# Module kmath-noa

A Bayesian computation library over
[NOA](https://github.com/grinisrit/noa.git)
together with relevant functionality from 
[LibTorch](https://pytorch.org/cppdocs). 

Our aim is to cover a wide set of applications from particle physics
simulations to deep learning. In fact, we support any 
differentiable program written on top of 
`AutoGrad` & `ATen`.

## Installation

To install the library, you can simply publish to the local
Maven repository:
```
./gradlew -q :kmath-noa:publishToMavenLocal
```
This will fetch and build native artifacts as well.
Currently, we support only
the [GNU](https://gcc.gnu.org/) toolchain. For `GPU` kernels, we require a compatible
[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
installation. If you are on Windows, we recommend setting up
everything on [WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html).