月度归档： 2021年11月

由于CentOS8即将停止维护，所以将系统更换为了Debian，并将TensorFlow在Debian 10上重新安装一下。这里使用的是 Debian 10.11.0版本，机器显卡使用的是1080Ti。如果在CentOS8中安装，可以参考我之前写的步骤： CentOS8安装TensorFlow | OopsDump

先安装Debian 10.11.0，就不按步骤具体说明了。主要注意几点：
1. 安装时，最好选择英文。
2. 分区时，要把默认的系统分区分大一些，或者分出一个 分区 用于挂载/opt目录。
3. 建议安装Gnome桌面环境。

配置Debian

下面的操作都要使用root权限。有一点需要提醒的是，Debian中切换到root用户，要使用“su -”而不能直接用“su”， 不然$PATH中不会包含sbin目录。

关闭自动待机，在界面程序中Settings->Power->Automatic suspend = off。

远程连接的情况，还可以完全禁止休眠和睡眠：（重新开启将mask改为unmask）

# systemctl mask sleep.target suspend.target hibernate.target hybrid-sleep.target

安装完成后，进入系统，配置软件源为本地DVD文件，因为这样可以最快安装，不联网的机器不用担心漏洞问题，也减少自动升级造成的麻烦。先新建个目录 /opt/debian_dvd/ ，将DVD1-3及firmware DVD保存到/opt/debian_dvd/目录下。再在/media/目录创建几个子目录用于开机挂CD，我用了/mdeia/debian_dvd1，/mdeia/debian_dvd2，/mdeia/debian_dvd3，/mdeia/debian_dvd4。然后修改/etc/fstab，增加开机挂CD文件。

(...)
/opt/debian_dvd/debian-10.11.0-amd64-DVD-1.iso /media/debian_dvd1 iso9660 defaults 0 0
/opt/debian_dvd/debian-10.11.0-amd64-DVD-2.iso /media/debian_dvd2 iso9660 defaults 0 0
/opt/debian_dvd/debian-10.11.0-amd64-DVD-3.iso /media/debian_dvd3 iso9660 defaults 0 0
/opt/debian_dvd/firmware-10.11.0-amd64-DVD-1.iso /media/debian_dvd4 iso9660 defaults 0 0

修改/etc/apt/sources.list文件，在其它非注释行首加#改为注释，增加指定DVD为软件源：

deb [trusted=yes] file:/media/debian_dvd1 buster contrib main
deb [trusted=yes] file:/media/debian_dvd2 buster contrib main
deb [trusted=yes] file:/media/debian_dvd3 buster contrib main
deb [trusted=yes] file:/media/debian_dvd4 buster contrib main non-free

deb http://deb.debian.org/debian/ buster main contrib
deb-src http://deb.debian.org/debian/ buster main contrib

如果不想重启机器，可以手动挂载：

# mount -o loop /opt/debian_dvd/debian-10.11.0-amd64-DVD-1.iso /media/debian_dvd1  (其他类似)

更新软件源信息：apt-get update

下面就可以安装一些常用的包了：

# apt-get install vim curl build-essential git python3.7-dev python3-pip linux-headers-$(uname -r) libglvnd-dev dkms pkg-config

将当前用户加入sudo：

# vim /etc/sudoers
(加入一行当前用户)
oopsdump ALL=(ALL) ALL

对于远程的童鞋，要关闭vim的鼠标支持，不能无法用putty上右键粘贴。修改/usr/share/vim/vim81/defaults.vim，将mouse=a该为mouse-=a。

配置远程桌面

安装XRDP远程桌面：

# apt-get install xrdp 
# systemctl enable xrdp

如果远程登录后是桌面空白，可以尝试：

# apt-get purge xserver-xorg-legacy

在使用的用户下：（无需root）

# echo gnome-session > ~/.xsession

如果在远程登录后，遇到提示“Authentication is required to create a color profile”，需要输入管理员密码。可以增加下面文件： /etc/polkit-1/localauthority.conf.d/02-allow-colord-oopsdump-com.conf，内容如下：（注意：将下面的{group}替换为你需要允许的用户组， {group} 可以使用video组）

polkit.addRule(function(action, subject) {
 if ((action.id == "org.freedesktop.color-manager.create-device" ||
 action.id == "org.freedesktop.color-manager.create-profile" ||
 action.id == "org.freedesktop.color-manager.delete-device" ||
 action.id == "org.freedesktop.color-manager.delete-profile" ||
 action.id == "org.freedesktop.color-manager.modify-device" ||
 action.id == "org.freedesktop.color-manager.modify-profile") &&
 subject.isInGroup("{group}")) {
 return polkit.Result.YES;
 }
 });

要注意的是，如果用户在图形界面没有Logout，远程是无法登录的。可以用SSH执行下面命令，强制用户注销：（DISPLAY值根据实际情况修改，可以ps -ef|grep Xorg查看带“:”的值）

env DISPLAY=:0.0 gnome-session-quit --logout

配置中文环境支持：

# dpkg-reconfigure locales
**选上zh_CN.UTF-8
**默认语言仍为en_US.UTF-8

安装中文输入支持：

# apt-get install fcitx fcitx-googlepinyin
# fcitx-configtool 
**在弹出界面点左下+号
**去掉Only Show Current Language的勾
**选中最下的Google Pinyin，然后OK
**然后就可以Ctrl + Space切换输入法

安装VSCode用来编码：

# curl https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.gpg
# install -o root -g root -m 644 microsoft.gpg /usr/share/keyrings/microsoft-archive-keyring.gpg
# sh -c 'echo "deb [arch=amd64,arm64,armhf signed-by=/usr/share/keyrings/microsoft-archive-keyring.gpg] https://packages.microsoft.com/repos/vscode stable main" > /etc/apt/sources.list.d/vscode.list'
# apt-get install apt-transport-https
# apt-get update
# apt-get install code

安装Nvidia闭源驱动

Nvidia的显卡驱动可以在官方网站下载：https://www.nvidia.com/Download/index.aspx?lang=en-us。选择GeForce类型， GeForce 10系列，GTX 1080Ti。

也可用我的下载地址直接下载：https://us.download.nvidia.cn/XFree86/Linux-x86_64/470.57.02/NVIDIA-Linux-x86_64-470.57.02.run

先关闭原开源的Nouveau 驱动：（原因可以参考早前写的在CentOS8下安装Nviadia驱动的过程。）

# vim /etc/modprobe.d/nouveau
文件中输入：
blacklist nouveau
options nouveau modeset=0
# depmod -ae
# vim /etc/default/grub
文件尾增加：
GRUB_CMDLINE_LINUX_DEFAULT="quiet rd.driver.blacklist=nouveau nouveau.modeset=0"
# update-grub2
# update-initramfs -u

关闭系统GUI，并重启：（使用GUI仍然会自动加载nouveau）

# systemctl set-default multi-user.target
# systemctl reboot

先停止Nouveau的驱动：rmmod nouveau

安装Nvidia的闭源驱动：./NVIDIA-Linux-x86_64-470.57.02.run

恢复图形界面并重启：

# systemctl set-default graphical.target
# systemctl reboot

我发现仍然会加载Nouveau的驱动，但Nvidia的驱动已经可以加载了。这时，可以验证一下：

# nvidia-smi
Thu Nov  1 09:08:34 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:05:00.0  On |                  N/A |
| 21%   38C    P8    11W / 250W |     65MiB / 11176MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1040      G   /usr/lib/xorg/Xorg                 39MiB |
|    0   N/A  N/A      1070      G   /usr/bin/gnome-shell               22MiB |
+-----------------------------------------------------------------------------+

安装CUDA和Cudnn

CUDA 11.4 安装按官网步骤提示即可。 也可用我下载地址： https://developer.download.nvidia.com/compute/cuda/11.4.2/local_installers/cuda_11.4.2_470.57.02_linux.run

安装CUDA 11.4：./cuda_11.4.2_470.57.02_linux.run
安装时，可以不再安装驱动：（因为上面安装的一样版本的驱动）

lqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqk
x CUDA Installer                                                               x
x - [ ] Driver                                                                 x
x      [ ] 470.57.02                                                           x
x + [X] CUDA Toolkit 11.4                                                      x
x   [X] CUDA Samples 11.4                                                      x
x   [X] CUDA Demo Suite 11.4                                                   x
x   [X] CUDA Documentation 11.4                                                x
x   Options                                                                    x
x   Install                                                                    x
x                                                                              x
x                                                                              x
x Up/Down: Move | Left/Right: Expand | 'Enter': Select | 'A': Advanced options x
mqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqj

验证是否安装成功：

# cd /usr/local/cuda/samples/1_Utilities/deviceQuery
# make
.....
# ./deviceQuery
./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce GTX 1080 Ti"
  CUDA Driver Version / Runtime Version          11.4 / 11.4
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 11176 MBytes (11719409664 bytes)
  (028) Multiprocessors, (128) CUDA Cores/MP:    3584 CUDA Cores
  GPU Max Clock rate:                            1582 MHz (1.58 GHz)
  Memory Clock rate:                             5505 Mhz
  Memory Bus Width:                              352-bit
  L2 Cache Size:                                 2883584 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 shared memory per multiprocessor:        98304 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
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 5 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.4, CUDA Runtime Version = 11.4, NumDevs = 1
Result = PASS

# make clean
...

需要手动下载Cudnn来安装： https://developer.nvidia.com/rdp/cudnn-download

下载Cudnn需要注册，如果不想注册，可以使用我下载用的地址： https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.4/11.4_20210831/cudnn-11.4-linux-x64-v8.2.4.15.tgz

安装方法：

# cd /usr/local/
# tar -xzvf <your save directory>cudnn-11.4-linux-x64-v8.2.4.15.tgz
# sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

安装虚拟运行环境

安装virtualenv，来进行TensorFlow与CentOS本身包的隔离：

# pip3 install --upgrade virtualenv

创建一个公用虚拟环境：

# mkdir -p /var/venvs/
# virtualenv --system-site-packages  /var/venvs/tensorflow

将下面的Diff信息修改到/var/venvs/tensorflow/bin/activate：

        unset _OLD_VIRTUAL_PYTHONHOME
    fi
+   if ! [ -z "${_OLD_VIRTUAL_LIB:+_}" ] ; then
+       LD_LIBRARY_PATH="$_OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM"
+       export LD_LIBRARY_PATH
+       unset _OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM
+   fi

    # This should detect bash and zsh, which have a hash command that must

  _OLD_VIRTUAL_PATH="$PATH"
- PATH="$VIRTUAL_ENV/bin:$PATH"
+ PATH="$VIRTUAL_ENV/bin:/usr/local/cuda/bin:$PATH"
  export PATH
+ _OLD_VIRTUAL_LIBPATH_OPPSDUMP_COM="$LD_LIBRARY_PATH"
+ LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
+ export LD_LIBRARY_PATH
+ export CUDADIR=/usr/local/cuda

# unset PYTHONHOME if set

当需要使用TensorFlow时，先进行：

# source /var/venvs/tensorflow/bin/activate

安装TensorFlow

安装TensorFlow：

**CPU版本
# pip3 install --upgrade tensorflow
**GPU版本
# pip3 install --upgrade tensorflow-gpu
**旧版本
# pip3 install tensorflow=={package_version}
**其他包PyTorch
# pip3 install --upgrade torch8
**如果安装tensorflow时出现h5py安装问题，可以使用下面方法：
# env H5PY_SETUP_REQUIRES=0 pip3 install -U --no-build-isolation h5py==3.1.0

测试命令：

$ python3 -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

如果遇到问题：

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
(...)

降低numpy版本：

# pip3 uninstall numpy
# pip3 install numpy==1.16.4

再次运行可以成功了。