2021-06-26 00:36:01 +03:00
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# Module kmath-noa
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2021-07-11 21:01:50 +03:00
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A Bayesian computation library over
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[NOA](https://github.com/grinisrit/noa.git)
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together with relevant functionality from
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[LibTorch](https://pytorch.org/cppdocs).
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2021-07-11 21:01:50 +03:00
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Our aim is to cover a wide set of applications from particle physics
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simulations to deep learning. In fact, we support any
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differentiable program written on top of
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`AutoGrad` & `ATen`.
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2021-06-27 18:28:28 +03:00
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## Installation
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2021-07-13 11:37:06 +03:00
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Currently, we support only
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the [GNU](https://gcc.gnu.org/) toolchain for the native artifacts.
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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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2021-07-12 23:12:02 +03:00
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To install the library, you can simply publish to the local
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Maven repository:
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```
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./gradlew -q :kmath-noa:publishToMavenLocal
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```
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This will fetch and build the `JNI` wrapper `jnoa`.
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In your own application add the local dependency:
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```kotlin
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repositories {
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mavenCentral()
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mavenLocal()
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}
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dependencies {
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implementation("space.kscience:kmath-noa:0.3.0-dev-14")
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}
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```
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To load the native library you will need to add to the VM options:
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```
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-Djava.library.path=${HOME}/.konan/third-party/noa-v0.0.1/cpp-build/kmath
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```
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