I am building numgraph, a lightweight and high-performance numerical core for learnable graphs, designed with a C++ backend and clean Python bindings to handle complex graph-based tensor operations and deep learning workflows efficiently.
The core goal is to provide fast, modular primitives for graph representation learning, spatial/relational computations, and seamless integration with modern deep learning stacks like PyTorch.
As the project moves into active development, I am looking for passionate co-maintainers and contributors to help build out:
C++ Core & Bindings: Optimizing numerical routines and writing robust Python/C++ extension bindings.
Graph Primitives: Developing efficient algorithms for learnable graph structures and operations.
Ecosystem Integration: Benchmarking, testing pipelines, and ensuring smooth compatibility with PyTorch modules.
If you love systems programming, C++/Python interoperability, or graph machine learning, I would love to collaborate with you to shape this from the ground up!
This looks promising, especially the focus on keeping the Python bindings clean while the graph primitives evolve. A small set of end-to-end benchmarks (including autograd and CPU/GPU transfer costs) would make the trade-offs very easy to evaluate. I’d also be interested in seeing how you plan to handle ABI compatibility as the C++ core changes.
Thanks for the insights! We’re actively building out the C++ backend and benchmark suite today. If you’re interested in shaping the architecture or co-maintaining numgraph, we’d love to have you on board! If you want to start contributing, check this link: