0 了解本机基本信息

0 参考文档

主要整体是这篇
1.安装cuda和cudnn
2.安装cuda和cudnn
3.安装cuda和cudnn
4.安装cuda和cudnn
1.安装nvidia-docker2
2.安装nvidia-docker2
利用deepo做深度学习环境-官方英文
利用deepo做深度学习环境-中文翻译

1 显卡信息

nvidia-smi
在这里插入图片描述

root@master:/home/hqc# ubuntu-drivers devices
	== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
	modalias : pci:v000010DEd00001B06sv000010DEsd0000120Fbc03sc00i00
	vendor   : NVIDIA Corporation
	model    : GP102 [GeForce GTX 1080 Ti]
	driver   : nvidia-driver-460-server - distro non-free
	driver   : nvidia-driver-450-server - distro non-free
	driver   : nvidia-driver-390 - distro non-free
	driver   : nvidia-driver-418-server - distro non-free
	driver   : nvidia-driver-470 - distro non-free
	driver   : nvidia-driver-470-server - distro non-free
	driver   : nvidia-driver-460 - distro non-free
	driver   : nvidia-driver-495 - distro non-free recommended
	driver   : xserver-xorg-video-nouveau - distro free builtin

提示信息recommend495版本，因此无需重新安装。

2 查看是否安装了cuda/cudnn

root@master:/home/hqc# cat /usr/local/cuda/version.txt
	cat: /usr/local/cuda/version.txt: 没有那个文件或目录
	
root@master:/home/hqc# nvcc -V
	
	Command 'nvcc' not found, but can be installed with:
	
	apt install nvidia-cuda-toolkit

都没有，参考这篇博客

3 关于cuda和cudnn的说明

deepo这个镜像中已经封装了cuda和cudnn，同时直接配置好了绝大多数深度学习的环境。

那为啥还要在本机上安装cuda和cudnn呢？
因为本地开发需要，或者拿到一个现成的深度学习程序需要本地先测试一下是否可运行。

1 安装cuda

nvidia官网下载
在这里插入图片描述
别的版本cuda下载

1 下载

root@master:/home/hqc# wget https://developer.download.nvidia.com/compute/cuda/11.5.1/local_installers/cuda_11.5.1_495.29.05_linux.run

在这里插入图片描述
下载速度也太慢了🤪

2 执行

root@master:/home/hqc# sudo sh cuda_11.5.1_495.29.05_linux.run

在这里插入图片描述
选择continue
出现原因：可能是验证nivdia-docker2时拉取了一个11.0版本的cuda
在这里插入图片描述
输入accept
在这里插入图片描述
注：一定不能再次安装driver
操作：移到driver项，按enter键即去掉勾选。然后install。

3 成功

root@master:/home/hqc# sudo sh cuda_11.5.1_495.29.05_linux.run
	===========
	= Summary =
	===========
	
	Driver:   Not Selected
	Toolkit:  Installed in /usr/local/cuda-11.5/
	Samples:  Installed in /root/, but missing recommended libraries
	
	Please make sure that
	 -   PATH includes /usr/local/cuda-11.5/bin
	 -   LD_LIBRARY_PATH includes /usr/local/cuda-11.5/lib64, or, add /usr/local/cuda-11.5/lib64 to /etc/ld.so.conf and run ldconfig as root
	
	To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-11.5/bin
	***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 495.00 is required for CUDA 11.5 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 /var/log/cuda-installer.log

出现此输出时便代表安装成功

4 配置

root@master:/home/hqc# vi ~/.bashrc

# 在文件结尾添上这两句指令
export PATH="/usr/local/cuda-11.5/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-11.5/lib64:$LD_LIBRARY_PATH"

# source一下使之生效
root@master:/home/hqc# source ~/.bashrc

在这里插入图片描述

5 验证

root@master:/home/hqc# cd /usr/local/cuda-11.5/samples/1_Utilities/deviceQuery

root@master:/usr/local/cuda-11.5/samples/1_Utilities/deviceQuery# sudo make
	/usr/local/cuda/bin/nvcc -ccbin g++ -I../../common/inc  -m64    --threads 0 --std=c++11 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o deviceQuery.o -c deviceQuery.cpp
	nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
	/usr/local/cuda/bin/nvcc -ccbin g++   -m64      -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -o deviceQuery deviceQuery.o 
	nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
	mkdir -p ../../bin/x86_64/linux/release
	cp deviceQuery ../../bin/x86_64/linux/release

root@master:/usr/local/cuda-11.5/samples/1_Utilities/deviceQuery# ./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          11.5 / 11.5
	  CUDA Capability Major/Minor version number:    6.1
	  Total amount of global memory:                 11178 MBytes (11721506816 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 / 1 / 0
	  Compute Mode:
	     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
	
	deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.5, CUDA Runtime Version = 11.5, NumDevs = 1
	Result = PASS

最后出现Result = PASS，才最终说明安装成功。

6 查看

root@master:/usr/local/cuda-11.5/samples/1_Utilities/deviceQuery# nvcc -V
	nvcc: NVIDIA (R) Cuda compiler driver
	Copyright (c) 2005-2021 NVIDIA Corporation
	Built on Thu_Nov_18_09:45:30_PST_2021
	Cuda compilation tools, release 11.5, V11.5.119
	Build cuda_11.5.r11.5/compiler.30672275_0

Build cuda_11.5.r11.5/compiler.30672275_0

2 安装cudnn

官网下载

登录之前需要注册会员，可能会报一些错误，注册好了登录还需要填一些东西，麻烦，随便填好了。网速也慢。

1 下载

下载cuDNN Library for Linux即可,安装cuDNN v8.3.0版本
在这里插入图片描述
在这里插入图片描述
下载速度好慢阿，等待吧。

2 安装

# 进入下载安装包的目录进行查看
root@master:/home/hqc# cd 下载
root@master:/home/hqc/下载# ls
	Anaconda3-5.3.1-Linux-x86_64.sh     iwlwifi-cc-46.3cfab8da.0
	cudnn-11.5-linux-x64-v8.3.0.98.tgz  iwlwifi-cc-46.3cfab8da.0.tgz

# 解压缩
root@master:/home/hqc/下载# tar -zxvf cudnn-11.5-linux-x64-v8.3.0.98.tgz
	cuda/include/cudnn.h
	cuda/include/cudnn_adv_infer.h
	cuda/include/cudnn_adv_infer_v8.h
	cuda/include/cudnn_adv_train.h
	cuda/include/cudnn_adv_train_v8.h
	cuda/include/cudnn_backend.h
	cuda/include/cudnn_backend_v8.h
	cuda/include/cudnn_cnn_infer.h
	cuda/include/cudnn_cnn_infer_v8.h
	...

# 复制解压出的cuda文件到用户文件夹中
root@master:/home/hqc/下载# cp cuda/lib64/* /usr/local/cuda-11.5/lib64/
root@master:/home/hqc/下载# cp cuda/include/* /usr/local/cuda-11.5/include/
root@master:/home/hqc/下载# cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
# 没有任何输出

# 更改一种方法仍然没有输出
root@master:/home/hqc/下载# sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
root@master:/home/hqc/下载# sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/ 
root@master:/home/hqc/下载# sudo chmod a+r /usr/local/cuda/include/cudnn.h 
root@master:/home/hqc/下载# sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
root@master:/home/hqc/下载# cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

查看cudnn信息不输出问题-参考评论
目前还没解决。----已解决
原因：最新的版本信息在cudnn_version.h里了，不在cudnn.h里
root@master:/home/hqc/下载# cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

3 验证

root@master:/home/hqc/下载# cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
	#define CUDNN_MAJOR 8
	#define CUDNN_MINOR 3
	#define CUDNN_PATCHLEVEL 0
	--
	#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
	
	#endif /* CUDNN_VERSION_H */
	
# 代表版本为cudnn8.3.0

更改为cudnn_version.h即可，因为最新的版本信息在cudnn_version.h里了，不在cudnn.h里。

3 安装nivdia-docker2

按官网安装教程操作
在这里插入图片描述
查看官网发现：不需要在本机上安装CUDA，只需要有驱动即可
因此决定，在下载cuda和cudnn的同时安装一下nivdia-docker。

具体指令安装nvidia-docker2

1 加入源

root@master:/home/hqc# 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/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
	OK
	deb https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/$(ARCH) /
	#deb https://nvidia.github.io/libnvidia-container/experimental/ubuntu18.04/$(ARCH) /
	deb https://nvidia.github.io/nvidia-container-runtime/stable/ubuntu18.04/$(ARCH) /
	#deb https://nvidia.github.io/nvidia-container-runtime/experimental/ubuntu18.04/$(ARCH) /
	deb https://nvidia.github.io/nvidia-docker/ubuntu18.04/$(ARCH) /

2 更新

root@master:/home/hqc# sudo apt-get update

3 下载

root@master:/home/hqc# sudo apt-get install -y nvidia-docker2
	正在读取软件包列表... 完成
	正在分析软件包的依赖关系树       
	正在读取状态信息... 完成       
	下列软件包是自动安装的并且现在不需要了：
	  chromium-codecs-ffmpeg-extra lib32gcc1 libc6-i386 libopencore-amrnb0 libopencore-amrwb0
	  linux-hwe-5.4-headers-5.4.0-42
	使用'sudo apt autoremove'来卸载它(它们)。
	将会同时安装下列软件：
	  libnvidia-container-tools libnvidia-container1 nvidia-container-toolkit
	下列【新】软件包将被安装：
	  libnvidia-container-tools libnvidia-container1 nvidia-container-toolkit nvidia-docker2
	升级了 0 个软件包，新安装了 4 个软件包，要卸载 0 个软件包，有 123 个软件包未被升级。
	需要下载 1,075 kB 的归档。
	解压缩后会消耗 4,747 kB 的额外空间。
	获取:1 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64  libnvidia-container1 1.7.0-1 [69.5 kB]
	获取:2 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64  libnvidia-container-tools 1.7.0-1 [22.7 kB]
	获取:3 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64  nvidia-container-toolkit 1.7.0-1 [977 kB]
	获取:4 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64  nvidia-docker2 2.8.0-1 [5,528 B]
	已下载 1,075 kB，耗时 6秒 (170 kB/s)                                                                 
	正在选中未选择的软件包 libnvidia-container1:amd64。
	(正在读取数据库 ... 系统当前共安装有 221226 个文件和目录。)
	正准备解包 .../libnvidia-container1_1.7.0-1_amd64.deb  ...
	正在解包 libnvidia-container1:amd64 (1.7.0-1) ...
	正在选中未选择的软件包 libnvidia-container-tools。
	正准备解包 .../libnvidia-container-tools_1.7.0-1_amd64.deb  ...
	正在解包 libnvidia-container-tools (1.7.0-1) ...
	正在选中未选择的软件包 nvidia-container-toolkit。
	正准备解包 .../nvidia-container-toolkit_1.7.0-1_amd64.deb  ...
	正在解包 nvidia-container-toolkit (1.7.0-1) ...
	正在选中未选择的软件包 nvidia-docker2。
	正准备解包 .../nvidia-docker2_2.8.0-1_all.deb  ...
	正在解包 nvidia-docker2 (2.8.0-1) ...
	正在设置 libnvidia-container1:amd64 (1.7.0-1) ...
	正在设置 libnvidia-container-tools (1.7.0-1) ...
	正在设置 nvidia-container-toolkit (1.7.0-1) ...
	正在设置 nvidia-docker2 (2.8.0-1) ...
	
	配置文件 '/etc/docker/daemon.json'
	 ==> 系统中的这个文件或者是由您创建的，或者是由脚本建立的。
	 ==> 软件包维护者所提供的软件包中也包含了该文件。
	   您现在希望如何处理呢？ 您有以下几个选择：
	    Y 或 I  ：安装软件包维护者所提供的版本
	    N 或 O  ：保留您原来安装的版本
	      D     ：显示两者的区别
	      Z     ：把当前进程切换到后台，然后查看现在的具体情况
	 默认的处理方法是保留您当前使用的版本。
	*** daemon.json (Y/I/N/O/D/Z) [默认选项=N] ? N
	正在处理用于 libc-bin (2.27-3ubuntu1.2) 的触发器 ...
# 此处我选择的N：保留您当前使用的版本（默认）

4 重启docker

root@master:/home/hqc# sudo systemctl restart docker

5 验证

root@master:/home/hqc# sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
	Unable to find image 'nvidia/cuda:11.0-base' locally
	11.0-base: Pulling from nvidia/cuda
	54ee1f796a1e: Pull complete 
	f7bfea53ad12: Pull complete 
	46d371e02073: Pull complete 
	b66c17bbf772: Pull complete 
	3642f1a6dfb3: Pull complete 
	e5ce55b8b4b9: Pull complete 
	155bc0332b0a: Pull complete 
	Digest: sha256:774ca3d612de15213102c2dbbba55df44dc5cf9870ca2be6c6e9c627fa63d67a
	Status: Downloaded newer image for nvidia/cuda:11.0-base
	Wed Dec 15 08:38:22 2021       
	+-----------------------------------------------------------------------------+
	| NVIDIA-SMI 495.44       Driver Version: 495.44       CUDA Version: 11.5     |
	|-------------------------------+----------------------+----------------------+
	| 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  | 00000000:01:00.0 Off |                  N/A |
	| 23%   30C    P8     8W / 250W |    148MiB / 11178MiB |      0%      Default |
	|                               |                      |                  N/A |
	+-------------------------------+----------------------+----------------------+
	                                                                               
	+-----------------------------------------------------------------------------+
	| Processes:                                                                  |
	|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
	|        ID   ID                                                   Usage      |
	|=============================================================================|
	+-----------------------------------------------------------------------------+
# Unable to find image 'nvidia/cuda:11.0-base' locally，需要先拉取

表示安装成功！

4 拉取deepo镜像配置深度学习环境

官网参考

1 拉取deepo完整版

出现错误：拉取不下来，可能是deepo这个镜像太大了，而docker默认的储存目录/var/lib/docker的空间不够

root@master:/home/hqc# docker pull ufoym/deepo
	Using default tag: latest
	latest: Pulling from ufoym/deepo
	6e0aa5e7af40: Pull complete 
	d47239a868b3: Pull complete 
	49cbb10cca85: Pull complete 
	4450dd082e0f: Pull complete 
	b4bc5dc4c4f3: Pull complete 
	5353957e2ca6: Pull complete 
	f91e05a16062: Pull complete 
	7a841761f52f: Pull complete 
	698198ce2872: Pull complete 
	05a2da03249e: Downloading [==================================================>]  804.9MB/804.9MB
	b1761864f72a: Download complete 
	29479e68065f: Download complete 
	3b8001916a15: Downloading [==================================================>]  3.871GB/3.871GB
	latest: Pulling from ufoym/deepo
	6e0aa5e7af40: Downloading [====>                                              ]  2.489MB/26.71MB
	d47239a868b3: Download complete 
	49cbb10cca85: Download complete 
	4450dd082e0f: Download complete 
	b4bc5dc4c4f3: Downloading [============================================>      ]  7.625MB/8.486MB
	5353957e2ca6: Download complete 
	f91e05a16062: Download complete 
	7a841761f52f: Downloading [>                                                  ]  537.5kB/664.6MB
	698198ce2872: Waiting 
	05a2da03249e: Waiting 
	b1761864f72a: Waiting 
	29479e68065f: Waiting 
	3b8001916a15: Waiting 
	error pulling image configuration: read tcp 172.27.228.135:60664->104.18.125.25:443: read: connection reset by peer
# 拉取失败

并且系统报var分区空间不足
在这里插入图片描述

2 解决问题

方法：决定将镜像完整迁移并更改目标路径目录为默认下载目录

# 查看系统内存情况
root@master:/home/hqc# df -h
	文件系统         容量  已用  可用 已用% 挂载点
	udev              32G     0   32G    0% /dev
	tmpfs            6.3G  2.1M  6.3G    1% /run
	/dev/nvme0n1p6    29G  1.7G   25G    7% /
	/dev/nvme0n1p10   94G   18G   72G   20% /usr
	tmpfs             32G     0   32G    0% /dev/shm
	tmpfs            5.0M  4.0K  5.0M    1% /run/lock
	tmpfs             32G     0   32G    0% /sys/fs/cgroup
	/dev/nvme0n1p9   9.4G   37M  8.8G    1% /tmp
	/dev/nvme0n1p7   946M  176M  706M   20% /boot
	/dev/nvme0n1p8    47G   13G   32G   29% /home
	/dev/nvme0n1p11  9.4G  6.6G  2.4G   74% /var
	/dev/loop0       640K  640K     0  100% /snap/gnome-logs/106
	/dev/nvme0n1p1    96M   32M   65M   33% /boot/efi
	/dev/loop1       2.7M  2.7M     0  100% /snap/gnome-system-monitor/174
	/dev/loop2        56M   56M     0  100% /snap/core18/2246
	/dev/loop4       768K  768K     0  100% /snap/gnome-characters/761
	/dev/loop3       219M  219M     0  100% /snap/gnome-3-34-1804/77
	/dev/loop5       248M  248M     0  100% /snap/gnome-3-38-2004/87
	/dev/loop7       128K  128K     0  100% /snap/bare/5
	/dev/loop6        43M   43M     0  100% /snap/snapd/14066
	/dev/loop8       384K  384K     0  100% /snap/gnome-characters/550
	/dev/loop9       2.5M  2.5M     0  100% /snap/gnome-calculator/748
	/dev/loop10      1.0M  1.0M     0  100% /snap/gnome-logs/100
	/dev/loop11       63M   63M     0  100% /snap/gtk-common-themes/1506
	/dev/loop12       62M   62M     0  100% /snap/core20/1242
	/dev/loop13      219M  219M     0  100% /snap/gnome-3-34-1804/72
	/dev/loop14       33M   33M     0  100% /snap/snapd/13640
	/dev/loop15      2.5M  2.5M     0  100% /snap/gnome-calculator/884
	/dev/loop16       56M   56M     0  100% /snap/core18/2253
	/dev/loop17       62M   62M     0  100% /snap/core20/1270
	/dev/loop18      2.3M  2.3M     0  100% /snap/gnome-system-monitor/148
	/dev/loop19      522M  522M     0  100% /snap/pycharm-community/261
	/dev/loop20       66M   66M     0  100% /snap/gtk-common-themes/1519
	/dev/loop21      243M  243M     0  100% /snap/gnome-3-38-2004/76
	tmpfs            6.3G   16K  6.3G    1% /run/user/121
	tmpfs            6.3G   72K  6.3G    1% /run/user/1000
# 发现/usr目录下的存储空间最多，因此打算把这个作为默认下载目录

具体操作参考这篇文档

# 停止docker
root@master:/home/hqc# service docker stop
# 或
root@master:/home/hqc# systemctl stop docker.service

# 创建一个/usr下用来存放镜像的新目录
root@master:/home/hqc# mkdir -p /usr/hqc/docker_root

# 将原目录下的所有镜像文件拷贝到这个目录中
root@master:/home/hqc# cp -R /var/lib/docker/* /usr/hqc/docker_root

# 找到/etc/docker下的daemon.json修改配置
root@master:/home/hqc# cd /etc/docker
root@master:/etc/docker# ls
	daemon.json  daemon.json.dpkg-dist  key.json
root@master:/etc/docker# vim daemon.json
	{
	  "data-root":"/usr/hqc/docker_root",# 添加这一行，注意末尾有逗号
	  "registry-mirrors": ["https://zwir0uyv.mirror.aliyuncs.com"]
	}

# 重新配置
root@master:/etc/docker# systemctl daemon-reload

# 再次运行docker
root@master:/etc/docker# systemctl start docker.service

# 再次查看默认目录
root@master:/etc/docker# docker info |grep "Docker Root Dir"
	WARNING: No swap limit support
	 Docker Root Dir: /usr/hqc/docker_root
# 成功更改

3 再次拉取deepo-拉取成功

root@master:/home/hqc# docker pull ufoym/deepo
	Using default tag: latest
	latest: Pulling from ufoym/deepo
	6e0aa5e7af40: Pull complete 
	d47239a868b3: Pull complete 
	49cbb10cca85: Pull complete 
	4450dd082e0f: Pull complete 
	b4bc5dc4c4f3: Pull complete 
	5353957e2ca6: Pull complete 
	f91e05a16062: Pull complete 
	7a841761f52f: Pull complete 
	698198ce2872: Pull complete 
	05a2da03249e: Pull complete 
	b1761864f72a: Pull complete 
	29479e68065f: Pull complete 
	3b8001916a15: Pull complete 
	Digest: sha256:79473e5e182257ce0aff172670d32d09204c48785c5b5daff1830dad83f4a548
	Status: Downloaded newer image for ufoym/deepo:latest
	docker.io/ufoym/deepo:latest
# 下载成功！！！

4 验证环境

中文翻译官网

root@master:/home/hqc# nvidia-docker run --rm ufoym/deepo nvidia-smi
	docker: Error response from daemon: Unknown runtime specified nvidia.
	See 'docker run --help'.

5 解决问题

1 方案一

参考解决（还未解决）

2 方案二

可用nvidia-docker image ls语句先查看nvidia-docker是否安装成功

root@master:/etc/docker# nvidia-docker image ls
	REPOSITORY                                                TAG         IMAGE ID       CREATED         SIZE
	registry.cn-beijing.aliyuncs.com/hqc-k8s/ali-array-plus   v2.5        7f4f56bbf3c8   3 days ago      928MB
	json-plus                                                 v1.2        fab5150c7c5e   5 days ago      928MB
	python                                                    latest      f48ea80eae5a   4 weeks ago     917MB
	nginx                                                     latest      ea335eea17ab   4 weeks ago     141MB
	quay.io/coreos/flannel                                    v0.15.1     e6ea68648f0c   4 weeks ago     69.5MB
	rancher/mirrored-flannelcni-flannel-cni-plugin            v1.0.0      cd5235cd7dc2   7 weeks ago     9.03MB
	hello-world                                               latest      feb5d9fea6a5   2 months ago    13.3kB
	registry.cn-beijing.aliyuncs.com/hqc-k8s/hello-world      v1.0        feb5d9fea6a5   2 months ago    13.3kB
	ufoym/deepo                                               latest      f07b2fdc30b2   6 months ago    14.3GB
# 和docker images查看一样的效果，应该是已经装好了

可尝试执行一下sudo docker run --rm --gpus all nvidia/cuda:11.5 nvidia-smi，主要是将11.0-base换成11.5，可能是因为本机上安装的是11.5，这个过程中remove掉了11.0-base。因此nvidia-docker可能失效了。

root@master:/etc/docker# sudo docker run --rm --gpus all nvidia/cuda:11.5 nvidia-smi
	Unable to find image 'nvidia/cuda:11.5' locally
	docker: Error response from daemon: manifest for nvidia/cuda:11.5 not found: manifest unknown: manifest unknown.
	See 'docker run --help'.

失败。

3 方案三

也有博客是运行sudo docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi这句指令，可试试

root@master:/etc/docker# sudo docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
	docker: Error response from daemon: Unknown runtime specified nvidia.
	See 'docker run --help'.
# 不行

4 方案四

或者尝试修改/etc/docker/daemon.json文件
在其中加入

{
    # "registry-mirrors": ["https://f1z25q5p.mirror.aliyuncs.com"],
    # 这句是之前配置的阿里云docker加速
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

不过这种方法好像是centos系统中的操作，或许不大对
验证过后果然不行，会造成的docker没法启动

5 方案五

参考官网操作
ubuntu下，运行sudo apt-get install nvidia-container-runtime指令

root@master:/etc/docker# sudo apt-get install nvidia-container-runtime
	正在读取软件包列表... 完成
	正在分析软件包的依赖关系树       
	正在读取状态信息... 完成       
	下列软件包是自动安装的并且现在不需要了：
	  chromium-codecs-ffmpeg-extra lib32gcc1 libc6-i386 libopencore-amrnb0 libopencore-amrwb0 linux-hwe-5.4-headers-5.4.0-42
	使用'sudo apt autoremove'来卸载它(它们)。
	下列【新】软件包将被安装：
	  nvidia-container-runtime
	升级了 0 个软件包，新安装了 1 个软件包，要卸载 0 个软件包，有 123 个软件包未被升级。
	需要下载 4,984 B 的归档。
	解压缩后会消耗 21.5 kB 的额外空间。
	获取:1 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64  nvidia-container-runtime 3.7.0-1 [4,984 B]
	已下载 4,984 B，耗时 1秒 (5,456 B/s)                 
	正在选中未选择的软件包 nvidia-container-runtime。
	(正在读取数据库 ... 系统当前共安装有 221251 个文件和目录。)
	正准备解包 .../nvidia-container-runtime_3.7.0-1_all.deb  ...
	正在解包 nvidia-container-runtime (3.7.0-1) ...
	正在设置 nvidia-container-runtime (3.7.0-1) ...

# 验证，还是不行
root@master:/etc/docker# nvidia-docker run --rm ufoym/deepo nvidia-smi
	docker: Error response from daemon: Unknown runtime specified nvidia.
	See 'docker run --help'.

6 方案六(最后成功的方案）

参考官网方案

root@master:/home/hqc# tee /etc/systemd/system/docker.service.d/override.conf <<EOF
> [Service]
> ExecStart=
> ExecStart=/usr/bin/dockerd --host=fd:// --add-runtime=nvidia=/usr/bin/nvidia-container-runtime
> EOF
[Service]
ExecStart=
ExecStart=/usr/bin/dockerd --host=fd:// --add-runtime=nvidia=/usr/bin/nvidia-container-runtime
# 输入方法为：输入第一行按enter键，在依次每行复制进去回车

# 更改配置文件使之生效
root@master:/home/hqc# sudo systemctl daemon-reload
# 重启docker服务
root@master:/home/hqc# sudo systemctl restart docker

# 验证
root@master:/home/hqc# nvidia-docker run --rm ufoym/deepo nvidia-smi
	Fri Dec 17 02:59:02 2021       
	+-----------------------------------------------------------------------------+
	| NVIDIA-SMI 495.44       Driver Version: 495.44       CUDA Version: 11.5     |
	|-------------------------------+----------------------+----------------------+
	| 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  | 00000000:01:00.0 Off |                  N/A |
	| 23%   28C    P8     9W / 250W |     11MiB / 11178MiB |      0%      Default |
	|                               |                      |                  N/A |
	+-------------------------------+----------------------+----------------------+
	                                                                               
	+-----------------------------------------------------------------------------+
	| Processes:                                                                  |
	|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
	|        ID   ID                                                   Usage      |
	|=============================================================================|
	+-----------------------------------------------------------------------------+
# 成功！！

6 再次验证

# 运行验证，并使Deepo能够在docker容器内使用GPU
root@master:/home/hqc# nvidia-docker run --rm ufoym/deepo nvidia-smi
	Fri Dec 17 03:04:18 2021       
	+-----------------------------------------------------------------------------+
	| NVIDIA-SMI 495.44       Driver Version: 495.44       CUDA Version: 11.5     |
	|-------------------------------+----------------------+----------------------+
	| 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  | 00000000:01:00.0 Off |                  N/A |
	| 23%   28C    P8     8W / 250W |     11MiB / 11178MiB |      0%      Default |
	|                               |                      |                  N/A |
	+-------------------------------+----------------------+----------------------+
	                                                                               
	+-----------------------------------------------------------------------------+
	| Processes:                                                                  |
	|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
	|        ID   ID                                                   Usage      |
	|=============================================================================|
	+-----------------------------------------------------------------------------+

# 将一个交互式shell放入一个容器，该容器不会在你退出之后自动删除。
root@master:/home/hqc# nvidia-docker run -it ufoym/deepo bash
# 进入python虚拟环境
root@2e0a741f5da1:/# python
	Python 3.6.9 (default, Jan 26 2021, 15:33:00) 
	[GCC 8.4.0] on linux
	Type "help", "copyright", "credits" or "license" for more information.
	>>> import tensorflow
		2021-12-17 03:06:21.636087: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
		# 这段好像报错了，但咱也不大懂为啥呀，好像是说不能使用GPU
		2021-12-17 03:06:21.636112: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
	>>> print(tensorflow.__name__, tensorflow.__version__)
		tensorflow 2.5.0
		# 好在可以正常显示tensorflow版本
# 后面再次import tensorflow就没出现之前的输出信息了，不知道为啥
	
	# 再验证一下torch环境
	>>> import torch
	>>> print(torch.__name__, torch.__version__)
		torch 1.9.0.dev20210415+cu101
	# 没出现问题

总之，总算成功拉！

5 总结

整个流程下来，好像本地cuda和cudnn都不是搭建的必须，由于网速和文件很大的因素，可选择安装。

# docker # 深度学习
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