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.
Vladimir.
Happy coding, Ed :)
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