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上一篇博客介绍了如何使用Theano+logistic regression来实现kaggle上的数字手写识别，文末提到了CPU计算实在太慢，因此在做完这个实验之后，博主查阅了Theano的文档，了解到Theano官方仅支持CUDA进行GPU运算，不支持OpenCL，也就是说Theano官方仅支持N卡。原因是，CUDA和OpenCL是两个GPU计算平台，CUDA仅支持N卡，OpenCL支持所有的显卡，二者的具体区别还请自行查询。无奈博主的笔记本有一张intel的集成显卡和AMD的一张入门独显，而Theano非官方的提供了libgpuarray来支持OpenCL，因此博主花了大量的时间来尝试安装libgpuarray。
libgpuarray支持的OS有Debian6，Ubuntu14.04，MAC OS X10.11和win7，而网上能找到的成功安装libgpuarray的只有两篇博文，全是在MAC OS上，下面给出博文链接，供后面的同学参考：
https://www.robberphex.com/2016/05/521
http://codechina.org/2016/04/how-to-install-theano-on-mac-os-x-ei-caption-with-opencl-support/
博主的最初OS是win7，整个6月的空闲时间几乎都用在安装libgpuarray上了，遇到了无数个坑，然并卵，最终也没能成功。这里列出在win7上安装libgpuarray需要的一些环境，供后面的同学参考：
7月份在win7上装了Ubuntu14.04的双系统，尝试在Ubuntu上实现Theano+OpenCL的GPU运算，最终libgpuarray算是安装成功吧，只是还不能用A卡来计算，具体问题文末介绍。下面介绍整个过程。
我的win7/Ubuntu14.04双系统安装过程参考了http://m.blog.csdn.net/article/details?id=43987599 这篇博文比较简单，这里不再展开。
博主开始是死在了这里，AMD驱动装坏了好几次，装坏了的结果就是重启后不能进入图形界面。然后只能在tty或者initramfs进行修复，这对于博主这种第一次接触linux的人来说太困难了，往往修复好了还是不能用，只好重装系统，整个过程重装了七八次。这里我介绍一种安装驱动的方法，比较简单快速（至少我是一次就成功了）。
在安装好Ubuntu14.04之后，第一件事就是换驱动。找到附加驱动，如下图所示，系统初始使用的驱动是开源的，我们选择来自fglrx的专有驱动，然后点击“应用更改”按钮，静静的等它装完重启。
重启后打开终端，输入fglrxinfo，终端会返回显卡信息，如下所示：
marcovaldo@marcovaldong:~$ fglrxinfo
display: :0 screen: 0
OpenGL vendor string: Advanced Micro Devices, Inc.
OpenGL renderer string: AMD Radeon HD 7400M Series
OpenGL version string: 4.5.13399 Compatibility Profile Context 15.201.1151
再在终端输入fgl_glxgears，会跳出一个测试窗口（旋转的方块），这就证明显卡驱动安装成功。这里，博主找到了安装驱动的比较好的方法，供后面的同学参考。
http://forum.ubuntu.org.cn/viewtopic.php?t=445434
http://www.tuicool.com/articles/6N3e2ir
前往AMD官网下载SDK（注意OS和位数），我这里下载的是Linux64位版AMD APP SDK 3.0。文件解压后出现一个.sh文件，终端输入命令
sudo sh AMD-APP-SDK-v3.0.130.136-GA-linux64.shAMDSDK默认会安装在/opt/下，这时候在终端输入clinfo命令会返回OpenCL平台信息和计算设备信息，下面给出我的笔记本的数据：
marcovaldo@marcovaldong:~$ clinfo
Number of platforms: 1
Platform Profile: FULL_PROFILE
Platform Version: OpenCL 2.0 AMD-APP (1800.11)
Platform Name: AMD Accelerated Parallel Processing
Platform Vendor: Advanced Micro Devices, Inc.
Platform Extensions: cl_khr_icd cl_amd_event_callback cl_amd_offline_devices
Platform Name: AMD Accelerated Parallel Processing
Number of devices: 2
Device Type: CL_DEVICE_TYPE_GPU
Vendor ID: 1002h
Board name: AMD Radeon HD 7400M Series
Device Topology: PCI[ B#1, D#0, F#0 ]
Max compute units: 2
Max work items dimensions: 3
Max work items[0]: 256
Max work items[1]: 256
Max work items[2]: 256
Max work group size: 256
Preferred vector width char: 16
Preferred vector width short: 8
Preferred vector width int: 4
Preferred vector width long: 2
Preferred vector width float: 4
Preferred vector width double: 0
Native vector width char: 16
Native vector width short: 8
Native vector width int: 4
Native vector width long: 2
Native vector width float: 4
Native vector width double: 0
Max clock frequency: 700Mhz
Address bits: 32
Max memory allocation: 134217728
Image support: Yes
Max number of images read arguments: 128
Max number of images write arguments: 8
Max image 2D width: 16384
Max image 2D height: 16384
Max image 3D width: 2048
Max image 3D height: 2048
Max image 3D depth: 2048
Max samplers within kernel: 16
Max size of kernel argument: 1024
Alignment (bits) of base address: 2048
Minimum alignment (bytes) for any datatype: 128
Single precision floating point capability
Denorms: No
Quiet NaNs: Yes
Round to nearest even: Yes
Round to zero: Yes
Round to +ve and infinity: Yes
IEEE754-2008 fused multiply-add: Yes
Cache type: None
Cache line size: 0
Cache size: 0
Global memory size: 536870912
Constant buffer size: 65536
Max number of constant args: 8
Local memory type: Scratchpad
Local memory size: 32768
Max pipe arguments: 0
Max pipe active reservations: 0
Max pipe packet size: 0
Max global variable size: 0
Max global variable preferred total size: 0
Max read/write image args: 0
Max on device events: 0
Queue on device max size: 0
Max on device queues: 0
Queue on device preferred size: 0
SVM capabilities:
Coarse grain buffer: No
Fine grain buffer: No
Fine grain system: No
Atomics: No
Preferred platform atomic alignment: 0
Preferred global atomic alignment: 0
Preferred local atomic alignment: 0
Kernel Preferred work group size multiple: 64
Error correction support: 0
Unified memory for Host and Device: 0
Profiling timer resolution: 1
Device endianess: Little
Available: Yes
Compiler available: Yes
Execution capabilities:
Execute OpenCL kernels: Yes
Execute native function: No
Queue on Host properties:
Out-of-Order: No
Profiling : Yes
Queue on Device properties:
Out-of-Order: No
Profiling : No
Platform ID: 0x7f98e6833430
Name: Caicos
Vendor: Advanced Micro Devices, Inc.
Device OpenCL C version: OpenCL C 1.2
Driver version: 1800.11
Profile: FULL_PROFILE
Version: OpenCL 1.2 AMD-APP (1800.11)
Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_gl_sharing cl_ext_atomic_counters_32 cl_amd_device_attribute_query cl_amd_vec3 cl_amd_printf cl_amd_media_ops cl_amd_media_ops2 cl_amd_popcnt cl_amd_image2d_from_buffer_read_only cl_khr_spir cl_khr_gl_event
Device Type: CL_DEVICE_TYPE_CPU
Vendor ID: 1002h
Board name:
Max compute units: 4
Max work items dimensions: 3
Max work items[0]: 1024
Max work items[1]: 1024
Max work items[2]: 1024
Max work group size: 1024
Preferred vector width char: 16
Preferred vector width short: 8
Preferred vector width int: 4
Preferred vector width long: 2
Preferred vector width float: 8
Preferred vector width double: 4
Native vector width char: 16
Native vector width short: 8
Native vector width int: 4
Native vector width long: 2
Native vector width float: 8
Native vector width double: 4
Max clock frequency: 2299Mhz
Address bits: 64
Max memory allocation: 2147483648
Image support: Yes
Max number of images read arguments: 128
Max number of images write arguments: 64
Max image 2D width: 8192
Max image 2D height: 8192
Max image 3D width: 2048
Max image 3D height: 2048
Max image 3D depth: 2048
Max samplers within kernel: 16
Max size of kernel argument: 4096
Alignment (bits) of base address: 1024
Minimum alignment (bytes) for any datatype: 128
Single precision floating point capability
Denorms: Yes
Quiet NaNs: Yes
Round to nearest even: Yes
Round to zero: Yes
Round to +ve and infinity: Yes
IEEE754-2008 fused multiply-add: Yes
Cache type: Read/Write
Cache line size: 64
Cache size: 32768
Global memory size: 6161788928
Constant buffer size: 65536
Max number of constant args: 8
Local memory type: Global
Local memory size: 32768
Max pipe arguments: 16
Max pipe active reservations: 16
Max pipe packet size: 2147483648
Max global variable size: 1879048192
Max global variable preferred total size: 1879048192
Max read/write image args: 64
Max on device events: 0
Queue on device max size: 0
Max on device queues: 0
Queue on device preferred size: 0
SVM capabilities:
Coarse grain buffer: No
Fine grain buffer: No
Fine grain system: No
Atomics: No
Preferred platform atomic alignment: 0
Preferred global atomic alignment: 0
Preferred local atomic alignment: 0
Kernel Preferred work group size multiple: 1
Error correction support: 0
Unified memory for Host and Device: 1
Profiling timer resolution: 1
Device endianess: Little
Available: Yes
Compiler available: Yes
Execution capabilities:
Execute OpenCL kernels: Yes
Execute native function: Yes
Queue on Host properties:
Out-of-Order: No
Profiling : Yes
Queue on Device properties:
Out-of-Order: No
Profiling : No
Platform ID: 0x7f98e6833430
Name: Intel(R) Core(TM) i3-2350M CPU @ 2.30GHz
Vendor: GenuineIntel
Device OpenCL C version: OpenCL C 1.2
Driver version: 1800.11 (sse2,avx)
Profile: FULL_PROFILE
Version: OpenCL 1.2 AMD-APP (1800.11)
Extensions: cl_khr_fp64 cl_amd_fp64 cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_gl_sharing cl_ext_device_fission cl_amd_device_attribute_query cl_amd_vec3 cl_amd_printf cl_amd_media_ops cl_amd_media_ops2 cl_amd_popcnt cl_khr_spir cl_khr_gl_event
另外还要在/root/.bashrc文件中添加环境变量，具体如下：
# AMD APP SDK
export AMDAPPSDKROOT="/opt/AMDAPPSDK-3.0"
export AMDAPPSDKSAMPLESROOT="/opt/AMDAPPSDK-3.0/""
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"/opt/AMDAPP/lib/x86_64":"/opt/AMDAPP/lib/x86"
export ATISTREAMSDKROOT=$AMDAPPSDKROOT到这里，AMD APP SDK就算是安装好了，下面再给出我参考的几篇博文：
https://www.blackmoreops.com/2013/11/22/install-amd-app-sdk-kali-linux/
http://blog.csdn.net/vblittleboy/article/details/8979288
Ubuntu14.04自带的python版本是2.7.6的，我这里把它升级成了2.7.11的，具体方法是在终端输入下面三条命令：
sudo add-apt-repository ppa:fkrull/deadsnakes-python2.7
sudo apt-get update
sudo apt-get upgrade为了防止安装过程出现错误影响整个python的环境，这里我们使用python的虚拟环境。
sudo apt-get install python-virtualenv
sudo apt-get install python-pip
virtualenv venv
source venv/bin/activate然后我们就进入了python的一个虚拟环境venv，下面的操作全是在venv中进行的。首先安装Theano和libgpuarray的一些依赖包，具体要求看libgpuarray官方文档
pip install numpy
pip install Cython
pip install Scipy安装scipy时可能会报错，可参考下面链接来修复：
http://stackoverflow.com/questions/11114225/installing-scipy-and-numpy-using-pip
然后是安装Theano，注意版本号为0.8.2的稳定Theano跟libgpuarray是不同步的，在使用时会报错，具体文末会提到。这里我安装的是Theano(0.9.0dev)：
pip install git+https://github.com/Theano/Theano.git
# 这里我使用的是robberphex的CSDN镜像，在此表示感谢
# pip install git+https://code.csdn.net/u010096836/theano.git这里还用到了libcheck，因此装上它：
sudo apt-get install check下面开始安装libgpuarray
git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray
mkdir Build
cd Build
cmake . -DCMAKE_INSTALL_PREFIX=../venv/ -DCMAKE_BUILD_TYPE=Release
make install
export LIBRARY_PATH=$LIBRARY_PATH:$PWD/../venv/lib
export CPATH=$CPATH:$PWD/../venv/
python setup.py build
python setup.py install下面开始测试一下，Theano官方给出了一段测试程序，我们命名为test.py，程序如下：
from theano import function, config, shared, tensor, sandbox
import numpy
import time
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
iters = 1000
rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], tensor.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, tensor.Elemwise) and
(‘Gpu‘ not in type(x.op).__name__)
for x in f.maker.fgraph.toposort()]):
print(‘Used the cpu‘)
else:
print(‘Used the gpu‘)先是仅用Theano和CPU，结果如下：
(venv)marcovaldo@marcovaldong:~/desktop$ python test.py
[Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
Looping 1000 times took 7.7898850441 seconds
Result is [ 1.23178032 1.61879341 1.52278065 ..., 2.20771815 2.29967753
1.62323285]
Used the cpu再是加了THEANO_FLAGS=mode=FAST_RUN的：
(venv)marcovaldo@marcovaldong:~/desktop$ THEANO_FLAGS=mode=FAST_RUN,floatX=float32 python test.py
[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]
Looping 1000 times took 3.86811089516 seconds
Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761
1.62323284]
Used the cpu
(venv)marcovaldo@marcovaldong:~/desktop$ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python test.py
[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]
Looping 1000 times took 3.84727883339 seconds
Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761
1.62323284]
Used the cpu下面使用OpenCL的时候就报错，网上没有找到有效的解决方法，希望有遇到过的大神给指点迷津，具体如下：
(venv)marcovaldo@marcovaldong:~/desktop$ THEANO_FLAGS=mode=FAST_RUN,device=opencl0:0,floatX=float32 python test.py
ERROR (theano.sandbox.gpuarray): Could not initialize pygpu, support disabled
Traceback (most recent call last):
File "/home/marcovaldo/myvenv/venv/local/lib/python2.7/site-packages/theano/sandbox/gpuarray/__init__.py", line 96, in <module>
init_dev(config.device)
File "/home/marcovaldo/myvenv/venv/local/lib/python2.7/site-packages/theano/sandbox/gpuarray/__init__.py", line 47, in init_dev
"Make sure Theano and libgpuarray/pygpu "
RuntimeError: (‘Wrong major API version for gpuarray:‘, -9997, ‘Make sure Theano and libgpuarray/pygpu are in sync.‘)
[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]
Looping 1000 times took 3.86138486862 seconds
Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761
1.62323284]
Used the cpu到这里，如果你没有下面的这个问题，你的libgpuarray应该就算装好了。
RuntimeError: (‘Wrong major API version for gpuarray:‘, -9997, ‘Make sure Theano and libgpuarray/pygpu are in sync.‘)
RuntimeError: (‘Wrong major API version for gpuarray:‘, -9998, ‘Make sure Theano and libgpuarray/pygpu are in sync.‘)接下来我会抽时间翻译一下libgpuarray的官方安装文档，供后来的同学参考。
现在的深度计算工具都是官方支持N卡，A卡在这方面实在太吃亏了，希望各个深度学习工具能尽快做出支持A卡的API。
最后鸣谢robberphex和Tinyfool，二位的博客我提供了思路。
Ubuntu14.04+Theano+OpenCL+libgpuarray实现GPU运算
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原文地址：http://blog.csdn.net/majordong100/article/details/51859994
