Deep Learning Playground


A minimal deep learning library.

Modern deep learning frameworks are too big. PyTorch and TensorFlow are hundreds of thousands of lines of code; their immense complexity makes adding new features like hardware acceleration difficult.

Nanograd is simple. Less code. Fewer operations. Built with Rust 🦀.

View Playground on GitHub

View Nanograd on GitHub


Nanograd represents networks as a dynamic DAG. Optimization is handled by backpropagation. However, the DAG only operates over scalar values. This means every neuron is separated into many add and multiply operations: simple, but slow.

The next version of nanograd will swap scalars with tensors.

How the Playground Works

The playground allows people to experiment with neural networks in the browser. Change hyperparameters, select a dataset, and add layers: all from a slick interface.

Training requests are fulfilled by nanograd. Since nanograd is written in Rust, the code is first compiled into a WebAssembly Module (WASM).

Model preferences are shared with a web worker, and the nanograd WASM executes on a separate thread. Results are sent back to the primary thread and displayed.