OpenCL/AMD:深度学习 [英] OpenCL / AMD: Deep Learning

问题描述

在"googl'ing"并进行一些研究时，我找不到任何严肃/流行的框架/SDK，用于科学的GPGPU计算和 OpenCL > AMD 硬件.我想念任何文学和/或软件吗?

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?

特别是我对深度学习感兴趣.

据我所知， deeplearning.net 建议使用 NVIDIA 硬件和 CUDA 框架.此外，我所知道的所有大型深度学习框架，例如 Caffe ， Theano ， Torch ， DL4J ，...专注于 CUDA ，并且不打算支持 OpenCL/AMD .

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.

此外，对于基于 CUDA 的深度学习任务，可以找到大量的科学论文以及相应的文献，但是对于基于 OpenCL/AMD 的解决方案，它几乎找不到.

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.

在2015/16年度，基于OpenCL/AMD 的解决方案是否有可能出现新的或现有的科学框架?

Is there any chance that new or existing scientific frameworks will show up for OpenCL/AMD based solutions in 2015/16?

使用 OpenCL/AMD 进行深度学习的良好开端是什么?有文学吗?教程?其他来源吗?

What is a good start for deep learning with OpenCL/AMD? Any literature? Tutorials? Miscellaneous sources?

推荐答案

编辑1 参见 Mikael Rousson的答案-亚马逊现在是前进的方向，因为您可以从中租用"它们的计算能力.

Edit 1 See Mikael Rousson's answer - Amazon is now the way forwards as you can "rent" computational power from them.

编辑2 我创建了一个

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.

编辑3 似乎TensorFlow现在比theano更加广泛地被接受，因此我对指南进行了相应的更新.

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

我所处的处境与我一样，因为我有一台配备Intel Iris显卡的MacBook Pro.我花了一周的大部分时间来仔细研究所有可能的解决方法，并且非常欢迎我提供的替代方法.

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.

我目前拥有的最佳解决方案是:

The best solution I currently have is to:

1. 安装python库 tensorflow ，并利用现有的GPU支持并继续更新到最新的开发版本.
2. 使用 theano -并使用 pyOpenCl .

1. Install the python library tensorflow and utilise what GPU support there is and continue to update to the latest development versions.
2. Use theano - and use existing GPU support similarly to tensorflow
3. Buy an NVIDIA graphics card and use it on a PC
4. 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.

我发现任何使用OpenCL的解决方案，例如 pyOpenCl ，还没有用于深度学习的用户友好界面，即，编码将花费更长的时间除了快速编码并在CPU上运行外，它还可以采用其他方法.话虽这么说，但这里是深度学习的最佳替代OpenCL库:

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
• 乔纳森的Torch7实用程序库-C ++

• Python - DeepCL
• Jonathan's Torch7 Utility Library - C++

开发中

• tensorflow 是完成了
• theano 是 rel =" noreferrer> clBLAS
• Caffe 在

• 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

这篇关于OpenCL/AMD:深度学习的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！