Ubuntu16.04安装Nvidia驱动cuda,cudnn和tensorflow-gpu

Posted 2022-10-30 莫失莫忘Lawlite

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之前有在阿里云GPU服务器上弄过：点击查看，这里从装Nvidia开始

一、安装Nvidia驱动

1.1 查找需要安装的Nvidia版本

1.1.1 官网

官网上查找： https://www.nvidia.com/Download/index.aspx?lang=en-us
- 这里是 GeForce GTX 1080 TI
- 如下图，推荐 410 版本的

15_GPU对应nvidia版本

16_GPU对应nvidia版本

1.1.2 命令行查看推荐驱动

查看驱动：ubuntu-drivers devices，如下图

ubuntu@ubuntu-System-Product-Name:~$ ubuntu-drivers devices
== cpu-microcode.py ==
driver   : intel-microcode - distro free

== /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
vendor   : NVIDIA Corporation
modalias : pci:v000010DEd00001B06sv00001458sd0000374Dbc03sc00i00
driver   : nvidia-410 - third-party free recommended
driver   : nvidia-384 - distro non-free
driver   : xserver-xorg-video-nouveau - distro free builtin
driver   : nvidia-390 - third-party free
driver   : nvidia-396 - third-party free

注意这里添加了ppa, 若是没有，可能最新的只有nvidia-384，但是若想安装cuda-9.0 需要大于384.81, 不然后面安装tensorflow-gpu 之后也会报错
- 图片对应网址：https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

14_cuda版本对应nvidia版本

添加 ppa:
- sudo add-apt-repository ppa:graphics-drivers/ppa （注意联网，去掉代理）
- sudo apt update
然后执行ubuntu-drivers devices就可以看到如上的结果
安装：
- 可能需要的依赖：sudo apt install dkms build-essential linux-headers-generic
- 有些可能需要禁用nouveau模块，查看：https://blog.csdn.net/u012235003/article/details/54575758
- sudo apt-get install linux-headers-$(uname -r)
- sudo apt install nvidia-410
- 重启机器
查看：
- nvidia-smi
- 显示如下结果

(wangyongzhi_ml) ubuntu@ubuntu-System-Product-Name:/usr/local/cuda-10.0/bin$ nvidia-smi
Thu Oct 25 15:49:46 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.66       Driver Version: 410.66       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  GeForce GTX 108...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   44C    P8    20W / 250W |     42MiB / 11174MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX 108...  Off  | 00000000:02:00.0 Off |                  N/A |
|  0%   50C    P8    20W / 250W |      2MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0       949      G   /usr/lib/xorg/Xorg                            39MiB |
+-----------------------------------------------------------------------------+

二、安装cuda

官网： https://developer.nvidia.com/cuda-toolkit-archive
选择想要安装的版本，这里选择的是cuda-9.0, 下载
安装
- chmod +x cuda_9.0.176_384.81_linux-run
- sudo ./cuda_9.0.176_384.81_linux-run
- 根据提示安装选择即可
- 添加环境变量
  - vim ~/.bashrc
  - 加入环境变量

# cuda9.0
export PATH=/usr/local/cuda-9.0/bin/:$PATH;
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64/:$LD_LIBRARY_PATH;

测试1
- nvcc -V
- 如下图，版本为V9.0.176

(wangyongzhi_ml) ubuntu@ubuntu-System-Product-Name:~/wangyongzhi/software$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176

测试2
- 如果上面安装过程中选择了安装Examples, 会在 ~ 文件夹下生成测试NVIDIA_CUDA-9.0_Samples 的文件
- 进入： cd NVIDIA_CUDA-9.0_Samples
- make
- 进入 NVIDIA_CUDA-9.0_Samples/bin/x86_64/linux/release 文件夹
  - 执行： ./deviceQuery, 可以看到类似如下信息

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 2 CUDA Capable device(s)

Device 0: "GeForce GTX 1080 Ti"
  CUDA Driver Version / Runtime Version          10.0 / 9.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 11174 MBytes (11717181440 bytes)
  (28) Multiprocessors, (128) CUDA Cores/MP:     3584 CUDA Cores
  GPU Max Clock rate:                            1683 MHz (1.68 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 number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024

三、安装cudnn

官网：https://developer.nvidia.com/rdp/cudnn-download
选择cuda对应的版本, 我的选择如下图

17_cudnn

安装
- tar -zxvf cudnn-9.0-linux-x64-v7.3.1.20.tgz
- 将解压得到的cuda 文件夹下的内容拷贝到对应的 /usr/local/cuda-9.0文件夹下即可

四、安装Anaconda和tensorflow-gpu

官网： https://www.anaconda.com/download/#linux
下载安装即可，我这里选择的是 python3.7 版本
安装之后添加到环境变量：

# anaconda3
export PATH=/home/ubuntu/anaconda3/bin:$PATH

创建虚拟环境，防止污染他人使用环境
- conda create -n xxx python-3.6
- conda install tensorflow-gpu
测试

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

打印如下信息：

2018-10-25 16:25:35.683507: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
totalMemory: 10.91GiB freeMemory: 10.72GiB
2018-10-25 16:25:35.783459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-10-25 16:25:35.783843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:02:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-10-25 16:25:35.784321: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2018-10-25 16:25:36.069610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-25 16:25:36.069634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971]      0 1
2018-10-25 16:25:36.069637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0:   N Y
2018-10-25 16:25:36.069639: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1:   Y N
2018-10-25 16:25:36.069852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10367 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-10-25 16:25:36.101498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10409 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
/job:localhost/replica:0/task:0/device:GPU:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1
2018-10-25 16:25:36.134430: I tensorflow/core/common_runtime/direct_session.cc:288] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
/job:localhost/replica:0/task:0/device:GPU:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1

五、多个cuda版本切换

安装cuda-9.0 会在 /usr/local/ 目录下
- 如下图，它会创建一个软连接指向了 /usr/local/cuda-9.0/

(wangyongzhi_ml) ubuntu@ubuntu-System-Product-Name:/usr/local$ ll
总用量 48
drwxr-xr-x 12 root root 4096 10月 25 14:51 ./
drwxr-xr-x 13 root root 4096 10月 25 09:39 ../
drwxr-xr-x  2 root root 4096 4月  21  2016 bin/
lrwxrwxrwx  1 root root   19 10月 25 00:41 cuda -> /usr/local/cuda-9.0/
drwxr-xr-x 19 root root 4096 10月 25 14:52 cuda-10.0/
drwxr-xr-x 18 root root 4096 10月 25 00:41 cuda-9.0/
drwxr-xr-x  2 root root 4096 4月  21  2016 etc/
drwxr-xr-x  2 root root 4096 4月  21  2016 games/
drwxr-xr-x  2 root root 4096 4月  21  2016 include/
drwxr-xr-x  4 root root 4096 4月  21  2016 lib/
lrwxrwxrwx  1 root root    9 10月 24 14:52 man -> share/man/
drwxr-xr-x  2 root root 4096 4月  21  2016 sbin/
drwxr-xr-x  8 root root 4096 4月  21  2016 share/
drwxr-xr-x  2 root root 4096 4月  21  2016 src/

所以正常安装cuda 其他版本，然后创建软连接指向对应的版本即可

sudo rm -rf cuda
sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda

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Ubuntu16.04安装Nvidia驱动cuda,cudnn和tensorflow-gpu

一、 安装Nvidia驱动

1.1 查找需要安装的Nvidia版本

1.1.1 官网

1.1.2 命令行查看推荐驱动

二、安装cuda

三、安装cudnn

四、安装Anaconda和tensorflow-gpu

五、 多个cuda版本切换

Reference

一、安装Nvidia驱动

五、多个cuda版本切换