CentOS7 安裝Nvidia Tesla T4驅動 CUDA CUDNN
阿新 • • 發佈：2021-07-21
顯示卡為 Nvidia Tesla T4
前置
安裝gcc編譯環境以及核心相關的包
# 新增阿里雲的安裝源
curl -o /etc/yum.repos.d/epel.repo http://mirrors.aliyun.com/repo/epel-7.repo curl -o /etc/yum.repos.d/CentOS-Base.repo https://mirrors.aliyun.com/repo/Centos-7.repo sed -i -e '/mirrors.cloud.aliyuncs.com/d' -e '/mirrors.aliyuncs.com/d' /etc/yum.repos.d/CentOS-Base.repo
# 安裝基礎環境
yum -y install apr autoconf automake bash bash-completion bind-utils bzip2 bzip2-devel chrony cmake coreutils curl curl-devel dbus dbus-libs dhcp-common dos2unix e2fsprogs e2fsprogs-devel file file-libs freetype freetype-devel gcc gcc-c++ gdb glib2 glib2-devel glibc glibc-devel gmp gmp-devel gnupg iotop kernel kernel-devel kernel-doc kernel-firmware kernel-headers krb5-devel libaio-devel libcurl libcurl-devel libevent libevent-devel libffi-devel libidn libidn-devel libjpeg libjpeg-devel libmcrypt libmcrypt-devel libpng libpng-devel libxml2 libxml2-devel libxslt libxslt-devel libzip libzip-devel lrzsz lsof make microcode_ctl mysql mysql-devel ncurses ncurses-devel net-snmp net-snmp-libs net-snmp-utils net-tools nfs-utils nss nss-sysinit nss-tools openldap-clients openldap-devel openssh openssh-clients openssh-server openssl openssl-devel patch policycoreutils polkit procps readline-devel rpm rpm-build rpm-libs rsync sos sshpass strace sysstat tar tmux tree unzip uuid uuid-devel vim wget yum-utils zip zlib* jq
# 時間同步
systemctl start chronyd && systemctl enable chronyd
# 重啟
reboot
# 整體升級
yum update -y
# 再次重啟
reboot
檢查
注意：安裝核心包時需要先檢查一下當前核心版本是否與所要安裝的kernel-devel/kernel-doc/kernel-headers的版本一致，請務必保持兩者版本一致，否則後續的編譯過程會出問題。
[root@localhost opt]# uname -a Linux localhost 3.10.0-1160.31.1.el7.x86_64 #1 SMP Thu Jun 10 13:32:12 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux [root@localhost opt]# yum list | grep kernel- kernel-devel.x86_64 3.10.0-1160.31.1.el7 @updates kernel-doc.noarch 3.10.0-1160.31.1.el7 @updates kernel-headers.x86_64 3.10.0-1160.31.1.el7 @updates kernel-tools.x86_64 3.10.0-1160.31.1.el7 @updates kernel-tools-libs.x86_64 3.10.0-1160.31.1.el7 @updates kernel-abi-whitelists.noarch 3.10.0-1160.31.1.el7 updates kernel-debug.x86_64 3.10.0-1160.31.1.el7 updates kernel-debug-devel.x86_64 3.10.0-1160.31.1.el7 updates kernel-tools-libs-devel.x86_64 3.10.0-1160.31.1.el7 updates
兩種方法可以解決版本不一致的問題：
方法一、升級核心版本，具體升級方法請自行百度, 可以不用設為預設啟動核心；
方法二、安裝與核心版本一致的kernel-devel/kernel-doc/kernel-headers，例如：
yum install "kernel-devel-uname-r == $(uname -r)"
安裝顯示卡驅動
下載
https://www.nvidia.cn/Download/index.aspx?lang=cn
檢視支援顯示卡的驅動最新版本及下載，下載之後是.run字尾。然後上傳到伺服器任意位置即可
準備
禁用系統預設安裝的 nouveau 驅動
# 修改配置 echo -e "blacklist nouveau\noptions nouveau modeset=0" > /etc/modprobe.d/blacklist.conf # 備份原來的映象檔案 cp /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r).img.bak # 重建新映象檔案 sudo dracut --force # 重啟 reboot # 檢視nouveau是否啟動，如果結果為空即為禁用成功 lsmod | grep nouveau
安裝DKMS模組
DKMS全稱是DynamicKernel ModuleSupport，它可以幫我們維護核心外的驅動程式，在核心版本變動之後可以自動重新生成新的模組。
yum -y install dkms
安裝
執行以下命令進行安裝，檔名替換為自己的。
sudo sh NVIDIA-Linux-x86_64-410.129-diagnostic.run -no-x-check -no-nouveau-check -no-opengl-files
# -no-x-check #安裝驅動時關閉X服務 # -no-nouveau-check #安裝驅動時禁用nouveau # -no-opengl-files #只安裝驅動檔案，不安裝OpenGL檔案
按照安裝提示進行安裝，一路點yes、ok
安裝完之後輸入以下命令 ，顯示如下:
[root@localhost opt]# nvidia-smi Wed Jul 7 11:11:33 2021 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 410.129 Driver Version: 410.129 CUDA Version: 10.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla T4 Off | 00000000:41:00.0 Off | 0 | | N/A 94C P0 36W / 70W | 0MiB / 15079MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
安裝CUDA
安裝前檢查
1、確定已經安裝NVIDIA顯示卡
[root@localhost opt]# lspci | grep -i nvidia 41:00.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)
2、確認安裝gcc，如果沒有安裝需要安裝
[root@localhost opt]# gcc --version gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) Copyright © 2015 Free Software Foundation, Inc. 本程式是自由軟體；請參看原始碼的版權宣告。本軟體沒有任何擔保； 包括沒有適銷性和某一專用目的下的適用性擔保。 # yum -y install gcc gcc-c++
3、禁用Nouveau
# 沒有輸出就是已經禁用了Nouveau # 如果沒有禁用, 看文件上面的禁用Nouveau [root@localhost opt]# lsmod | grep nouveau [root@localhost opt]#
4、
[root@localhost opt]# systemctl set-default multi-user.target Removed symlink /etc/systemd/system/default.target. Created symlink from /etc/systemd/system/default.target to /usr/lib/systemd/system/multi-user.target.
安裝
此處的安裝環境為離線環境，需要先下載cuda安裝檔案，安裝檔案可以去官網地址下載對應的系統版本，官網下載地址：https://developer.nvidia.com/cuda-toolkit-archive
CUDA版本按照自己的需求選擇即可, 這裡我選擇的安裝型別為 runfile(local)
wget https://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run sudo sh cuda_10.1.243_418.87.00_linux.run
接著，會出現安裝介面，輸入accept，
第二個介面, 直接選擇install
安裝後腳本輸出, 臨時儲存一下, 後面需要:
=========== = Summary = =========== Driver: Installed Toolkit: Installed in /usr/local/cuda-10.1/ Samples: Installed in /root/, but missing recommended libraries Please make sure that - PATH includes /usr/local/cuda-10.1/bin - LD_LIBRARY_PATH includes /usr/local/cuda-10.1/lib64, or, add /usr/local/cuda-10.1/lib64 to /etc/ld.so.conf andrun ldconfig as root To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-10.1/bin To uninstall the NVIDIA Driver, run nvidia-uninstall Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-10.1/doc/pdf for detailed information on setting upCUDA. Logfile is /var/log/cuda-installer.log
新增CUDA進入環境變數
# 需要按照自己的cuda安裝指令碼輸出來更改 [root@localhost cuda-10.1]# tail -5 /etc/profile PATH=$PATH:/usr/local/cuda-10.1/bin/ LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/lib64/ export PATH export LD_LIBRARY_PATH [root@localhost cuda-10.1]# source /etc/profile
驗證
[root@localhost cuda-10.1]# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
[root@localhost NVIDIA_CUDA-10.1_Samples]# cd /root/NVIDIA_CUDA-10.1_Samples
[root@localhost NVIDIA_CUDA-10.1_Samples]# make
[root@localhost NVIDIA_CUDA-10.1_Samples]# cd 1_Utilities/deviceQuery
[root@localhost deviceQuery]# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla T4"
CUDA Driver Version / Runtime Version 10.1 / 10.1
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 15080 MBytes (15812263936 bytes)
(40) Multiprocessors, ( 64) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1590 MHz (1.59 GHz)
Memory Clock rate: 5001 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 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 number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
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 3 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): 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 / 65 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.1, NumDevs = 1
Result = PASS
主要關注 Result = PASS 代表測試通過
安裝cuDNN
下載
從官網上（https://developer.nvidia.com/rdp/cudnn-archive）下載相關版本的CUDNN（需要先註冊賬號才能下載）：
注意：要選擇CUDA相對應版本的。
安裝
上傳並解壓
[root@localhost opt]# cd /opt/ [root@localhost opt]# tar xzvf cudnn-10.1-linux-x64-v7.6.5.32.tgz cuda/include/cudnn.h cuda/NVIDIA_SLA_cuDNN_Support.txt cuda/lib64/libcudnn.so cuda/lib64/libcudnn.so.7 cuda/lib64/libcudnn.so.7.6.5 cuda/lib64/libcudnn_static.a [root@localhost opt]# cp cuda/include/cudnn.h /usr/local/cuda/include [root@localhost opt]# cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 [root@localhost opt]# chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* [root@localhost opt]# chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*