raven
Raven is a comprehensive ecosystem for scientific computing in OCaml.
Everything you know from Python, rebuilt with type safety and functional programming:
| Raven | Python equivalent | Description |
|---|---|---|
| nx | NumPy | N-dimensional arrays with CPU and Metal backends |
| hugin | Matplotlib | 2D plotting with Cairo and SDL backends |
| quill | Jupyter | Notebooks as markdown files |
| rune | JAX | Automatic differentiation (JIT coming post-v1) |
| kaun ᚲ | PyTorch/Flax | Neural network layers and optimizers |
| sowilo ᛋ | OpenCV | Differentiable computer vision operations |
why raven?
Python's monopoly on scientific computing forces an impossible choice: use Python for everything, or prototype in Python then rewrite for production.
We think there's a better way. OCaml lets you prototype as quickly as Python, but the same code scales to production. Strong typing catches bugs before they crash your ML pipeline. JIT compilation matches NumPy/PyTorch performance. Pluggable backends let you run the same code on CPU, Metal, or CUDA.
see it in action
Here's what scientific computing looks like with type safety:
(* Load and plot some data *) open Nx open Hugin let x = linspace float32 0. (2. *. Float.pi) 100 let y = Nx.map Float.sin x let noise = randn float32 (shape x) |> mul_scalar 0.1 let y_noisy = add y noise let fig = figure () let ax = subplot fig let _ = ax |> Plotting.scatter ~x ~y:y_noisy ~alpha:0.5 ~label:"data" |> Plotting.plot ~x ~y ~color:Artist.Color.red ~label:"true" |> Axes.legend in show fig (* Type error at compile time, not runtime: *) (* let z = add x (ones float64 [|50|]) -- Won't compile! *)
The compiler catches shape and dtype mismatches before they crash your pipeline.
join us
Raven is pre-alpha. Things will break. APIs will change. But the foundations are solid.
We're building in public and need your help:
- Try it out - Install the libraries, run the examples, tell us what breaks
- Report issues - Found a bug? Missing a feature? Let us know
- Join the conversation - API decisions happen in GitHub discussions
- Contribute - Check out good first issues
support the project
Building a complete scientific computing ecosystem takes time and focus. We're raising funds to work on Raven full-time and deliver on our roadmap.
Your sponsorship helps us release a stable V1 with GPU backends, achieve NumPy/PyTorch performance parity, and build comprehensive documentation and tutorials.