ubuntu16.04部署GPU环境-创新互联
参考文档
https://blog.csdn.net/nwpushuai/article/details/79935740
https://blog.csdn.net/qq_43030766/article/details/91513501
https://blog.csdn.net/zhqh200/article/details/77646497
https://www.cnblogs.com/zixuan-L/p/11023051.html
https://blog.csdn.net/huangfei711/article/details/79230446
https://www.cnblogs.com/yjlch2016/p/8641910.html创新互联建站 - 四川主机托管，四川服务器租用，成都服务器租用，四川网通托管,绵阳服务器托管，德阳服务器托管，遂宁服务器托管，绵阳服务器托管，四川云主机，成都云主机，西南云主机，四川主机托管，西南服务器托管，四川/成都大带宽，服务器机柜，四川老牌ＩＤＣ服务商
硬件环境
CPU I7-7700,8M,3.6GHZ,4核
内存 DDR4 16G
硬盘 SSD 500G
系统 Ubuntu 16.04 Desktop版（需要用到图像界面）
显卡 NVDIA GeForce GTX1050Ti 4G系统环境
1.双网卡绑定
root@mec03:~# cat /etc/modules
# /etc/modules: kernel modules to load at boot time.
#
# This file contains the names of kernel modules that should be loaded
# at boot time, one per line. Lines beginning with "#" are ignored.
bonding mode=0 miimon=100
root@mec03:/etc/network# cat /etc/network/interfaces
auto bond0
iface bond0 inet static
address 172.30.10.249
netmask 255.255.255.0
gateway 172.30.10.254
post-up ifenslave bond0 enp2s0 enp3s0
pre-down ifenslave -d bond0 enp2s0 enp3s0
开机启动放在rc.local里面
root@mec03:/etc/network# modprobe bonding
关闭网卡管理会与bonding冲突
root@mec03:/etc/network# systemctl disable network-manager.service2.设置apt-list源
root@mec03:~# cat /etc/apt/sources.list
deb http://mirrors.163.com/ubuntu/ xenial main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ xenial-security main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ xenial-updates main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ xenial-proposed main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ xenial-backports main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ xenial main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ xenial-security main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ xenial-updates main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ xenial-proposed main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ xenial-backports main restricted universe multiverse3.默认语言设置
root@mec03:~# cat /etc/default/locale
# File generated by update-locale
# LANG="zh_CN.UTF-8"
# LANGUAGE="zh_CN:zh"
LANG="en_US.UTF-8"
LANGUAGE="en_US:en"二、安装Nvidia GTX 1050TI驱动
1.禁用系统默认自带nvidia驱动
root@mec03:~# lsmod | grep nouveau
nouveau 1724416 1
mxm_wmi 16384 1 nouveau
wmi 24576 2 mxm_wmi,nouveau
i2c_algo_bit 16384 1 nouveau
ttm 106496 1 nouveau
drm_kms_helper 172032 1 nouveau
drm 401408 4 drm_kms_helper,ttm,nouveau
video 45056 1 nouveau2.禁用模块
root@mec03:~# vim /etc/modprobe.d/blacklist.conf
在文件末尾添加如下几行：
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist rivatv
blacklist nvidiafb3.更新内核
root@mec03:~# update-initramfs -u
update-initramfs: Generating /boot/initrd.img-4.15.0-45-generic4.重启
root@mec03:~# reboot5.上传cudnn_cudn.zip包
root@mec03:~# rz
root@mec03:~# ls
cudnn_cuda cudnn_cuda.zip
root@mec03:~# cd cudnn_cuda/
root@mec03:~/cudnn_cuda# ls
cuda_10.0.130.1_linux.run libcudnn7-dev_7.6.3.30-1+cuda10.0_amd64.deb
cuda_10.0.130_410.48_linux.run libcudnn7-doc_7.6.3.30-1+cuda10.0_amd64.deb
libcudnn7_7.6.3.30-1+cuda10.0_amd64.deb NVIDIA-Linux-x86_64-435.21.run6.安装驱动
root@mec03:~/cudnn_cuda# systemctl stop lightdm.service
root@mec03:~/cudnn_cuda# sh NVIDIA-Linux-x86_64-435.21.run
Verifying archive integrity... OK
Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 435.21........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
root@mec03:~/cudnn_cuda# lsmod | grep nvi
nvidia_drm 45056 0
nvidia_modeset 1118208 1 nvidia_drm
nvidia 19472384 1 nvidia_modeset
drm_kms_helper 172032 1 nvidia_drm
drm 401408 3 drm_kms_helper,nvidia_drm
ipmi_msghandler 53248 2 ipmi_devintf,nvidia三.安装cuda 10.1
root@mec03:~/cudnn_cuda# sh cuda_10.0.130_410.48_linux.run
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n
Install the CUDA 10.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-10.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 10.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /root ]:
Installing the CUDA Toolkit in /usr/local/cuda-10.0 ...
Installing the CUDA Toolkit in /usr/local/cuda-10.0 ...
Missing recommended library: libGLU.so
Missing recommended library: libX11.so
Missing recommended library: libXi.so
Missing recommended library: libXmu.so
Installing the CUDA Samples in /root ...
Copying samples to /root/NVIDIA_CUDA-10.0_Samples now...
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-10.0
Samples: Installed in /root, but missing recommended libraries
Please make sure that
- PATH includes /usr/local/cuda-10.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-10.0/lib64, or, add /usr/local/cuda-10.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-10.0/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-10.0/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 384.00 is required for CUDA 10.0 functionality to work.
To install the driver using this installer, run the following command, replacing with the name of this run file:
sudo .run -silent -driver
Logfile is /tmp/cuda_install_9752.log
root@mec03:~/cudnn_cuda# vim /etc/ld.so.conf
root@mec03:~/cudnn_cuda# ldconfig
root@mec03:~# cat /etc/profile
export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda
root@mec03:~# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130 四.安装cuDNN 7.6
root@mec03:~/cudnn_cuda# dpkg -i libcudnn7_7.6.3.30-1+cuda10.0_amd64.deb
Selecting previously unselected package libcudnn7.
(Reading database ... 184057 files and directories currently installed.)
Preparing to unpack libcudnn7_7.6.3.30-1+cuda10.0_amd64.deb ...
Unpacking libcudnn7 (7.6.3.30-1+cuda10.0) ...
Setting up libcudnn7 (7.6.3.30-1+cuda10.0) ...
Processing triggers for libc-bin (2.23-0ubuntu11) ...
root@mec03:~/cudnn_cuda# dpkg -i libcudnn7-dev_7.6.3.30-1+cuda10.0_amd64.deb
Selecting previously unselected package libcudnn7-dev.
(Reading database ... 184063 files and directories currently installed.)
Preparing to unpack libcudnn7-dev_7.6.3.30-1+cuda10.0_amd64.deb ...
Unpacking libcudnn7-dev (7.6.3.30-1+cuda10.0) ...
Setting up libcudnn7-dev (7.6.3.30-1+cuda10.0) ...
update-alternatives: using /usr/include/x86_64-linux-gnu/cudnn_v7.h to provide /usr/include/cudnn.h (libcudnn) in auto mode
root@mec03:~/cudnn_cuda# dpkg -i libcudnn7-doc_7.6.3.30-1+cuda10.0_amd64.deb
Selecting previously unselected package libcudnn7-doc.
(Reading database ... 184069 files and directories currently installed.)
Preparing to unpack libcudnn7-doc_7.6.3.30-1+cuda10.0_amd64.deb ...
Unpacking libcudnn7-doc (7.6.3.30-1+cuda10.0) ...
Setting up libcudnn7-doc (7.6.3.30-1+cuda10.0) ...
root@mec03:~/cudnn_cuda# cp /usr/include/cudnn.h /usr/local/cuda/include
root@mec03:~/cudnn_cuda# cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 3
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#include "driver_types.h"五.测试GPU效果
1.安装python3.6
root@mec03:~# add-apt-repository ppa:jonathonf/python-3.6
A plain backport of *just* Python 3.6. System extensions/Python libraries may or may not work.
Don't remove Python 3.5 from your system - it will break.
More info: https://launchpad.net/~jonathonf/+archive/ubuntu/python-3.6
Press [ENTER] to continue or ctrl-c to cancel adding it
gpg: keyring `/tmp/tmpec5st1dk/secring.gpg' created
gpg: keyring `/tmp/tmpec5st1dk/pubring.gpg' created
gpg: requesting key F06FC659 from hkp server keyserver.ubuntu.com
gpg: /tmp/tmpec5st1dk/trustdb.gpg: trustdb created
gpg: key F06FC659: public key "Launchpad PPA for J Fernyhough" imported
gpg: Total number processed: 1
gpg: imported: 1 (RSA: 1)
OK
root@mec03:~# update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.5 1
update-alternatives: using /usr/bin/python3.5 to provide /usr/bin/python3 (python3) in auto mode
root@mec03:~# update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 2
update-alternatives: using /usr/bin/python3.6 to provide /usr/bin/python3 (python3) in auto mode
root@mec03:~# update-alternatives --install /usr/bin/python python /usr/bin/python2 100
update-alternatives: using /usr/bin/python2 to provide /usr/bin/python (python) in auto mode
root@mec03:~# update-alternatives --install /usr/bin/python python /usr/bin/python3 150
update-alternatives: using /usr/bin/python3 to provide /usr/bin/python (python) in auto mode
root@mec03:~# python3
Python 3.6.8 (default, May 7 2019, 14:58:50)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>2.安装pip3
root@mec03:~# apt install python3-pip3.安装tensorflow
root@mec03:~# pip3 install tensorflow-gpu==1.13.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
Collecting tensorflow-gpu==1.13.14.测试gpu
测试python语句
import numpy
import tensorflow as tf
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
root@mec03:~# python3
Python 3.6.8 (default, May 7 2019, 14:58:50)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
ement=True))
print(sess.run(c))>>> import tensorflow as tf
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
>>> a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
>>> b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
>>> c = tf.matmul(a, b)
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2019-09-14 12:27:18.309361: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-14 12:27:18.360212: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-09-14 12:27:18.360498: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3bb3a20 executing computations on platform CUDA. Devices:
2019-09-14 12:27:18.360512: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2019-09-14 12:27:18.379184: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2019-09-14 12:27:18.380446: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3ccb2f0 executing computations on platform Host. Devices:
2019-09-14 12:27:18.380503: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): ,
2019-09-14 12:27:18.380792: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 3.66GiB
2019-09-14 12:27:18.380852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-14 12:27:18.382037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-14 12:27:18.382075: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-14 12:27:18.382090: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-14 12:27:18.382242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3452 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
2019-09-14 12:27:18.384493: I tensorflow/core/common_runtime/direct_session.cc:317] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
>>> print(sess.run(c))
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
2019-09-14 12:27:20.118473: I tensorflow/core/common_runtime/placer.cc:1059] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0
a: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-09-14 12:27:20.118492: I tensorflow/core/common_runtime/placer.cc:1059] a: (Const)/job:localhost/replica:0/task:0/device:GPU:0
b: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-09-14 12:27:20.118502: I tensorflow/core/common_runtime/placer.cc:1059] b: (Const)/job:localhost/replica:0/task:0/device:GPU:0
[[22. 28.]
[49. 64.]]
>>> 5.查看GPU使用情况
root@mec03:~# nvidia-smi
Fri Sep 6 19:42:42 2019
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 9558 C python3 3865MiB |
| 0 12510 G /usr/lib/xorg/Xorg 39MiB |
| 0 12608 G gnome-shell 38MiB |
+-----------------------------------------------------------------------------+
Fri Sep 6 00:22:27 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 435.21 Driver Version: 435.21 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 105... Off | 00000000:01:00.0 On | N/A |
| 31% 62C P0 N/A / 80W | 3955MiB / 4038MiB | 97% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 9558 C python3 3865MiB |
| 0 12510 G /usr/lib/xorg/Xorg 39MiB |
| 0 12608 G gnome-shell 38MiB |
+-----------------------------------------------------------------------------+另外有需要云服务器可以了解下创新互联cdcxhl.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。
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