ubuntu18.04配置deepo深度学习环境（cuda + cudnn + nvidia-docker + deepo）--超级细致，并把遇到的错误和所有解决方案都列出来了
Posted ERROR_LESS
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了ubuntu18.04配置deepo深度学习环境（cuda + cudnn + nvidia-docker + deepo）--超级细致，并把遇到的错误和所有解决方案都列出来了相关的知识，希望对你有一定的参考价值。
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
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
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
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
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
输入accept
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
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
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
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
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
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。
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以上是关于ubuntu18.04配置deepo深度学习环境（cuda + cudnn + nvidia-docker + deepo）--超级细致，并把遇到的错误和所有解决方案都列出来了的主要内容，如果未能解决你的问题，请参考以下文章
Ubuntu18.04 + CUDA9.0 + cuDNN7.3 + Tensorflow-gpu-1.12 + Jupyter Notebook深度学习环境配置
ubuntu18.04上安装anaconda-python深度学习环境
docker部署ubuntu18.04深度学习环境——cuda11.1cudnn8.0.5pytorch1.8.0