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问题:
While "googl'ing" and doing some research I were not able to find any serious/popular framework/sdk for scientific GPGPU-Computing and OpenCL on AMD hardware. Is there any literature and/or software I missed?
Especially I am interested in deep learning.
For all I know deeplearning.net recommends NVIDIA hardware and CUDA frameworks. Additionally all big deep learning frameworks I know, such as Caffe, Theano, Torch, DL4J, ... are focussed on CUDA and do not plan to support OpenCL/AMD.
Furthermore one can find plenty of scientific papers as well as corresponding literature for CUDA based deep learning tasks but nearly nothing for OpenCL/AMD based solutions.
Is there any chance that new or existing scientific frameworks will show up for OpenCL/AMD based solutions in 2015/16?
What is a good start for deep learning with OpenCL/AMD? Any literature? Tutorials? Miscellaneous sources?
回答1:
Edit 1 See Mikael Rousson's answer - Amazon is now the way forwards as you can "rent" computational power from them.
Edit 2 I've created a series of guides on how to set up Amazon EC2 Instances for Deep Learning with theano. It's a lot more convenient than running on a personal machine.
I have been in the same situation as yourself as I have a MacBook Pro with Intel Iris graphics. I have spent the best part of a week looking through all possible workarounds and I would be more than welcome to alternatives to those that I offer.
The best solution I currently have is to:
- Install the
python library, theano and utilise what GPU support there is and continue to update to the latest development versions. - Buy an NVIDIA graphics card and use it on a PC
- If you absolutely need a solution in OpenCL and you are willing to code everything from a high level of understanding (no tutorials) look at DeepCL and possibly pyOpenCl.
I have found that any solution using OpenCL, e.g. pyOpenCl, doesn't yet have user friendly interfaces for Deep Learning i.e. it will take longer to code it in an alternative method than to just code it fast and run on a CPU. With that said though, here are of the best alternative OpenCL libraries for deep learning:
In Development
回答2:
--- Aug 2017 Update cool new things happened in the AMD side ---
now it is actually possible to run any library on most AMD hardware Check Here
As of 25 October 2015
it seems that AMD and others have extended their hands on the development of several OpenCL accelerated frameworks for deeplearning. So yeah OpenCL support is now existent for deeplearning :)
This is a list of OpenCL accelarated framework or tools that have been developed keeping deep learning in mind primarily. I hope they will get updated over the upcoming years
We know right now(25 October 2015) there are three deep learning framework that are very very popular to researchers and has seen some commercial products
Theano
Caffe
Torch
caffe has a pretty good OpenCL support because amd developed a complete version of caffe which supports almost all the features of caffe and also it is being developed actively. it is named OpenCL Caffe. and here is the repository
OpenCL Caffe
if you are thinking about performance then according to that site(i have not bench-marked it myself) it gives around 261 images per second or 22.5 million images per day in a AMD R9 Fury hardware(training). to compare with nvidia K40, which can process 40 million images a day. so according to the site it can give half performance in one-sixth money.(considering k40 is 3000$ card and r9 fury is around 600$). however using any consumer card will give you a problem about memory(vram) which is quite important in deep learning.
Torch in recent days also seem to have decent OpenCL support. However it is maintained by a single person. It claims to have full support for all the features of torch. However it does not give an idea about performance. Here is the repository. it is actively maintained.
cltorch
Currently there does not seems to be a decent opencl backend for theano framework but work is in progress. and simple programs can be done with the current version.
There are some other opencl frameworks for deeplearning too. It will take some time to sort them out to see if they work properly or not.
回答3:
An alternative is to use GPU instances on Amazon Web Services. You can find AMIs with commonly used deep learning packages already installed. For example:
Tip: use spot instances to get a cheaper price (around 10 cents/hour for a g2.2xlarge).
回答4:
PlaidML (https://github.com/plaidml/plaidml) is a fully open source deep learning runtime that runs on top of OpenCL and integrates with Keras to provide a familiar user-facing API. The README in the repo has more detailed status, currently convnet inference on Linux is well supported but we (http://vertex.ai) are working to expand completeness and platform support as quickly as we can. Our continuous integration machines include an assortment of AMD and NVIDIA GPUs, all Linux for now but we are also working on adding Mac and Windows.
回答5:
I'm writing opencl 1.2 support for Tensorflow. https://github.com/hughperkins/tensorflow-cl Currently supports:
- blas matrix multiplication
- gradients
- eigen operations such as: reductions, argmin/argmax, per-element operations (binary and unary)
回答6:
TensorFlow now have OpenCL support on the roadmap.
See: Github issue.
Hopefully not that far away from a working version.
回答7:
回答8:
Check out the ROCm platform, which is driven by AMD. This is the first open-source HPC/Hyperscale-class platform for GPU computing that’s also programming-language independent.
Specifically:
