OpenCL / AMD: Deep Learning [closed]

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.

Edit 3 It seems that TensorFlow is now far more widely accepted than theano so I have updated the guide accordingly.

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 tensorflow and utilise what GPU support there is and continue to update to the latest development versions. Use theano - and use existing GPU support similarly to tensorflow 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:

Python - DeepCL

Jonathan's Torch7 Utility Library - C++

In Development

tensorflow is adding OpenCL support once improvements to Eigen and other dependencies are finished

theano is adding support to OpenCL through clBLAS

Caffe is in development stages of adding OpenCL support but a bit behind theano in progress, it seems

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--- 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.

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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:

Torch: https://github.com/torch/torch7/wiki/Cheatsheet#ec2-public-ami

Theano+Caffe: https://www.kaggle.com/forums/f/208/getting-started/t/11505/an-aws-ami-mainly-for-deep-learning

Tip: use spot instances to get a cheaper price (around 10 cents/hour for a g2.2xlarge).

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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.

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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)

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TensorFlow now have OpenCL support on the roadmap.

See: Github issue.

Hopefully not that far away from a working version.

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Please check out https://01.org/intel-deep-learning-framework - The Intel® Deep Learning Framework (IDLF) provides a unified framework for Intel® platforms accelerating Deep Convolutional Neural Networks. It is Open Source, so you could port it to AMD hardware as well. The cool thing: it could run on a MacBook Pro with Intel Iris graphics.

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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:

For deep learning, see https://rocm.github.io/dl.html

For OpenCL, see: https://rocm.github.io/QuickStartOCL.html https://gpuopen.com/opencl-rocm1-6/

https://rocm.github.io/QuickStartOCL.html

https://gpuopen.com/opencl-rocm1-6/

Useful tutorials can be found here: https://rocm.github.io/tutorials.html

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