Ubuntu16.04+Theano环境

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安装Anaconda：

官网下载Anaconda
切换到下载目录
```
cd ~/下载/
```
用bash运行下载好的.sh文件
```
bash Anaconda2-4.3.0-Linux-x86_64.sh
```

进入欢迎界面

Welcome to Anaconda2 4.3.0 (by Continuum Analytics, Inc.)In order to continue the installation process, please review the licenseagreement.Please, press ENTER to continue
>>>

按回车

================Anaconda License================Copyright 2016, Continuum Analytics, Inc.All rights reserved under the 3-clause BSD License:Redistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditions are met:* Redistributions of source code must retain the above copyright notice,this list of conditions and the following disclaimer.* Redistributions in binary form must reproduce the above copyright notice,this list of conditions and the following disclaimer in the documentationand/or other materials provided with the distribution.* Neither the name of Continuum Analytics, Inc. nor the names of itscontributors may be used to endorse or promote products derived from thissoftware without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THEIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSEARE DISCLAIMED. IN NO EVENT SHALL CONTINUUM ANALYTICS, INC. BE LIABLE FORANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENT--更多--

可以按q退出

显示是否同意条款，输入yes

Do you approve the license terms? [yes|no]>>> yes

跳出是否使用默认安装路径，直接回车（如果要改直接输入想要的安装路径）

Anaconda2 will now be installed into this location:/home/ziven/anaconda2  - Press ENTER to confirm the location  - Press CTRL-C to abort the installation  - Or specify a different location below[/home/ziven/anaconda2] >>>

等待安装

安装完成，选择是否配置环境变量【注意：默认是no】，因此这里要输入yes，否则之后要手动添加环境变量

Python 2.7.13 :: Continuum Analytics, Inc.creating default environment...installation finished.Do you wish the installer to prepend the Anaconda2 install locationto PATH in your /home/ziven/.bashrc ? [yes|no][no] >>> yes

Anaconda安装完成

Prepending PATH=/home/ziven/anaconda2/bin to PATH in /home/ziven/.bashrcA backup will be made to: /home/ziven/.bashrc-anaconda2.bakFor this change to become active, you have to open a new terminal.Thank you for installing Anaconda2!Share your notebooks and packages on Anaconda Cloud!Sign up for free: https://anaconda.org

输入

anacron -V

可显示版本

Anacron 2.3Copyright (C) 1998  Itai Tzur <itzur@actcom.co.il>Copyright (C) 1999  Sean 'Shaleh' Perry <shaleh@debian.org>Copyright (C) 2004  Pascal Hakim <pasc@redellipse.net>Mail comments, suggestions and bug reports to <pasc@redellipse.net>.

安装CUDA：

确保GPU为CUDA所支持的GPU
```
lspci | grep -i nvidia
```
参照GPU支持列表
确定系统版本
```
uname -m && cat /etc/*release
```
确定gcc版本
```
gcc --version
```
选择显卡驱动
下载CUDA Toolkit,建议使用.deb

切换到下载目录

sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

更新apt源
```
sudo apt-get updatesudo apt-get upgrade
```
安装cuda
```
sudo apt-get install cuda
```
再次更新apt源
```
sudo apt-get updatesudo apt-get upgrade
```
更新软件包
```
sudo apt-get cuda
```
选择最新安装的显卡驱动
如果没有新的显卡驱动可以如下安装
```
sudo apt-get install cuda-drivers
```

添加环境变量

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}

检测安装

1 cd /usr/local/cuda-8.0/samples/2 sudo make

使用deviceQuery检测安装

1 cd ./bin/x86_64/linux/release/2 ./deviceQuery

可以看到显卡信息和最后的PASS即可

./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking)Detected 1 CUDA Capable device(s)Device 0: "GeForce 940MX"  CUDA Driver Version / Runtime Version          8.0 / 8.0  CUDA Capability Major/Minor version number:    5.0  Total amount of global memory:                 2002 MBytes (2099642368 bytes)  ( 3) Multiprocessors, (128) CUDA Cores/MP:     384 CUDA Cores  GPU Max Clock rate:                            1242 MHz (1.24 GHz)  Memory Clock rate:                             1001 Mhz  Memory Bus Width:                              64-bit  L2 Cache Size:                                 1048576 bytes  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers  Total amount of constant memory:               65536 bytes  Total amount of shared memory per block:       49152 bytes  Total number of registers available per block: 65536  Warp size:                                     32  Maximum number of threads per multiprocessor:  2048  Maximum number of threads per block:           1024  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)  Maximum memory pitch:                          2147483647 bytes  Texture alignment:                             512 bytes  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)  Run time limit on kernels:                     Yes  Integrated GPU sharing Host Memory:            No  Support host page-locked memory mapping:       Yes  Alignment requirement for Surfaces:            Yes  Device has ECC support:                        Disabled  Device supports Unified Addressing (UVA):      Yes  Device PCI Domain ID / Bus ID / location ID:   0 / 2 / 0  Compute Mode:     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce 940MXResult = PASS

SElinux报错的话需要
```
sudo setenforce 0
```
然后跑一下bandwidthTest看一下
```
./bandwidthTest
```
显示PASS即可
CUDA安装完成

安装cuDNN:

下载cuDNN

进入下载目录解压tar包

1 cd ~/下载/2 tar -zxf cudnn-8.0-linux-x64-v5.1.tgz cuda/

进入cuda文件夹
```
cd ~/cuda
```

复制头文件到/usr/local/include

sudo cp include/cudnn.h /usr/local/include/

复制lib文件到/usr/local/lin
```
sudo cp lib64/* /usr/local/lib
```
编辑.bashrc添加环境变量
```
vim ~/.bashrc
```
在最后一行添加
```
export LD_LIBRARY_PATH=/usr/local/lib
```

安装theano：

conda install theanopip install nose_parameterized

进入Python检查tehano：
```
import theanotheano.test（）
```

如果报错

Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.

则执行

conda install nomkl

结果为ok则安装成功

配置.theanorc（配置GPU加速）：
```
cd ~vim .theanorc
```

写入并保存：

[global]  floatX=float32  device=gpu  base_compiledir=~/external/.theano/  allow_gc=False  warn_float64=warn  [mode]=FAST_RUN    [nvcc]  fastmath=True    [cuda]  root=/usr/local/cuda

创建一个test.py：

from theano import function, config, shared, sandbox  import theano.tensor as T  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([], T.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, T.Elemwise) for x in f.maker.fgraph.toposort()]):      print('Used the cpu')  else:      print('Used the gpu')

如果最后一行显示Used the gpu则表示GPU已启用

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