Deep Learning on the GPU in Clojure from Scratch: Sharing Memory

dragan.rocks

105 points·dragandj··10 comments

`uncomplicate` libraries are awesome. Thanks so much for all this write up and putting out one of my favorite pieces of open source code.

It's just so easy to rapidly iterate when it comes to doing experiments on the GPU with Clojure and these abstractions. It just feels right

I had to deal with similar memory corruption problems but I could just piggy back on Clojure's concurrency primitives to keep this under control without pulling my hair out with all the ugly boiler plate and having that sweet sweet repl on the side.

`(with-release...)`? Oh yes! Please.

<end-rant> The sad part, like all Clojure code I have written in the past, I am having a more social problem of convincing people in my team to use tools like these to make their lives easier, especially when you can quantify the productivity gained. People now see me as the lone crazy lisp guy in the corner and is making me question a lot of absurdities in the software industry. </end-rant>

Thank you for the kind words!

part 5. i’ve got a lot of catching up now.

Kudos for the effort, I am looking forward to using Clojure for deep learning!

is there a list of 'data science' libs for clojure somewhere?

There's Incanter[1], but I've never used it, so can't comment on how good or complete it is. And of course there are the article author's libraries: https://dragan.rocks/software/

[1] https://data-sorcery.org/

This is a good starting point, but there is also a lot more out there https://www.clojure-toolbox.com / https://github.com/weavejester/clojure-toolbox.com

mxnet is also on Clojure https://github.com/apache/incubator-mxnet/tree/master/contri...

Not a list, but here's another lib:

https://github.com/originrose/cortex

Dragan does it again

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