PyGo Framework Embeds CPython in Go Binary for Deep Learning Workloads

A developer has built an experimental deep learning framework called PyGo that uses Go as the top-level API, embedded CPython for autograd and model management, and C++/CUDA for low-level computation. The architecture connects these layers via a CGo bridge and pybind11, with tensors stored in shared memory to avoid data copying across layers. Several models including LLaMA-3, GPT-2, BERT, and Whisper are partially implemented, alongside features like Flash Attention v2 and GPTQ/AWQ quantisation. The project is at an early stage, and the creator is actively seeking contributors with expertise in Go/CGo, Python autograd, and C++/CUDA kernel development. A key technical challenge currently under investigation is the CGo call overhead at the tensor boundary between Go and the embedded Python runtime.