系统安装情况以及深度学习环境搭建

1.戴尔AL安装Ubuntu16.04问题总结

1）.找不到固态硬盘

由于dell电脑的出厂设置，在BIOS里面都统一把硬盘模式设为RAID ON，但这种模式下可能会导致不能正确识别或者完全发挥处SSD的性能。下面是把RAID模式更改位AHCI的方法。

进入wins之后，按下WIN键+R键，输入msconfig,进入如下引导界面，安全引导打钩，最小打钩，如下所示

之后，点击重新启动；在启动之后，按下F2键进入BIOS依次找到Advanced界面，选中SATA operation,并按下回车键，选择AHCI模式，这里提示要重新装系统，不用理会，点击YES即可，然后按F10，选择YES，重启电脑。重启之后，进入windows的安全模式，再次按下WIN键和R键，并输入msconfig，在引导界面，把之前的安全引导的勾全部去掉，

 

然后点击下面的确定，最后选择重新启动。开机成功，证明我们开启了AHCI模式。

2）触摸屏驱动不对

sudo su
echo 'blacklist i2c_hid' >> /etc/modprobe.d/blacklist.conf
depmod -a
update-initramfs -u

and reboot

 

3）黑屏

 安装完ubuntu16.04之后，可能会出现黑屏的现象，解决方法：

一、

  1. 开机在系统选择时按”e”进入grub的编辑模式
  2. 找到“quite splash”并在后面加上对nvidia显卡的驱动支持”nomodeset”
  3. 按 Ctrl+X或F10启动系统
  4. 以管理员权限编辑/etc/default/grub
  5. 找到GRUB_CMDLINE_LINUX_DEFAULT=”quiet splash”，修改为：GRUB_CMDLINE_LINUX_DEFAULT=”quiet splash nomodeset”
  6. 更新grub：sudo update-grub，并重新开机

二、安装完系统后，可能会进入系统，进入之后执行如下

sudo nano /etc/default/grub
找到这一行：
GRUB_CMDLINE_LINUX_DEFAULT="quiet splash"修改为GRUB_CMDLINE_LINUX_DEFAULT="quiet splash nomodeset"
貌似Ctrl+o, ctrl +x后（具体看下面提示）更新GRUB，输入：sudo update-grub

 

环境搭建

1.安装依赖包

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler  
  
sudo apt-get install --no-install-recommends libboost-all-dev  
  
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev  
  
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev  
  
sudo apt-get install git cmake build-essential

2.安装显卡驱动

由于16.04默认安装的是nouveau显卡驱动，而它不能用于CUDA的，需要卸载并重新安装

1）首先禁用Ubuntu16.04自带的显卡驱动nouveau，禁用方法就是在 /etc/modprobe.d/blacklist-nouveau.conf 文件中添加一条禁用命令，如下

sudo gedit /etc/modprobe.d/blacklist-nouveau.conf 

打开后发现该文件中没有任何内容，写入：

    blacklist nouveau  
    options nouveau modeset=0

保存后关闭文件，注意此时还需执行以下命令使禁用 nouveau 真正生效：

 

 sudo update-initramfs -u

 

检测禁用生效了没，使用如下

lsmod | grep nouveau

下面就开始重装显卡驱动：

我的驱动下载的是NVIDIA_Linux-x86_64-415.13.run,放到自己的用户名home目录下

下面进入文本模式，ctrl+alt+f1,在文本模式下关闭桌面服务：sudo service lightdm stop,（如果要下载之前安装的英伟达驱动可以使用sudo apt-get purge nvidia* ）,进入到存放驱动的目录下，执行如下命令：

sudo sh NVIDIA_Linux-x86_64-415.13.run --no-opengl-files   #run文件的文件名根据自己下的文件名修改，默认是我提供的文件

期间出现如下：

  1. Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 387.26?  
  2. (y)es/(n)o/(q)uit: y 
  1. do you want to run nvidia-xconfig？  
  2. (y)es/(n)o/(q)uit: n 

 

  1. Install the CUDA 9.1 Samples?  
  2. (y)es/(n)o/(q)uit: n 
  1. Install the CUDA 9.1 Toolkit?  
  2. (y)es/(n)o/(q)uit: n 

然后重新启动系统reboot就可以了，在此驱动安装完毕。使用如下命令nvidia-settings和nvidia-smi来验证。

下面安装cuda10(通过命令nvidia-smi来查看到的)，下载之，名字叫cuda_10.0.130_410.48_linux.run。

执行如下

sudo sh cuda_9.1.85_387.26_linux.run --no-opengl-libs 

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 387.26?  
(y)es/(n)o/(q)uit: n  
  
Install the CUDA 9.1 Toolkit?  
(y)es/(n)o/(q)uit: y  
  
Enter Toolkit Location  
 [ default is /usr/local/cuda-9.1 ]:   
  
Do you want to install a symbolic link at /usr/local/cuda?  
(y)es/(n)o/(q)uit: y  
  
Install the CUDA 9.1 Samples?  
(y)es/(n)o/(q)uit: y  
  
Enter CUDA Samples Location  
 [ default is /home/ccem ]:   
  
Installing the CUDA Toolkit in /usr/local/cuda-9.1 ...  
Installing the CUDA Samples in /home/ccem ...  
Copying samples to /home/ccem/NVIDIA_CUDA-9.1_Samples now...  
Finished copying samples.  
  
===========  
= Summary =  
===========  
  
Driver:   Not Selected  
Toolkit:  Installed in /usr/local/cuda-9.1  
Samples:  Installed in /home/ccem  
  
Please make sure that  
 -   PATH includes /usr/local/cuda-9.1/bin  
 -   LD_LIBRARY_PATH includes /usr/local/cuda-9.1/lib64, or, add /usr/local/cuda-9.1/lib64 to /etc/ld.so.conf and run ldconfig as root  
  
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-9.1/bin  
  
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-9.1/doc/pdf for detailed information on setting up CUDA.  
  
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 384.00 is required for CUDA 9.1 functionality to work.  
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:  
 sudo <CudaInstaller>.run -silent -driver  
  
Logfile is /tmp/cuda_install_36731.log

如果出现如下，则说明缺少依赖库

Installing the CUDA Toolkit in /usr/local/cuda-9.1 …   
Missing recommended library: libGLU.so   
Missing recommended library: libX11.so   
Missing recommended library: libXi.so   
Missing recommended library: libXmu.so
则对应安装依赖库
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

安装完后，配置cuda的环境变量下面是为当前用户配置

sudo gedit ~/.bashrc  
export PATH=/usr/local/cuda/bin:$PATH     #/usr/local/cuda和/usr/local/cuda-10.0是同一个文件夹，前者是后者的镜像  
  
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

source ~/.bashrc使之生效；下面是为所有用户配置环境变量

$ sudo vim /etc/profile
export PATH=/usr/local/cuda/bin:${PATH} # 必须
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH} # 非必须，可以用前面介绍的方式

检验CUDA 是否安装成功，输入：

cd /usr/local/cuda-10.0/samples/1_Utilities/deviceQuery  
  
sudo make  
  
./deviceQuery

下面是安装cuDNN v7，我下载的版本是cudnn-10.0-linux-x64-v7.4.1.5.tgz。把他解压到任何路径，我的解压路径位/usr/local/cudnn下面(tar zxvf ./cudnn-10.0-linux-x64-v7.4.1.5.tgz -C /usr/local/cudnn)，解压后的文件夹名为cuda，文件夹中包含两个文件夹：一个为include，另一个为lib64。将解压后的文件中的lib64文件夹关联到环境变量中。这一步很重要，sudo gedit ~/.bashrc,输入如下内容

export LD_LIBRARY_PATH=/your/path/to/cudnn/lib64:$LD_LIBRARY_PATH

其中/your/path/to/cudnn/lib64是指.tgz解压后的文件所在路径中的lib64文件夹。保存，退出并source一下，再重启一下Terminal（终端），该步骤可以成功的配置cuDNN的Lib文件，配置cuDNN的最后一步就是将解压后的cuDNN文件夹（一般该文件名为cuda）中的include文件夹（/your/path/to/cudnn/include）中的cudnn.h文件拷贝到/usr/local/cuda/include中，由于进入了系统路径，因此执行该操作时需要获取管理员权限。

   cd cuda/include
   sudo cp *.h /usr/local/cuda/include/
之后，再重置cudnn.h文件的读写权限： sudo chmod a+r /usr/local/cuda/include/cudnn.h，至此，cuDNN的配置就全部安装完成了。

下面安装tensorflow，我选择的源码安装方式，参考https://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/get_started/os_setup.md以及https://blog.csdn.net/a446712385/article/details/79149977

在终端输入以下命令：

$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow

–recurse-submodules 参数是必须得, 用于获取 TesorFlow 依赖的 protobuf 库.放入home目录下，下面下载Bazel并安装之

下载的名字为bazel-0.15.2-installer-linux-x86_64.sh

安装其他依赖：

sudo apt-get update
sudo apt-get install python-pip python-numpy swig python-dev python-wheel
sudo apt-get install pkg-config zip g++ zlib1g-dev unzip
sudo apt-get install default-jdk






//For Python 2.7: 

  sudo apt-get install python-numpy swig python-dev python-wheel


//For Python 3.x: 

  $ sudo apt-get install python3-numpy swig python3-dev python3-wheel












在这里使用python3.


export PATH=/usr/bin:$PATH,这是python环境变量的配置


./bazel-0.15.2-installer-linux-x86_64.sh --user
将执行路径output/bazel 添加到$PATH环境变量后bazel工具就可以使用了，环境变量配置
~/.bashrc下面输入
export PATH=$HOME/bin:$PATH


下面去配置tensorflow，


进入到它的文件夹下面，执行./configure


这部分是配置tensorflow，然后再生成whl安装tensorflow。 

直接pip安装就是安装官网提供的已经配置好的whl包，而源码安装就是利用bazel编译后，生成whl包，再进行安装。


（如果是需要开启GPU，在这里需要配置cuda和cudnn）


1）配置








You have bazel 0.17.2 installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3.5


Found possible Python library paths:
  /usr/local/lib/python3.5/dist-packages
  /usr/lib/python3/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib/python3.5/dist-packages]

Do you wish to build TensorFlow with Apache Ignite support? [Y/n]: n
No Apache Ignite support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 


Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 


Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.

Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: 


Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]: 


Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 


Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
    --config=gdr            # Build with GDR support.
    --config=verbs          # Build with libverbs support.
    --config=ngraph         # Build with Intel nGraph support.
Configuration finished



View Code


CUDA 9.0 : 10.0


上面的部分代码是参考https://www.cnblogs.com/seniusen/p/9756302.html


以上在配置的过程中可能会出错，在这里我把系统默认的Python2改为了python3.5，使用方法如下


备份原来的python2软链接，sudo cp /usr/bin/python /usr/bin/python_bak,  sudo rm /usr/bin/python 然后执行sudo ln -s /usr/bin/python3.5 /usr/bin/python,使用python --version测试成功，return back to python2.7 sudo rm /usr/bin/python  sudo ln -s /usr/bin/python2.7 /usr/bin/python但是在编译tensorflow的时候会出现一些问题，NO module named keras.preprocessing，解决方法sudo pip install keras,但是又出现其他的错误ModuleNotFoundError: No module named 'pip._internal'，解决方法




 wget https://bootstrap.pypa.io/get-pip.py  --no-check-certificate
sudo python get-pip.py




然后测试，pip -V，即可解决。


下面进行编译


在tensorflow目录下，输入以下三个命令：




bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package




编译很久，结束之后，执行




bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg




tmp/tensorflow_pkg目录下找到（whl包的名字可能不一样，和电脑属性或者当前tensorflow版本之类的有关），我的名字为tensorflow-1.12.0rc0-cp35-cp35m-linux_x86_64.whl


 将其复制到主文件夹,以便安装


sudo pip install tensorflow-1.12.0rc0-cp35-cp35m-linux_x86_64.whl

等待安装完成后，输入以下命令，不报错即安装成功.


 


 测试是否安装成功




python
#这里会输出python的版本信息，见下图
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
#这里会输出GPU的相关信息，表明TensorFlow是在GPU上运行的，见下图
>>> sess.run(hello)
b'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(22)
>>> sess.run(a+b)
32
>>>




 以下是tensorflow c++的接口设置https://www.cnblogs.com/seniusen/p/9756302.html







		

			posted on 
2018-12-02 21:34 
gary_123 
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