Building an autograd engine in pure Rust (www.evis.dev)

A developer published a hands‑on walkthrough building a minimal autograd engine in pure Rust called nanograd, not to rival PyTorch on features or speed but to demystify what happens under loss.backward(). The post reconstructs the three core autograd pieces — a computational graph (DAG), a tensor wrapper that stores data/grad and links into the graph, and per‑operation gradient functions that save forward context and implement backwards logic — then implements them step‑by‑step, tests basic ops, and uses a small PyTorch comparison to validate behavior. Technically, nanograd centers on a TensorData struct (data, grad, prev, backward_fn) wrapped in Arc<Mutex<...>> for thread safety. Backward_fn is a boxed dynamic closure (Box<dyn Fn() + Send>) so different ops can attach custom backward behavior. The author implements new/data/grad getters, tracks parents in prev, and plans basic ops (add, mul, pow), non‑linearities, backward traversal, and a training loop to build a toy neural net. A small gotcha highlighted: this toy engine uses 0.0 for uninitialized grads versus PyTorch’s None. The writeup is a practical primer for anyone wanting an intuition for autograd internals, shows how Rust’s ownership and concurrency model shapes design choices, and provides a clear foundation for experimenting with Rust‑native ML tooling.