bayadera

Both. See https://github.com/uncomplicate/bayadera too. Whatever the language, you have to make function calls to cuDNN, because cuDNN is optimized by Nvidia, which put serious resources into it. Even if you wrote everything in C++/CUDA, you probably can't match it for the standard stuff. The custom things that I write, I prefer to write in CUDA kernels + Clojure management code. Clojure kernels are technically possible in the same way as Julia or whatever kernels are possible, but the thing is that you have to match low-level hardware features anyway, so CUDA C/C++ just makes more sense for kernels, which are tiny part of code anyway.

Not sure what they cover, but there are surely libraries available, https://deeplearning4j.org/ https://github.com/originrose/cortex http://www.onyxplatform.org/ https://neanderthal.uncomplicate.org/ https://clojurecuda.uncomplicate.org/ https://clojurecl.uncomplicate.org/ https://github.com/uncomplicate/bayadera https://devblogs.nvidia.com/grcuda-a-polyglot-language-bindi... Also since Pytorch 1.4 there is initial support for Java, built on top of the new pure C++ API.

We have used clj-ml[0], which wraps a bunch of Weka stuff, as well as the XGBoost JVM bindings. I've used Bayadera[1] and Dragan's other tools for linear algebra, although not in production. I was always sad that Incanter didn't really go anywhere, but at this point I wouldn't be surprised if Clojure became a respectable platform for data science, especially given Clojurists Together's funding focus this quarter[2]. 0: https://github.com/shark8me/clj-ml 1: https://github.com/uncomplicate/bayadera 2: https://www.clojuriststogether.org/news/q4-2019-funding-anno...

Dunno if you're aware of Bayadera[1], but if you happen to like a Bayesian workflow and don't rely overmuch on ggplot2 niceness, you can actually go a whole day without touching R. 1: https://github.com/uncomplicate/bayadera

Yes. See https://github.com/uncomplicate/bayadera/tree/master/test/cl... and https://www.youtube.com/watch?v=bEOOYbscyTs https://www.youtube.com/watch?v=TGxYfi3Vi3s

Thanks for mentioning Neanderthal! http://neanderthal.uncomplicate.org http://github.com/uncomplicate/neanderthal It aims to have more features, and more speed than numpy, with Clojure, on the JVM + Nvidia + AMD + Intel. Also relevant here is Bayadera, Clojure/GPU Bayesian modeling lib for the JVM: http://github.com/uncomplicate/bayadera

Source: https://github.com/uncomplicate/neanderthal Also used for Bayesian data analysis on the GPU in: https://github.com/uncomplicate/bayadera

Direct link to Getting Started: http://clojurecl.uncomplicate.org/articles/getting_started.h... source code: https://github.com/uncomplicate/clojurecl Heavily used in other Clojure libraries: https://github.com/uncomplicate/neanderthal https://github.com/uncomplicate/bayadera Also, the CUDA equivalent: https://github.com/uncomplicate/clojurecuda

Dragan has an excellent platform for Bayesian Data Analysis: https://github.com/uncomplicate/bayadera I first learned about it while moaning about Incanter in previous Clojure threads! But I don't think it's broad enough (I realise it doesn't aim to be) to usefully capture enough mindshare that Clojure is a first-class citizen in the numerical computing space.

This doesn't even mention Bayadera, his Bayesian data analysis environment: https://github.com/uncomplicate/bayadera

WRT Incanter, you might find some high-performance libraries interesting: http://neanderthal.uncomplicate.org and http://github.com/uncomplicate/bayadera. They are much more low-level than Incanter, but also orders of magnitude faster, and intended as a foundation for the Incanter-like libraries to be built on top. They are actively developed.

... and if you like Clojure, you might try Bayadera, which has its own engine running the analysis on the GPU. http://github.com/uncomplicate/bayadera