一台新联想机器的配置过程

分手后的思念是犯贱 2022-11-19 07:42 176阅读 0赞

**目录**

0.目标环境

1.安装双系统

2.安装chrome,输入法

3.安装gcc

4.安装显卡驱动与高版本cuda

5.安装pycharm

6.安装docker与nvidia-docker

7.联想新机安装显卡驱动

8.安装pip

--------------------

# 0.目标环境 #

windows+ubuntu共存
    nvidia driver
    cuda
    nvidia-docker
    pycharm
    chrome
    python3.8
    pip

# 1.安装双系统 #

由于本机上是有自带的windows系统的，所以，这里直接使用u盘安装ubuntu系统。

1）制作ubuntu20.04的u盘

UltralSo+官网下载系统+u盘

2）安装

[https://blog.csdn.net/zr459927180/article/details/51627910][https_blog.csdn.net_zr459927180_article_details_51627910]

安装时分辨率无法调整，看不到分区选项时：[https://www.zhihu.com/question/299230727][https_www.zhihu.com_question_299230727]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70][]

# 2.安装chrome,输入法 #

chrome:[https://blog.csdn.net/snowdream86/article/details/106160498][https_blog.csdn.net_snowdream86_article_details_106160498]

输入法:

[https://pinyin.sogou.com/linux/?r=pinyin][https_pinyin.sogou.com_linux_r_pinyin]

[https://www.cnblogs.com/Areas/p/13438171.html][https_www.cnblogs.com_Areas_p_13438171.html]

[https://zhuanlan.zhihu.com/p/142206571][https_zhuanlan.zhihu.com_p_142206571]

# 3.安装gcc #

[https://blog.csdn.net/m0\_37412775/article/details/109355044][https_blog.csdn.net_m0_37412775_article_details_109355044]

sudo apt-get update
    sudo apt-get upgrade
    sudo apt-get  install  build-essential
    sudo apt-get -y install gcc-7 g++-7

change gcc version:[https://linuxconfig.org/how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa][https_linuxconfig.org_how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa]

# 4.安装显卡驱动与高版本cuda  #

cp cuda/lib64/* /usr/local/cuda-11.1/lib64/
    cp cuda/include/* /usr/local/cuda-11.1/include/

1）下载安装显卡驱动

[https://blog.csdn.net/Thanlon/article/details/106125738][https_blog.csdn.net_Thanlon_article_details_106125738]

2）安装cuda-11.1.0

[https://developer.nvidia.com/zh-cn/cuda-downloads?target\_os=Linux&target\_arch=x86\_64&target\_distro=Ubuntu&target\_version=2004&target\_type=runfilelocal][https_developer.nvidia.com_zh-cn_cuda-downloads_target_os_Linux_target_arch_x86_64_target_distro_Ubuntu_target_version_2004_target_type_runfilelocal]

[https://blog.csdn.net/qq\_36999834/article/details/107589779][https_blog.csdn.net_qq_36999834_article_details_107589779]

3）安装cudnn

wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
    sudo sh cuda_11.1.0_455.23.05_linux.run

参考：

[https://blog.csdn.net/qq\_33200967/article/details/80689543][https_blog.csdn.net_qq_33200967_article_details_80689543]

# 5.安装pycharm #

[https://www.jetbrains.com/pycharm/download/other.html][https_www.jetbrains.com_pycharm_download_other.html]

[https://blog.csdn.net/feimeng116/article/details/105837483][https_blog.csdn.net_feimeng116_article_details_105837483]

cp pycharm-professional-2019.3.5.tar.gz  /home/xxx/software
    tar -zxvf pycharm-professional-2019.3.5.tar.gz -C ./software/pycharm-2019.3/

# 6.安装docker与nvidia-docker #

[https://docs.docker.com/engine/install/ubuntu/][https_docs.docker.com_engine_install_ubuntu]

[https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html\#nvidia-drivers][https_docs.nvidia.com_datacenter_cloud-native_container-toolkit_install-guide.html_nvidia-drivers]

sudo apt-get update
    sudo apt-get install -y apt-transport-https  ca-certificates  curl  gnupg-agent  software-properties-common
    curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
    sudo apt-key fingerprint 0EBFCD88
    sudo add-apt-repository  "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
     bionic \
     stable"
    sudo apt-get update
    sudo apt-get install -y docker-ce docker-ce-cli containerd.io
    sudo docker run hello-world
    sudo usermod -aG docker gmt
    sudo systemctl restart docker
    sudo systemctl status docker
    docker --version
    curl https://get.docker.com | sh   && sudo systemctl --now enable docker
    distribution=$(. /etc/os-release;echo $ID$VERSION_ID)    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
    sudo apt-get update
    sudo apt-get install -y nvidia-docker2
    sudo systemctl restart docker

导入自己的之前的nvidia-docker 镜像，并创建一个容器：

sudo docker load -i tensorflow.tar
    sudo docker images # 查看导入的images
    sudo sh ./tf_dep/debug.sh # 创建新容器
    
    
    
    # debug.sh
    #!/bin/bash
    nvidia-docker run -m 8GB -it --net=host \
                    -v /home/***/tensorflow/:/root \
                    --name tensorflow tensorflow:v2  /bin/bash

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1][]

# 7.联想新机安装显卡驱动 #

尝试过以下，nvidia-smi都无法找到显卡驱动：

1）ubuntu16.04、ubuntu18.04、ubuntu20.04

2）不同内核版本5.4与5.8

3）使用dmesg查看过kernel的log

4）显卡驱动安装方式（图像界面安装、终端安装），高低版本

5）安装完不同版本的cuda。

最后成功步骤：

1）将Ctrl+Alt+F1，进入bios，修改secure boot为disabled；

2）启动项中ubuntu选项，以5.4.0 kernel进入系统（此处是需要自己去安装内核的）；

3）安装gcc 7，g++ 7；

4）卸载之前安装的驱动，autoinstall

命令如下：

sudo apt-get update
    sudo apt-get upgrade
    sudo apt-get  install  build-essential
    sudo apt-get -y install gcc-7 g++-7
    
    sudo apt-get remove --purge nvidia* 
    sudo ubuntu-drivers autoinstall
    sudo reboot

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 2][]

注：这里可能是我安装的cuda版本太多了，与前面不太一致。将就看，差别不大。

# 8.安装pip #

ubuntu系统自带的python3.8好像没有pip，这里应该需要自己安装了。对于使用python的，这个必须要有。

sudo apt install -y python3-pip

参考：

[https://blog.csdn.net/smcaa/article/details/86482872][https_blog.csdn.net_smcaa_article_details_86482872]

[https://blog.csdn.net/cskywit/article/details/85211884][https_blog.csdn.net_cskywit_article_details_85211884]

[https_blog.csdn.net_zr459927180_article_details_51627910]: https://blog.csdn.net/zr459927180/article/details/51627910
[https_www.zhihu.com_question_299230727]: https://www.zhihu.com/question/299230727
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70]: /images/20221119/805dde7e7ede4780923dc5a801f94ab7.png
[https_blog.csdn.net_snowdream86_article_details_106160498]: https://blog.csdn.net/snowdream86/article/details/106160498
[https_pinyin.sogou.com_linux_r_pinyin]: https://pinyin.sogou.com/linux/?r=pinyin
[https_www.cnblogs.com_Areas_p_13438171.html]: https://www.cnblogs.com/Areas/p/13438171.html
[https_zhuanlan.zhihu.com_p_142206571]: https://zhuanlan.zhihu.com/p/142206571
[https_blog.csdn.net_m0_37412775_article_details_109355044]: https://blog.csdn.net/m0_37412775/article/details/109355044
[https_linuxconfig.org_how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa]: https://linuxconfig.org/how-to-switch-between-multiple-gcc-and-g-compiler-versions-on-ubuntu-20-04-lts-focal-fossa
[https_blog.csdn.net_Thanlon_article_details_106125738]: https://blog.csdn.net/Thanlon/article/details/106125738
[https_developer.nvidia.com_zh-cn_cuda-downloads_target_os_Linux_target_arch_x86_64_target_distro_Ubuntu_target_version_2004_target_type_runfilelocal]: https://developer.nvidia.com/zh-cn/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=2004&target_type=runfilelocal
[https_blog.csdn.net_qq_36999834_article_details_107589779]: https://blog.csdn.net/qq_36999834/article/details/107589779
[https_blog.csdn.net_qq_33200967_article_details_80689543]: https://blog.csdn.net/qq_33200967/article/details/80689543
[https_www.jetbrains.com_pycharm_download_other.html]: https://www.jetbrains.com/pycharm/download/other.html
[https_blog.csdn.net_feimeng116_article_details_105837483]: https://blog.csdn.net/feimeng116/article/details/105837483
[https_docs.docker.com_engine_install_ubuntu]: https://docs.docker.com/engine/install/ubuntu/
[https_docs.nvidia.com_datacenter_cloud-native_container-toolkit_install-guide.html_nvidia-drivers]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#nvidia-drivers
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1]: /images/20221119/0cc32d4947f141ec856d06540e7fd672.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 2]: /images/20221119/23e3904ad59f420caec18069b8ba6845.png
[https_blog.csdn.net_smcaa_article_details_86482872]: https://blog.csdn.net/smcaa/article/details/86482872?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.control
[https_blog.csdn.net_cskywit_article_details_85211884]: https://blog.csdn.net/cskywit/article/details/85211884