实验室GPU操作记录20211020

淩亂°似流年 2022-09-16 13:18 163阅读 0赞

# 1013维修后，操作记录 #

## 第一次运行 ##

我用的是用户`xxxy2080_10`

首先，用我的电脑VNCviewer打开服务器，发现只能显示一部分屏幕，而且，当我把窗口放大发现，这部分屏幕也会被放大。总之，就是看不了完整的整张屏幕。解决方法是，带参数启动vncviewer：

需要先将VNCviewer加入path。然后

vncviewer --FullScreen=1

然后，根据教程来

### 下载python，Anconada3 ###

// 创建screen会话
    screen -S python
    
    // 我看到Home文件夹中已经有了,所以有些不用再下了
    // 下载python3.7.10
    wget -c https://www.python.org/ftp/python/3.7.10/Python-3.7.10.tar.xz
    
    // 下载anaconda3
    wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh

### 安装python ###

// 下面不空行的,是解压python,安装python
    tar vxf Python-3.7.10.tar.xz 
    cd Python-3.7.10/
    // 注意,下面prefix的参数使用命令 pwd 获取
    ./configure --prefix=/xxxy2080hppc/xxxy2080_10/Python-3.7.10
    make &&  make altinstall 
    
    // 添加环境变量
    vim ~/.bash_profile
    // 下面写入之前,依次: i(切换输入模式)-写入下面的-esc(切换命令模式)-:wq(保存退出)
    :/xxxy2080hppc/xxxy2080_10/Python-3.7.10/bin 
    // 使环境变量生效
    source ~/.bash_profile 
    python3.7 -V 		// Python 3.7.10

### pycharm ###

看到系统路径里已经有了pycharm,于是试了一下

// 首先,系统路径里有这些:
    PATH=$PATH:$HOME/.local/bin:$HOME/bin:/usr/local/cuda-10.2/bin:/usr/local/TensorRT-7.1.3.4/lib:/xxxy2080hppc/xxxy2080_10/pycharm-community-2021.2.2/bin/:/xxxy2080hppc/xxxy2080_10/Python-3.7.10/bin
    
    使用教程给出的命令是：
    cd pycharm-community-2021.2.2/bin/ 
    sh pycharm.sh

### 安装anaconda ###

// 安装anaconda
    // 首先修改Anaconda3-2021.05-Linux-x86_64.sh脚本为可执行脚本。 
    chmod u+x Anaconda3-2021.05-Linux-x86_64.sh 
    
    ./Anaconda3-2021.05-Linux-x86_64.sh
    出现提示"Permission denied"
    进入文件存放地址,我的在Home中,右击设置属性-Permission,将后面两个属性改为读和写
    再次运行上面的命令,就可以了
    
    Please answer 'yes' or 'no':'
    >>> yes
    
    // 接下来这个命令好像是创建安装目录
    /share/nishome/20070104_5/anaconda3
    报错：mkdir: cannot create directory \u2018/share\u2019: Permission denied
    
    // 上面这一步是使用共享文件安装,但是没有权限创建文件夹,故报错,解决方法是
    [/xxxy2080hppc/xxxy2080_10/anaconda3] >>> (直接按Enter)

（这一步没有成功，探索性的）获取权限,参考这个帖子,建议不光要读博文,也要读评论,慎重!!!

[出现Permission denied的解决办法（750权限谨慎使用）][Permission denied_750]

// 获取权限
    sudo chmod -R 750 share
    //这时会显示以下内容,这里直接翻译一下:
    我们相信您已经收到了当地系统管理员的常规讲座。 通常归结为以下三点：
    #1) 尊重他人的隐私。
    #2) 打字前请三思。
    #3) 能力越大，责任越大。
    // 紧接着,需要输入密码,然后,并没有成功。它提示
    xxxy2080_10 不在 sudoers 文件中。 此事件将被报告。

添加anaconda环境变量:

// 添加环境变量
    vim ~/.bash_profile
    // 下面写入之前,依次: i(切换输入模式)-写入下面的-esc(切换命令模式)-:wq(保存退出)
    :/xxxy2080hppc/xxxy2080_10/anaconda3/bin 
    // 使环境变量生效
    source ~/.bash_profile 
    
    // 下面这句: 安装完conda一定要执行，否则会导致VNC黑屏
    (base) [xxxy2080_10@xxxy2080 ~]$ conda config --set auto_activate_base false
    (base) [xxxy2080_10@xxxy2080 ~]$ anaconda -V
    anaconda Command line client (version 1.7.2)

至此,python 3.7.10,anaconda 3,pycharm 安装完毕!

### 安装tf-1 ###

// 这儿可能需要先添加一下源,清华源.参照教程中来
    
    // 安装TensorFlow 1.15
    conda create -n tf_1 python=3.7.10 tensorflow-gpu=1.15.0

安装完毕后,测试tf -1 环境是否可用

(base) [xxxy2080_10@xxxy2080 ~]$ conda env list
    # conda environments:
    #
    base                  *  /xxxy2080hppc/xxxy2080_10/anaconda3
    tf_1                     /xxxy2080hppc/xxxy2080_10/anaconda3/envs/tf_1
    
    (base) [xxxy2080_10@xxxy2080 ~]$ conda activate tf_1
    (tf_1) [xxxy2080_10@xxxy2080 ~]$ python
    Python 3.7.10 (default, Jun  4 2021, 14:48:32) 
    [GCC 7.5.0] :: Anaconda, Inc. on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import tensorflow as tf
    >>> tf.__version__
    '1.15.0'
    >>> tf.test.is_gpu_available()
    2021-10-19 15:53:58.720085: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
    2021-10-19 15:53:58.749006: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
    2021-10-19 15:53:58.760656: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a5ff66e410 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
    2021-10-19 15:53:58.760754: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
    2021-10-19 15:53:58.763249: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
    2021-10-19 15:53:59.777727: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:18:00.0
    2021-10-19 15:53:59.779281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:3b:00.0
    2021-10-19 15:53:59.780800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 2 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:86:00.0
    2021-10-19 15:53:59.782295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 3 with properties: 
    name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
    pciBusID: 0000:af:00.0
    2021-10-19 15:53:59.782762: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
    2021-10-19 15:53:59.784861: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
    2021-10-19 15:53:59.787020: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
    2021-10-19 15:53:59.787481: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
    2021-10-19 15:53:59.789899: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
    2021-10-19 15:53:59.791726: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
    2021-10-19 15:53:59.797119: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
    2021-10-19 15:53:59.807201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1, 2, 3
    2021-10-19 15:53:59.807265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
    2021-10-19 15:53:59.814790: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
    2021-10-19 15:53:59.814839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 1 2 3 
    2021-10-19 15:53:59.814892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N N N N 
    2021-10-19 15:53:59.814906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 1:   N N N N 
    2021-10-19 15:53:59.814938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 2:   N N N N 
    2021-10-19 15:53:59.814971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 3:   N N N N 
    2021-10-19 15:53:59.822682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 10312 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:18:00.0, compute capability: 7.5)
    2021-10-19 15:53:59.826405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:1 with 10312 MB memory) -> physical GPU (device: 1, name: GeForce RTX 2080 Ti, pci bus id: 0000:3b:00.0, compute capability: 7.5)
    2021-10-19 15:53:59.829359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:2 with 10312 MB memory) -> physical GPU (device: 2, name: GeForce RTX 2080 Ti, pci bus id: 0000:86:00.0, compute capability: 7.5)
    2021-10-19 15:53:59.832894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:3 with 10312 MB memory) -> physical GPU (device: 3, name: GeForce RTX 2080 Ti, pci bus id: 0000:af:00.0, compute capability: 7.5)
    2021-10-19 15:53:59.836824: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a60163e770 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
    2021-10-19 15:53:59.836863: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
    2021-10-19 15:53:59.836878: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce RTX 2080 Ti, Compute Capability 7.5
    2021-10-19 15:53:59.836892: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): GeForce RTX 2080 Ti, Compute Capability 7.5
    2021-10-19 15:53:59.836906: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (3): GeForce RTX 2080 Ti, Compute Capability 7.5
    True

至此,tf 1.15.0安装成功

### 传输文件 ###

XFTP新建会话后,设置如下:

> 连接名及远端主机: 问我我告诉你
> 
> 后面两项的账号密码: 使用的用户服务器账号和密码

## 查看配置信息 ##

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_19_color_FFFFFF_t_70_g_se_x_16]

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16]

# 服务器维修前，操作记录 #

## 登录 ##

ssh登录,使用cmd  
![在这里插入图片描述][3a4a4fa3a0224e4fa3c9689d30793c38.png]  
输入yes:  
![在这里插入图片描述][dfa7d53a4d3f4062a317c84c1f9ea0f6.png]  
输入密码:  
![在这里插入图片描述][1ebc496a88514821ad727a9af3f737a1.png]

#### （选做）查看：版本 ####

查看NVIDIA版本：  
![在这里插入图片描述][727c48f709d74ae19a9d9bdae216daad.png]

查看GPU状态:  
![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16 1]

## 拷贝conda本地环境至实验室Gpu： ##

参考链接：[conda环境迁移到其他机器上][conda]  
[anaconda使用教程+直接环境拷贝移植所遇到的问题解决][anaconda]博文最后

### 导出本地环境 ###

本地端操作,先是激活本地使用的环境。  
![在这里插入图片描述][d1d1beee3b17432e93a582d000cc3142.png]  
然后，导出conda安装的包记录。  
![在这里插入图片描述][0c7b151c6bbf45efbbc4b0d704551604.png]  
然后，导出pip安装的包记录。  
![在这里插入图片描述][f9eedae5dc5641dba06c7b7e27920aba.png]

### 将导出环境传输给server ###

![在这里插入图片描述][d62fd8df85904260954db823e61da54a.png]

### server端部署环境 ###

#### （选做）查看：出现问题 ####

###### 磁盘不足了 ######

![在这里插入图片描述][b6d26ae640b54954b6c448687b830cbb.png]

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16 2]

###### 偶然发现:pytorch直接可用 ######

![在这里插入图片描述][b95f2de42c9e485083d6451b38011629.png]

[Permission denied_750]: https://blog.csdn.net/qq_16525279/article/details/80245350
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_19_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/9c25e1d10e114093aa2d3db7bfb3a7a2.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/96742941ec7c4b1891e59f3df2c31be2.png
[3a4a4fa3a0224e4fa3c9689d30793c38.png]: /images/20220828/de31f0549f8a421f9b5d015424c9f47b.png
[dfa7d53a4d3f4062a317c84c1f9ea0f6.png]: /images/20220828/f350da9478d14ccda88b3aadf1c6e663.png
[1ebc496a88514821ad727a9af3f737a1.png]: /images/20220828/5d7c425f8fe7441a821ac3a7ee963064.png
[727c48f709d74ae19a9d9bdae216daad.png]: /images/20220828/8568bf7e22294856b838e62342ea4a7b.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16 1]: /images/20220828/ecd392be59434446aef7bb4e4b1207bc.png
[conda]: https://blog.csdn.net/ysq319/article/details/102773615
[anaconda]: https://blog.csdn.net/qq_41554005/article/details/89052435
[d1d1beee3b17432e93a582d000cc3142.png]: /images/20220828/8b88c1a87c1f4ea69c3f6d635d35836a.png
[0c7b151c6bbf45efbbc4b0d704551604.png]: /images/20220828/5a0c21e190a2477fba6121922164d244.png
[f9eedae5dc5641dba06c7b7e27920aba.png]: /images/20220828/940b0c60364a458499f5f96883721c6c.png
[d62fd8df85904260954db823e61da54a.png]: /images/20220828/d286e0e473d14e0386d75036921ccb5e.png
[b6d26ae640b54954b6c448687b830cbb.png]: /images/20220828/2bf4d7e5c59f4274821d7cf486332c1f.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSp6JOd6JOd55qE5pys5oiR_size_20_color_FFFFFF_t_70_g_se_x_16 2]: /images/20220828/5de105c48cf14ee49fdb3a35f8cd15c5.png
[b95f2de42c9e485083d6451b38011629.png]: /images/20220828/3a7f6f81c382400aaaa7fdee2e10150b.png