forked from kscience/kmath
32 lines
1.1 KiB
Markdown
32 lines
1.1 KiB
Markdown
# Module kmath-noa
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This module provides a `kotlin-jvm` frontend for the
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[NOA](https://github.com/grinisrit/noa.git)
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library together with relevant functionality from
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[LibTorch](https://pytorch.org/cppdocs).
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Our aim is to create a Bayesian computational platform
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which covers a wide set of applications from particle physics
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simulations to deep learning and general differentiable programs
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written on top of `AutoGrad` & `ATen`.
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## Installation
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Currently, to build native artifacts, we support only
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the [GNU](https://gcc.gnu.org/) toolchain. For `GPU` kernels, we require a compatible
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[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
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installation. If you are on Windows, we recommend setting up
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everything on [WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html).
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To install the library, you have to publish
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locally `kmath-core`, `kmath-tensors` with `kmath-noa`:
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```
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./gradlew -q :kmath-core:publishToMavenLocal :kmath-tensors:publishToMavenLocal :kmath-noa:publishToMavenLocal
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```
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This builds `jtorch` a JNI wrapper for `NOA/LibTorch`, placed inside:
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`~/.konan/third-party/kmath-noa-<version>/cpp-build`
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