j4orz / teenygrad
a teaching deep learning framework: the bridge from micrograd to tinygrad
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Repository Overview (README excerpt)
Crawler viewThe teaching deep learning framework for the SITP textbook. *train nanogpt by building teenygrad in Python and Rust — the bridge from micrograd to tinygrad* --- Installation Graph Mode graph mode (developed in part 3 of the book) is a pure Python Tensor compiler. Eager Mode eager mode (developed in part 1 and 2 of the book) has a mixed source of Python, Rust, and CUDA Rust in order to support CPU and GPU acceleration. The Python to Rust interop is implemented using CPython Extension Modules via , with the shared object files compiled by driving via PyO3's build tool . **CPU kernels (x86/ARM)** • CPU kernels do not use the docker container (for now). **GPU kernels (PTX)** To enable GPU acceleration, teenygrad uses CUDA Rust, which in turn requires a specific version matrix required (notably an old version of LLVM) and so CUDA Rust's provided docker containers and shell scripts are used. • Install NVIDIA Container Toolkit on your machine • Run the following in your shell: • Point to the Rust and CUDA Rust source: