【深度学习】安装TensorFlow-GPU

1、Windows版

准备

干净的系统，没有安装过Python，有的话就卸载了。

另外我的系统安装了VS2015 VS2017（这里我不知道是不是必备的）。

现在TensorFlow和cuda以及cuDNN品名升级，所以这里采用了几乎是最新版的了（2018年11月19日）

• Anaconda——清华tuna下载
• 显卡驱动——点我去英伟达官网自行下载对应驱动
• cuda9.0安装包——点我去百度云下载
• cuDNN7.x安装包——点我去百度云下载

安装

1、安装Anaconda

这里省略。注意一点，安装的选项加入path，都勾选。

2、安装显卡驱动

默认安装。

3、安装cuda9.0

默认安装。

4、安装cuDNN 7.x

将压缩包解压，放在C:ProgramDataNVIDIA GPU Computing Toolkitv9.0这个目录下。

然后将目录C:ProgramDataNVIDIA GPU Computing Toolkitv9.0in添加到环境变量PATH里。

验证

1、启动Anaconda Prompt

输入

1 conda env list

显示只有一个base或者root的环境。表示只有一个环境。

2、修改Anaconda的软件源

执行

1 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
2 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
3 conda config --set show_channel_urls yes

表示将anaconda的软件下载源修改成清华Tuna的了。

3、创建用于TensorFlow的Python环境

conda create -n tf-gpu-py3.5 python=3.5

例子:

D:Userszyb>conda create -n tf-gpu-py3.5 python=3.5
Solving environment: done

## Package Plan ##

  environment location: C:anaconda35envs	f-gpu-py3.5

  added / updated specs:
    - python=3.5


The following NEW packages will be INSTALLED:

    certifi:        2018.8.24-py35_1001 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    pip:            18.0-py35_1001      https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    python:         3.5.5-he025d50_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    setuptools:     40.4.3-py35_0       https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    vc:             14.1-h21ff451_1     https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/peterjc123
    vs2017_runtime: 15.4.27004.2010-1   https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/peterjc123
    wheel:          0.32.0-py35_1000    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
    wincertstore:   0.2-py35_1002       https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

Proceed ([y]/n)? y
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate tf-gpu-py3.5
#
# To deactivate an active environment, use
#
#     $ conda deactivate

4、激活刚刚创建的环境

conda activate tf-gpu-py3.5

5、安装TensorFlow GPU版

conda install tensorflow-gpu

6、代码验证

启动python

输入如下代码

import tensorflow as tf

查看是否报错。

如果报错，就使用conda install 包名（比如numpy）

如果不报错，接着执行

 1 a = tf.constant([1.0,2.0,3.0,4.0,5.0,6.0],shape=[2,3],name='a')
 2 b = tf.constant([1.0,2.0,3.0,4.0,5.0,6.0],shape=[3,2],name='b')
 3 c = tf.matmul(a,b)
 4 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 5 #这步结束之后，会出现一个警告：
 6 #Device mapping: no known devices.
 7 #2018-11-19 22:18:15.899459: I T:srcgithub	ensorflow	ensorflowcorecommon_runtimedirect_session.cc:288] Device mapping:
 8 #不用管，执行下一步
 9 print(sess.run(c))
10 #输出如下：
11 MatMul: (MatMul): /job:localhost/replica:0/task:0/device:CPU:0
12 2018-11-19 22:18:23.059234: I T:srcgithub	ensorflow	ensorflowcorecommon_runtimeplacer.cc:935] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:CPU:0
13 a: (Const): /job:localhost/replica:0/task:0/device:CPU:0
14 2018-11-19 22:18:23.064109: I T:srcgithub	ensorflow	ensorflowcorecommon_runtimeplacer.cc:935] a: (Const)/job:localhost/replica:0/task:0/device:CPU:0
15 b: (Const): /job:localhost/replica:0/task:0/device:CPU:0
16 2018-11-19 22:18:23.069134: I T:srcgithub	ensorflow	ensorflowcorecommon_runtimeplacer.cc:935] b: (Const)/job:localhost/replica:0/task:0/device:CPU:0
17 [[22. 28.]
18  [49. 64.]]

验证成功。

2、Ubuntu下安装GPU版TensorFlow

准备

1、Anaconda-Linux版本的——去清华tuna自行下载

2、显卡驱动——去官网自行下载

点我去百度云下载3、4需要的文件

3、cuda9.0——去官网自行下载Linux版本的

4、cuDNN7.x——去官网下载Linux版本的（需要注册并且join）

安装

1、Anaconda安装

这里需要注意，直接把软件安装在自己的家目录下即可。

2、安装显卡驱动

官网下载驱动，然后使用sudo安装。

安装的过程中，第一步需要你阅读安装协议。使用q退出。

3、安装cuda9.0

默认安装。

安装的过程中，第一步需要你阅读安装协议。使用q退出。

9.0有一个base安装包还有4个升级包。都是有序号的。

使用sudo chmod +x *.run给这5个文件加上可执行权限

然后一个个安装。

然后将安装完后的路径加入PATH环境变量。

1 export PATH=/usr/local/cuda-9.0/bin:/usr/local/cuda-9.0/lib64:$PATH
2 export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH

4、安装cuDNN

解压出来两个文件夹一个是include 一个是lib64

a.使用sudo将include里的cudnn.h文件复制到/usr/local/cuda-9.0/include  目录下

b.使用sudo将lib64里面的libcudnn.so.7.3.1 libcudnn_static.a两个文件复制到/usr/local/cuda-9.0/lib64 目录下。

c.做两个软连接，cd到/usr/local/cuda-9.0/lib64 目录下，执行：

1 sudo ln -s libcudnn.so.7.3.1 libcudnn.so
2 sudo ln -s libcudnn.so.7.3.1 libcudnn.so.7

验证

0、cuda验证

#进入样本目录
cd ~/home/NVIDIA_CUDA-9.0_Samples
#编译样本
make -j8
#进入生成可执行文件的目录
cd bin/x86_64/linux/release
#执行设备测试程序
./deviceQuery
#输出如下
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1070"
  CUDA Driver Version / Runtime Version          10.0 / 9.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 8116 MBytes (8510701568 bytes)
  (15) Multiprocessors, (128) CUDA Cores/MP:     1920 CUDA Cores
  GPU Max Clock rate:                            1683 MHz (1.68 GHz)
  Memory Clock rate:                             4004 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 2097152 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 2 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
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS
#看到PASS后执行带宽测试
./bandwidthTest 
#输出如下：
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce GTX 1070
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432            12758.2

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432            12867.2

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432            191582.5

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
#看到PASS表示测试通过，如果FAIL，重启然后重新执行即可。

1、创建anaconda环境（和Windows一样）

1 conda create -n tf-gpu-py3.5 python=3.5
2 #
3 # To activate this environment, use
4 #
5 #     $ conda activate tf-gpu-py3.5
6 #
7 # To deactivate an active environment, use
8 #
9 #     $ conda deactivate

2、激活tf-gpu-py3.5

conda activate ty-py-3.5-cpu

3、安装tensorflow-gpu

conda install tensorflow-gpu

4、代码验证

(tf-gpu-py3.5) tf@lolita-ThinkStation-P318:~/anaconda3/envs$ python
Python 3.5.6 |Anaconda, Inc.| (default, Aug 26 2018, 21:41:56) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> a = tf.constant([1.0,2.0,3.0,4.0,5.0,6.0],shape=[2,3],name='a')
>>> b = tf.constant([1.0,2.0,3.0,4.0,5.0,6.0],shape=[3,2],name='b')
>>> c = tf.matmul(a,b)
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2018-11-19 22:43:27.732910: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-11-19 22:43:27.824810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-19 22:43:27.825419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties: 
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
totalMemory: 7.93GiB freeMemory: 7.64GiB
2018-11-19 22:43:27.825445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-11-19 22:43:27.995777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-19 22:43:27.995806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971]      0 
2018-11-19 22:43:27.995826: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0:   N 
2018-11-19 22:43:27.996035: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7377 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1
2018-11-19 22:43:28.026839: I tensorflow/core/common_runtime/direct_session.cc:288] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1

>>> print(sess.run(c))
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
2018-11-19 22:44:23.662448: I tensorflow/core/common_runtime/placer.cc:935] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0
a: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-11-19 22:44:23.662561: I tensorflow/core/common_runtime/placer.cc:935] a: (Const)/job:localhost/replica:0/task:0/device:GPU:0
b: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-11-19 22:44:23.662589: I tensorflow/core/common_runtime/placer.cc:935] b: (Const)/job:localhost/replica:0/task:0/device:GPU:0
[[22. 28.]
 [49. 64.]]

验证完毕。