[swift-users] Netlib

Vladimir.S svabox at gmail.com
Tue Oct 3 13:38:57 CDT 2017

On 03.10.2017 19:02, Edward Connell via swift-users wrote:
> Hi All,
> Sorry something strange happened with the first post, so I am reposting this.
> I've recently wrapped up an ML framework research project that I've been working on 
> for some time.
> It addresses a lot of difficult design problems, and works around a lot of compiler 
> bugs and Linux library deficiencies.
> It's written almost entirely in Swift 4.0 with some C and Cuda kernels.
> Development and testing were done primarily on Ubuntu 16.04, but it will also build 
> on MacOS.
> Linux was the primary environment, because there aren't any modern Macs that can host 
> big NVIDIA hardware
> The framework interfaces with many common C libraries such as: *Cuda, cuDNN, lmdb, 
> png, jpeg, zlib*
> Anyone in the community that is trying to work with these libraries might benefit 
> from the Swift wrapper classes and examples of successful use. There are other 
> isolated technology pieces that might be of use also.
> Other people's projects and examples helped me along the way, so I am hoping that my 
> work will help some of you as well.
> The code and overview docs are published on GitHub
> docs: https://github.com/ewconnell/Netlib/wiki <https://github.com/ewconnell/Netlib/wiki>
> code: https://github.com/ewconnell/Netlib <https://github.com/ewconnell/Netlib>

Ed, it looks awesome! :-) Thank you for doing this and for sharing this!

I'm a beginner in ML world, so could you clarify some points please:

1. Do I understand correctly, that main purpose of your library is to train the 
model? So, then we can export structure(connections)&weights and use them where we 
need? Or also the purpose is efficient 'calculation' of trained model on Mac/Linux, 
so this could be used in production apps?

2. Currently this library has only Cuda compute service implemented, right? Do you 
plan to implement the same for Metal soon?

3. What are your plans to support iOs and its Metal? So, the same library/code/way 
could be used to train/experiment and to 'use' in apps on all(macOS/Linux/iOS) platforms.

Thank you.

> Happy coding, Ed :)
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