Incin, a rust machine learning framework for setting fire to dimensionality bugs

A developer has released Incin, a deep learning framework in Rust that encodes a tensor's shape, dtype, device, and gradient state into its type, turning mismatches into compiler errors. The project aims to explore how much of the tensor contract can be carried by the type system while remaining flexible and user-friendly.

Incin https://github.com/xupremix/incin is a deep learning framework in Rust where a tensor’s shape, dtype, device and gradient state all live in its type. Shape, dtype, device mismatches and so on are compiler errors. js use incin::prelude:: ; let x = Cpu.randn shape 4, 8 ?; let w = Cpu.randn shape 8, 2 ?; let y = x.matmul &w ?; // 4, 8 x 8, 2 - 4, 2 let bad = Cpu.randn shape 3, 8 ?; let = x.matmul &bad ?; // inner dims 8 and 3: does not compile The main goal was to find out how much of the tensor contract the type system can genuinely carry, how flexible we can make it, and how pleasant to work with it can be. If you're interested on the features, architecture or anything else: