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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.




FANN is GNU licensed (LGPL 2.1), doesn't rely on non-free software, is written in C,so it's the same as Gneural in those regards. But it also is way more mature, has more features, compiles and runs on Linux, Windows and MacOS,and has bindings to 28 other languages.


Gneural will compete with Theano sort of like how the GNU Hurd competes with Linux


I know you're just trying to be funny, but I don't think it's funny at all.

The Linux kernel undoubtedly many features that the Hurd system lacks, but that is due to the severe lack of manpower of the latter system and the billions of Dollars being poured into the former.

On the other hand the Hurd has features that the Linux kernel can never hope to achieve because of its architecture.


> The Linux kernel undoubtedly many features that the Hurd system lacks, but that is due to the severe lack of manpower of the latter system and the billions of Dollars being poured into the former.

That's why GNU Hurd is essentially a dead project. Sadly it never attracted the attention and manpower necessary for it to survive.

> On the other hand the Hurd has features that the Linux kernel can never hope to achieve because of its architecture.

For example?


Fault isolation. We're doing it for daemons, we're doing it for web browsers, it is insane we're not doing it for operating system services. I bought a graphic tablet and the first time I plugged it into my laptop the Linux kernel crashed. And this was merely a faulty driver, not even malicious hardware.

Also think of the effort it took to introduce namespaces to all the Linux subsystems. After a decade the user namespace still has problems. This is ridiculously easy on a distributed system, yet very hard on a monolithic one.


I am not trying to be funny. I am dead serious. Aeolos explained it perfectly.


In other words, not at all


That's not necessarily relevant though. I'm sure the FSF would love to see Free Software replace all proprietary software, but in the end, the real point is that Free Software options are available to the people who want them. This isn't like a battle between commercial entities where market share is king and a project will be dropped if it isn't profitable. Gneural will be a success if a community forms around it and people work on it and use it, however small that community might be.


The problem I have with it is that they could be contributing their brain power and time to other open source projects instead of recreating the wheel for very little benefit. Take my opinion with a grain of salt, as I consider the more restrictive Copyleft licensing as new /loss/ for society.


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 these packages.


Caffe has an OpenCL backend - https://github.com/BVLC/caffe/tree/opencl


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 and imagination on this one.


It seems like there must be more at play, but I'll admit a lack of insight and imagination on this one.

I think the reasons are twofold: 1. CUDA had a big headstart over OpenCL. 2. NVIDIA has invested a lot in great libraries for scientific computing. E.g. for neural nets, they have made a library of primitives on top of CUDA for neural nets (cuDNN), which has been adopted by all the major packages.


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...


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] https://developer.nvidia.com/cuda-toolkit


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.


Because it requires nVidias cooperation in implementing OpenCL. And of course they are not about to do so in a useful manner when they are leading with CUDA.

Also, the premise of OpenCL is somewhat faulty. You end up optimizing for particular architectures regardless.


and relies on non-free components (CUDA).

Yeah, this is one reason I'm really hoping some of the stuff AMD is pushing, in regards to openness around GPUs, gains traction. And why I am hoping OpenCL continues to improve so that it can be a viable option. Being dependent on nVidia for all time would blow.


The use of the gplv3 allows gnueral to have, as a dependency, any of the apache or permissive licensed tools like TF, torch, etc, and then through those tools 'export' their dependence on non-free components from nvidia and others.

I don't think this is wrong, per se, but it is ...funny when the fsf portrays their work as morally superior to us horrible corporate permissive license lovers, while inexorably depending on non-free components.

In an ideal world this project will be popular and will lead to someone on gnueral writing nvidia compatible drivers that will allow them to reject nvidia's, but I'm not optimistic. Not because of some incompetency on the Gnueral team, but nvidia's long history of making life very difficult for open driver writers.


Does the FSF really depend on non-free components?


It's possible to run any of the "major" neural network toolkits (Caffe, Torch, Theano) on CPU-only systems. All of them are permissively licensed (to my knowledge).

It will be prohibitively difficult to train the model without some kind of hardware assistance (CUDA). This means that if we're building an ImageNet object detector, even if the code implements the model correctly the first time, training it to have close-to-state-of-the-art accuracy will take several consecutive months of CPU time. Torch has rudimentary support for OpenCL, but it isn't there yet. There are very good pre-trained models that are licensed under academic-only licenses that also help fill the gap. (This is about as permissively as it could be licensed because the ImageNet training data itself is under an academic-only license anyway.)

I'm not sure what niche this project fills. If you want an open-source neural network, you have several high-quality choices. If you need good models, you can either use any of the state-of-the-art academic only ones, or you would have to collect some dataset completely by yourself.


> This is about as permissively as it could be licensed because the ImageNet training data itself is under an academic-only license anyway.

Does this necessarily follow, that a machine-learning model is a derived work of all data it's trained on? As far as I know, the law in this area isn't really settled. And many companies are operating on the assumption that this isn't the case. It would lead to some absurd conclusions in some cases, for example if you trained a model to recognize company logos, you'd need permission of the logos' owners to distribute it.

(This is assuming traditional copyright law; under jurisdictions like the E.U. that recognize a separate "database right" it's another story.)


I'm not aware of the formal legality of it, but I don't see why it wouldn't be the case. Without the training data, the model can't work. That seems to fit the definition of "derivative work".


IANAL, but I looked at the definition of derivative work, and it seems really hard to apply to learning algorithms. But I'm going to disagree with you. I notice that US law mentions "preexisting material employed in the work". IMO a set of neural network weights contains no preexisting material at all. All the examples of derivative works include at parts of previously copyrighted works directly.

I'd like to note that some publishers, like Elsevier, allow you access to their dataset (full texts of articles) under a license with the condition that you can not freely distribute models learnt from their data.


Wrong, most do support OpenCL or at least have partial support. It's just much less supported because not so much people see too much benefit from it. Btw, it's all open source. If you miss some functionality, it's really easy to add.


Yeah, you don't depend on CUDA/cuDNN, but of course you can use them if you want it to be fast

But the CPU fallback is there


Its going to need to use CUDA or it will not be competitive with alternatives. CUDA makes training networks more than an order of magnitude faster.


But that may or may not matter, depending on what you're doing. And how often you do it. If I have a network that I only retrain once a month, I can deal with it taking a day or two to train. Heck, it could take a week as far as that goes.

OTOH, it obviously matters a lot if you're constantly iterating and training multiple times a day or whatever.


The difference is between training taking a week, and training taking 10 weeks.

It takes a week to train a standard AlexNet model on 1 GPU on ImageNet (and this is pretty far from state of the art).

It takes 4 GPUs 2 weeks to train a marginally-below state of the art image classifier on ImageNet (http://torch.ch/blog/2016/02/04/resnets.html) - the 101 layer deep residual network. This would be 20 weeks on an ensemble of CPUs. (State of the art is 152 layers; I don't have the numbers but I'd guess-timate 3-4 weeks to train on 4 GPUs).


For state of the art work "a day or two" is pretty fast for a production network, and that's on one or more big GPUs. Not using CUDA is definitely a dealbreaker for any kind of real deep learning beyond the mnist tutorials. It's common to leave a Titan X to run over a weekend; that would be weeks on a CPU.


Well not using CUDA isn't necessarily synonymous with "use a CPU". There is OpenCL. But still, you have a point even if we might quibble over details. This is why I am very much hoping AMD gets serious about Machine Learning and hoping for OpenCL on AMD chips will eventually reach a level of parity (or near parity) with the CUDA on nVidia stuff.


Its unlikely that AMD is going to be able to make serious inroads in the near future. nVidia has built quite a lead not just in terms of chips but tooling. I had thought a couple of years ago that AMD should be building a competitor to the Tesla. It should be able to build a more hybrid solution than nVidia can given its in house CPU development talent. But I haven't seen them building anything like that and a competitor to nVidia may have to come from somewhere else. In the absence of a serious competitor OpenCL is not very interesting.


Yeah, and that's sad. I really hate to see this whole monoculture thing, especially since CUDA isn't OSS. :-(


Its really a hardware problem.


Why not focus on adding GPLed code to an existing package with a GPL-friendly license?


FSF want to be copyright holder for all of it projects code so it's possible to relicense codebase under newer version of GPL. For same reason everyone contribute to their projects must sign CLA.




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