensibility. - Zero-copy interop across PyTorch, JAX, and CuPy using DLPack protocol. - Compact value and call convention covering common data types for ultra low-overhead ML applications. - Multi-language support out of the box: Python, C++, and Rust (with a path towards more languages).