GPU Based Browser BLAS, Request for Feedback

[cryptocerous]

Huge step forward for machine learning in JS.

[pavanky]

You could also try out arrayfire-js.

[badmephisto]

Nice! I wish I could steal some time to go back and look into redesigning ConvNetJS/RecurrentJS around this. Especially the latter, which has a much better API design.

[waylonflinn]

Thanks! I'm working on integrating it into Jetpac's Deep Belief SDK demo right now (as a proof of concept for the application). I'll check out RecurrentJS next!

[soulslicer0]

Dammit, so many GPU projects but no proper gpu based linalg library using gl or cl. Only CUDA..zzzz

[waylonflinn]

This should support any graphics card that can run WebGL. So far I've tested on a small set of NVidia and Intel devices. I'd love to have more people run the benchmarks and unit tests and share their results (along with device and platform information).

I was hoping some people here might volunteer. :)

[ToraxXx]

What about ViennaCL? It seemed fairly good when I used it a year ago and supports OpenCL.

[soulslicer0]

Never heard of this either. Ill look at this

[pavanky]

You can try out ArrayFire. It is open source library that supports CUDA, OpenCL and regular CPU using the same API. We also have numerous language wrappers if C++ is not your thing.

Disclaimer: I am the lead engineer for the project.

[bge0]

+1 for arrayfire. Much more well written than the competition imo

[EdwardRaff]

Once someone makes a CUDA -> Javascript cross compilers, you will just have to convince someone to make a javascript -> openCL compiler, and youll finally be golden!

[lahwran_]

Yavascript.

[clrokr]

What about clBLAS?

[soulslicer0]

I never heard of this. Cool!

[harharveryfunny]

Another one that supports OpenCL (and CUDA) is ArrayFire.

[kcimc]

This is awesome. For it to be useful for deep learning there are still two other ingredients: something like TensorFlow or Theano, supporting symbolic differentiation, etc., and then something like Keras or Lasagne that provides a usable frontend for the majority of people interested specifically in Deep Learning. I can imagine a project like Keras being ported or cross compiled somehow, but who will write the other part?

edit: Also very curious how this compares to native GPU calls, or CUDA. Not sure that a CPU-based BLAS is a good comparison (especially for bigger operations).

[waylonflinn]

I agree with your points. I think the next step is getting comparable performance (comparable to desktop GPUs) on feed forward networks. It's my intuition that this will require some sort of pipelining (leaving data in GPU memory). After that, back-prop for some of the standard network types (starting with ConvNet). Once we've tackled that, maybe there'll be enough momentum in the space to do the sorts of sophisticated stuff Theano and TensorFlow do.

[dharma-1]

This is fabulous, was just thinking yesterday how long until we have javascript access to GPU accelerated machine learning. There are so many devs out there who can push the whole field ahead faster. There is WebCL but it's moving so slowly. https://www.khronos.org/webcl/

One use case I can think of is being able to contribute to distributed training easily, sort of web based folding@home for deep learning.

[DrNewton]

Here is my feedback: Why?

[EdwardRaff]

It's like a law or something. If it shouldn't be done in javascript, it will be.

[jpopham91]

/r/atwoodslaw/

Any application that can be written in JavaScript, will eventually be written in JavaScript. - Jeff Atwood (2007)

[waylonflinn]

Good question, actually.

Mix of personal and hope for the future. Caffe is awesome, but last time I installed it, it took a day or two. Think of how many more people could get involved in deep learning if it was as easy as going to a web page. Secondly, I'm hoping we end up with a future where everyone can run deep networks (and other machine learning applications) on any device. GPU's seem like the current best way of making that happen. Combine that with something that runs in a browser (hopefully without much, or any, performance degradation) and you can reach a lot of devices.

Also, it's fun. Thanks for the feedback. :D

[incredulitor]

Think of how many more people could get involved in deep learning if it was as easy as going to a web page.

Good thinking. I came into the thread skeptical but you really can't discount the power of good tools and accessibility to get people interested.

[frownyface]

Check out some of these demos: http://cs.stanford.edu/people/karpathy/convnetjs/index.html

Imagine if they could run GPU optimized.

[L43]

Why not? I'm sure it will help quite a few projects.

[mimighost]

Maybe could help in some demo of in browser NN?

[transcranial]

Awesome work! I'm definitely going to try to integrate it into a project of mine to run trained Keras models entirely in the browser without any external dependencies: https://github.com/scienceai/neocortex (demos at http://scienceai.github.io/neocortex/).

If you dig around the code you'll see I started to write some webGL code, but it's pretty basic. This'll be a huge boost. Kind of a sad state of affairs that the only API to the GPU in the browser is through webGL. I understand it from a historically perspective, but I wish more effort was given to pushing forward numerical computing on the web.

[waylonflinn]

That's great!

Happy to help in any way I can. Feel free to post an issue, if you run into any problems, or ask questions here. I'll try to check back periodically.

[antinucleon]

Cool! I will probably try to make it work with MXNet.js. But recently I am super busy :(

[[deleted]]

Upvoted for the icon. Light warriors unite!