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 t
[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? 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. amitsarkar1066@gmail.com
Key Takeaways
- •[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 t
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