About the author, https://engineering.purdue.edu/gekcogrp/research-group/JeanM...
GNU Gneural Network
21–30 of 111 posts
Re: GNU Gneural Network
#22I agree with the general motivation that having too much AI research in the hands of software companies who keep it proprietary harms transparency and progress. But there is already a lot of neural-network free software, so why another package? For example, these widely used packages are free software, and seemingly more featureful: http://torch.ch/ , http://www.deeplearning.net/software/theano/ , http://pybrain.org/…
Almost all of the open source software in the area is permissive-licensed, and relies on non-free components (CUDA). To be honest, I'm not sure how Gneural plans to compete with those packages without support from CUDA or cuDNN, all of which are distinctly not open source.
Why not focus on adding GPLed code to an existing package with a GPL-friendly license?
Re: GNU Gneural Network
#23Earlier quoted context omitted.
I'd really like to understand the reasons behind the focus on CUDA and not OpenCL. My understanding is that nVidia and AMD made sure their hardware and software would make the GPU accessible for non-graphics tasks, but AMD's version is not functionally or legally locked to their hardware. Why hasn't OpenCL taken off and run on nVidia hardware? It seems like there must be more at play, but I'll admit a lack of insight…
I recall hearing that CUDA has much more mature tooling. Not only the already mentioned cuDNN, but the CUDA Toolkit [0] seems like a really comprehensive set of tools and libraries to help you with pretty much anything you might want to compute on a GPU. Also somewhat related: AMD seems to be moving towards supporting CUDA on its GPUs in the future: http://www.amd.com/en-us/press-releases/Pages/boltzmann-init... [0]…
On closer inspection, it looks like AMD's CUDA support consists of "run these tools over your code and it will translate it so your code does not depend on CUDA"...
Its sort of supporting CUDA, just like a car ferry sort of lets your car 'drive' across a large body of water.
Re: GNU Gneural Network
#24Aw. It should have been named the GNU Gneural Gnetwork, gno?
Re: GNU Gneural Network
#25Earlier quoted context omitted.
Almost all of the open source software in the area is permissive-licensed, and relies on non-free components (CUDA). To be honest, I'm not sure how Gneural plans to compete with those packages without support from CUDA or cuDNN, all of which are distinctly not open source.
To be honest, I'm not sure how Gneural plans to compete with those packages without support from CUDA or cuDNN, all of which are distinctly not open source. I don't see the point either. Gneural will probably never be better than Theano, Torch, Tensorflow, Caffe, et al., which are already open. If anything, time/resources are much better invested in contributing to a polished/competitive OpenCL backend to one of thes…
Caffe has an OpenCL backend - https://github.com/BVLC/caffe/tree/opencl
Re: GNU Gneural Network
#26The "ethical motivations" section is out of place here. Its moaning about "money driven companies" (as though money were a bad thing), or "monopoly" (which does not exist in AI), just reflects badly upon the project.
Re: GNU Gneural Network
#27Earlier quoted context omitted.
Almost all of the open source software in the area is permissive-licensed, and relies on non-free components (CUDA). To be honest, I'm not sure how Gneural plans to compete with those packages without support from CUDA or cuDNN, all of which are distinctly not open source.
I'd really like to understand the reasons behind the focus on CUDA and not OpenCL. My understanding is that nVidia and AMD made sure their hardware and software would make the GPU accessible for non-graphics tasks, but AMD's version is not functionally or legally locked to their hardware. Why hasn't OpenCL taken off and run on nVidia hardware? It seems like there must be more at play, but I'll admit a lack of insight…
Performance. OpenCL has been 2-5x slower for ML than CUDA. Not sure of the exact reason but I think it's the highly optimised kernels which are not there with OpenCL, but are with CuDNN. I think it's mostly a software issue, compute capacity in theory should be more or less the same with equivalent AMD/NVidia cards.
AMD should have invested much more heavily into ML, if they had, their share price would probably look a bit better than it does now.
This looks interesting - running CUDA on any GPU. http://venturebeat.com/2016/03/09/otoy-breakthrough-lets-gam...
Re: GNU Gneural Network
#28The "ethical motivations" section is out of place here. Its moaning about "money driven companies" (as though money were a bad thing), or "monopoly" (which does not exist in AI), just reflects badly upon the project.
The monopoly _does_ exist in AI: machine learning is entirely data driven, and companies like Facebook and Twitter quite literally have investors throwing money at them because they have such valuable data for that purpose. Google is no different.
Re: GNU Gneural Network
#29Love it! If you want to play with state-of-the-art machine learning software, this is not for you. But if you want a clean implementation of neural networks in C that has a GPL license and no non-free components, this is a good start.
It's 1980s hobbyist level code. Almost small enough to be on stackoverflow as a sample. Not that much there to love!
Re: GNU Gneural Network
#30At this stage of things, I think it's more forward looking to open source trained models. Not only they are beginning to be the real core of future building blocks (see, e.g., trained word2vec vectors) but also the contain the real complexity in a NN, i.e., the are the "real function" you would want in a library.