PyGo: A Deep Learning Framework Where Go Calls Python Calls C++

PyGo: A Deep Learning Framework Where Go Calls Python Calls C++

[Project] PyGo - embedding CPython inside a Go process to build a deep learning framework.

I've been working on something a bit unusual: a deep learning framework where Go is the top-level API, Python handles autograd and the model zoo, and C++/CUDA does the raw compute.

The architecture looks like this:

Go API → CGo bridge → CPython (embedded) → pybind11 → CUDA/AVX-512 kernels

The key insight: instead of a Python sidecar in every pod, CPython runs inside the Go binary. Tensors live in shared memory - zero-copy across all three layers.

Why Go on top?

Go is already running most ML infrastructure (K8s, Prometheus, etcd). PyGo makes models first-class citizens there, without rewriting your ops stack. Goroutines make dynamic batching trivial.

Current state

• LLaMA-3, GPT-2, BERT, ViT, Whisper partially implemented
• Flash Attention v2, GPTQ/AWQ quantisation
• FSDP, DPO, SFT trainers

Early stage - looking for Go + C++/CUDA contributors.

The main unsolved problem is CGo call overhead at the tensor boundary. If anyone has experience embedding CPython in a Go process, I'd love to talk.

Looking for core contributors - especially Go devs with CGo experience, Python autograd engineers, and C++/CUDA kernel writers.

Interested in joining? Email: ami***********@gmail.com