Ubuntu 18.04 + Python3.6 + CUDA-10.0 + CUDNN-7.5.0 + tensorflow-gpu-1.13.1
转自：https://blog.csdn.net/ycfn97/article/details/100084641
Install nvidia driver
首先卸载原有驱动
sudo apt-get purge nvidia
添加图形驱动程序到源列表:
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt upgrade
检查将安装什么驱动程序:
ubuntu-drivers devices
root@boss-To-be-filled-by-O-E-M:/home/boss# ubuntu-drivers devices
== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
modalias : pci:v000010DEd00001E04sv00007377sd00001730bc03sc00i00
vendor : NVIDIA Corporation
driver : nvidia-driver-430 - third-party free recommended
driver : nvidia-driver-410 - third-party free
driver : nvidia-driver-415 - third-party free
driver : xserver-xorg-video-nouveau - distro free builtin
注意！！！虚拟机无法安装NVIDIA显卡驱动程序，虚拟机中只有虚拟显卡没有NVIDIA显卡，不能安装NVIDIA驱动，无论是在NVIDIA官网下载NVIDIA-Linux-x86_64-430.40.run还是ppa下载都是没用的，也无法安装TensorFlow-gpu，虚拟机只能用cpu版本的TensorFlow。如果是考虑“虚拟一个系统”来使用显卡的话，考虑容器化技术——Docker大家可以参考这位大佬的文章虚拟机上无法安装tensorflow-GPU版本
虚拟机驱动程序是VM，，，
== /sys/devices/pci0000:00/0000:00:0f.0 ==
modalias : pci:v000015ADd00000405sv000015ADsd00000405bc03sc00i00
vendor : VMware
model : SVGA II Adapter
driver : open-vm-tools-desktop - distro free
自动安装最新的驱动程序
sudo ubuntu-drivers autoinstall
显示
This system doesn't support Secure Boot
Secure Boot not enabled on this system.
Done.
nvidia:
Running module version sanity check.
- Original module
- No original module exists within this kernel
- Installation
- Installing to /lib/modules/5.0.0-25-generic/updates/dkms/
nvidia-modeset.ko:
Running module version sanity check.
- Original module
- No original module exists within this kernel
- Installation
- Installing to /lib/modules/5.0.0-25-generic/updates/dkms/
nvidia-drm.ko:
Running module version sanity check.
- Original module
- No original module exists within this kernel
- Installation
- Installing to /lib/modules/5.0.0-25-generic/updates/dkms/
nvidia-uvm.ko:
Running module version sanity check.
- Original module
- No original module exists within this kernel
- Installation
- Installing to /lib/modules/5.0.0-25-generic/updates/dkms/
depmod...
DKMS: install completed.
表示安装成功
然后重启机器:
sudo reboot
检查正确的安装驱动程序:
nvidia-smi
显示 说明NVIDIA驱动安装成功
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/bandwidthTest# nvidia-smi
Tue Aug 27 20:03:00 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.40 Driver Version: 430.40 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 RTX 208... Off | 00000000:01:00.0 On | N/A |
| 26% 32C P8 9W / 250W | 302MiB / 11016MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1135 G /usr/lib/xorg/Xorg 16MiB |
| 0 1186 G /usr/bin/gnome-shell 58MiB |
| 0 1422 G /usr/lib/xorg/Xorg 98MiB |
| 0 1566 G /usr/bin/gnome-shell 127MiB |
+-----------------------------------------------------------------------------+
这里也可以下载并安装 Cuda 和 Nvidia 驱动一起执行，分开安装的筒子们请略过下面的部分直接跳到 install cuda&cudnn部分
卸载所有原驱动
$ sudo apt-get purge nvidia*
禁用nouveau
新建-blacklist-nouveau.conf 输⼊指令:
$ sudo vim /etc/modprobe.d/blacklist-nouveau.conf
往 blacklist-nouveau.conf 文件中写⼊：
blacklist nouveau
options nouveau modeset=0
禁 Ubuntu 自带开源驱动nouveau，写入后保存重启
$ sudo reboot
重启后在终端执行行命令:
$ lsmod | grep nouveau
查看nouveau模块是否被加载，若无输出，则执行下一步
禁⽤X服务
$ sudo service lightdm stop
注意！！！在Ubuntu禁用X进入tty1终端后登陆用户输入密码时不能用小键盘，系统不识别，别问我怎么知道的 ）逃…
确定下载的 cuda 版本后执行指令
$ sudo chmod 777 cuda_9.0.176_384.81_linux.run
$ sudo ./cuda_9.0.176_384.81_linux.run --no-opengl-libs
进入 CUDA 安装中同意安装 Nvidia 驱动
[accept] #同意安装
[y] #安装Driver，将自动安装CUDA版本相匹配的Nvidia驱动
[y] #安装CUDA Toolkit install
#安装到默认目录
[y] #创建安装目录的软链接
[n] #不复制Samples，因为在安装目录下有/samples
安装完成后会显示 CUDA 和 Nvidia 驱动成功安装
后面vim 打开.bashrc 在末行加⼊命令详见install cuda&cudnn部分
Install cuda&cudnn
cd 下载目录
sudo sh cuda_10.0.130_410.48_linux.run --override --silent --toolkit
cd 下载目录
tar -xzvf cudnn-10.0-linux-x64-v7.5.0.56.solitairetheme8
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
现在必须为/.bashrc添加一些路径:
gedit ~/.bashrc
在/.bashrc后面加上
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda
现在重新加载你的终端配置:
source ~/.bashrc
sudo ldconfig
检查路径是否正确安装:
echo $CUDA_HOME
测试版本号
查看 CUDA 版本：
cat /usr/local/cuda/version.txt
查看 CUDNN 版本：
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
也可以这样测试cuda安装情况
编译并测试设备 deviceQuery:
root@boss-To-be-filled-by-O-E-M:/home/boss/download# nvcc -V
输出如下
root@boss-To-be-filled-by-O-E-M:/home/boss/download# nvcc -V
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
编译并测试带宽 bandwidthTest:
$ cd /usr/local/cuda-9.0/samples/1_Utilities/deviceQuery
$ sudo make
$ ./deviceQuery
输出如下
root@boss-To-be-filled-by-O-E-M:/home/boss/download# cd /usr/local/cuda-10.0/samples/1_Utilities/deviceQuery
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/deviceQuery# sudo make
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_30,code=sm_30 -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_75,code=compute_75 -o deviceQuery.o -c deviceQuery.cpp
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_30,code=sm_30 -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_75,code=compute_75 -o deviceQuery deviceQuery.o
mkdir -p ../../bin/x86_64/linux/release
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/deviceQuery# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce RTX 2080 Ti"
CUDA Driver Version / Runtime Version 10.1 / 10.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 11017 MBytes (11552096256 bytes)
(68) Multiprocessors, ( 64) CUDA Cores/MP: 4352 CUDA Cores
GPU Max Clock rate: 1545 MHz (1.54 GHz)
Memory Clock rate: 7000 Mhz
Memory Bus Width: 352-bit
L2 Cache Size: 5767168 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: 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 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 = 10.1, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS
编译并测试带宽 bandwidthTest:
$ cd ../bandwidthTest
$ sudo make
$ ./bandwidthTest
输出如下
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/deviceQuery# cd ../bandwidthTest
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/bandwidthTest# sudo make
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_30,code=sm_30 -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_75,code=compute_75 -o bandwidthTest.o -c bandwidthTest.cu
"/usr/local/cuda-10.0"/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_30,code=sm_30 -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_75,code=compute_75 -o bandwidthTest bandwidthTest.o
mkdir -p ../../bin/x86_64/linux/release
cp bandwidthTest ../../bin/x86_64/linux/release
root@boss-To-be-filled-by-O-E-M:/usr/local/cuda-10.0/samples/1_Utilities/bandwidthTest# ./bandwidthTest
[CUDA Bandwidth Test] - Starting...
Running on...
Device 0: GeForce RTX 2080 Ti
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12162.1
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12451.7
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 513231.8
Result = PASS
如果两个测试的结果都是 Result = PASS CUDA ，说明安装成功
安装TensorFlow-gpu
这里我选择了清华大学的开源镜像以提高下载速度，提示出现pip not found ，不要直接复制提示安装pip，要根据Python版本下载你想要的pip，否则会下载Python2.7
root@sunqi-To-be-filled-by-O-E-M:/# pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pip -U
Command 'pip' not found, but can be installed with:
apt install python-pip
root@sunqi-To-be-filled-by-O-E-M:/# apt install python-pip
直接复制提示就会变成下面这样要修改Python默认版本，不要这样
root@sunqi-To-be-filled-by-O-E-M:/# pip -V
pip 9.0.1 from /usr/lib/python2.7/dist-packages (python 2.7)
修改Python默认版本
详见我的另一篇博客linux修改Python默认版本
修改完Python默认版本如果还不改，那么依然会是Python2.7的pip
root@sunqi-To-be-filled-by-O-E-M:/# apt install python-pip
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
python-pip 已经是最新版 (9.0.1-2.3~ubuntu1.18.04.1)。
pip -V还是no module named pip
root@sunqi-To-be-filled-by-O-E-M:/# pip -V
Traceback (most recent call last):
File "/usr/bin/pip", line 9, in <module>
from pip import main
ModuleNotFoundError: No module named 'pip'
必须这样安装
root@sunqi-To-be-filled-by-O-E-M:/# apt install python3-pip
然后再查看pip版本是否符合要求
pip -v
如果pip -v出现报错：bash: /usr/bin/pip: 没有那个文件或目录 可参考pip -v报错：bash: /usr/bin/pip: 没有那个文件或目录
更新pip
sudo pip install --upgrade pip
安装Tensorflow-gpu
pip install --user tensorflow-gpu
这里还有一种方法用bazel编译，需要下载一些依赖库，就不赘述了，感性趣的筒子们自行百度
在此，TensorFlow-gpu环境就已经安装完成，下面进行测试
import tensorflow as tf
hello=tf.constant("Hello,Tensorflow!")
sees=tf.Session()
print(sees.run(hello))
如果得到了输出hello，TensorFlow！那么恭喜你完成了DeepLearning基本环境的配置
如果出错，请检查NVIDIA驱动版本和cuda以及cudnn版本是否正确匹配
常见问题
I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
详见cuda程序执行出错: libcudart.so.10.0: cannot open shared object file: No such file or directory
听说看完了会点赞的都是美女帅哥
作者：楚千羽
出处：https://www.cnblogs.com/chuqianyu/
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