亲宝软件园·资讯

展开

Ubuntu安装和卸载CUDA和CUDNN Ubuntu安装和卸载CUDA和CUDNN的实现

夜雨飘零1 人气:0
想了解Ubuntu安装和卸载CUDA和CUDNN的实现的相关内容吗,夜雨飘零1在本文为您仔细讲解Ubuntu安装和卸载CUDA和CUDNN的相关知识和一些Code实例,欢迎阅读和指正,我们先划重点:Ubuntu安装卸载CUDA,Ubuntu安装卸载CUDNN,下面大家一起来学习吧。

前言

最近在学习PaddlePaddle在各个显卡驱动版本的安装和使用,所以同时也学习如何在Ubuntu安装和卸载CUDA和CUDNN,在学习过程中,顺便记录学习过程。在供大家学习的同时,也在加强自己的记忆。本文章以卸载CUDA 8.0 和 CUDNN 7.05 为例,以安装CUDA 10.0 和 CUDNN 7.4.2 为例。

安装显卡驱动

禁用nouveau驱动

sudo vim /etc/modprobe.d/blacklist.conf

在文本最后添加:

blacklist nouveau
options nouveau modeset=0

然后执行:

sudo update-initramfs -u

重启后,执行以下命令,如果没有屏幕输出,说明禁用nouveau成功:

lsmod | grep nouveau

下载驱动

官网下载地址:https://www.nvidia.cn/Download/index.aspx?lang=cn ,根据自己显卡的情况下载对应版本的显卡驱动,比如笔者的显卡是RTX2070:

下载完成之后会得到一个安装包,不同版本文件名可能不一样:

NVIDIA-Linux-x86_64-410.93.run

卸载旧驱动

以下操作都需要在命令界面操作,执行以下快捷键进入命令界面,并登录:

Ctrl-Alt+F1

执行以下命令禁用X-Window服务,否则无法安装显卡驱动:

sudo service lightdm stop

执行以下三条命令卸载原有显卡驱动:

sudo apt-get remove --purge nvidia*
sudo chmod +x NVIDIA-Linux-x86_64-410.93.run
sudo ./NVIDIA-Linux-x86_64-410.93.run --uninstall

安装新驱动

直接执行驱动文件即可安装新驱动,一直默认即可:

sudo ./NVIDIA-Linux-x86_64-410.93.run

执行以下命令启动X-Window服务

sudo service lightdm start

最后执行重启命令,重启系统即可:

reboot

注意: 如果系统重启之后出现重复登录的情况,多数情况下都是安装了错误版本的显卡驱动。需要下载对应本身机器安装的显卡版本。

卸载CUDA

为什么一开始我就要卸载CUDA呢,这是因为笔者是换了显卡RTX2070,原本就安装了CUDA 8.0 和 CUDNN 7.0.5不能够正常使用,笔者需要安装CUDA 10.0 和 CUDNN 7.4.2,所以要先卸载原来的CUDA。注意以下的命令都是在root用户下操作的。

卸载CUDA很简单,一条命令就可以了,主要执行的是CUDA自带的卸载脚本,读者要根据自己的cuda版本找到卸载脚本:

sudo /usr/local/cuda-8.0/bin/uninstall_cuda_8.0.pl

卸载之后,还有一些残留的文件夹,之前安装的是CUDA 8.0。可以一并删除:

sudo rm -rf /usr/local/cuda-8.0/

这样就算卸载完了CUDA。

安装CUDA

安装的CUDA和CUDNN版本:

接下来的安装步骤都是在root用户下操作的。

下载和安装CUDA

我们可以在官网:CUDA10下载页面
下载符合自己系统版本的CUDA。页面如下:

下载完成之后,给文件赋予执行权限:

chmod +x cuda_10.0.130_410.48_linux.run

执行安装包,开始安装:

./cuda_10.0.130_410.48_linux.run

开始安装之后,需要阅读说明,可以使用Ctrl + C直接阅读完成,或者使用空格键慢慢阅读。然后进行配置,我这里说明一下:

(是否同意条款,必须同意才能继续安装)
accept/decline/quit: accept

(这里不要安装驱动,因为已经安装最新的驱动了,否则可能会安装旧版本的显卡驱动,导致重复登录的情况)
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n

Install the CUDA 10.0 Toolkit?(是否安装CUDA 10 ,这里必须要安装)
(y)es/(n)o/(q)uit: y

Enter Toolkit Location(安装路径,使用默认,直接回车就行)
 [ default is /usr/local/cuda-10.0 ]: 

Do you want to install a symbolic link at /usr/local/cuda?(同意创建软链接)
(y)es/(n)o/(q)uit: y

Install the CUDA 10.0 Samples?(不用安装测试,本身就有了)
(y)es/(n)o/(q)uit: n

Installing the CUDA Toolkit in /usr/local/cuda-10.0 ...(开始安装)

安装完成之后,可以配置他们的环境变量,在vim ~/.bashrc的最后加上以下配置信息:

export CUDA_HOME=/usr/local/cuda-10.0
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
export PATH=${CUDA_HOME}/bin:${PATH}

最后使用命令source ~/.bashrc使它生效。

可以使用命令nvcc -V查看安装的版本信息:

test@test:~$ 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

测试安装是否成功

执行以下几条命令:

cd /usr/local/cuda-10.0/samples/1_Utilities/deviceQuery
make
./deviceQuery

正常情况下输出:

./deviceQuery Starting...

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

Detected 1 CUDA Capable device(s)

Device 0: "GeForce RTX 2070"
 CUDA Driver Version / Runtime Version   10.0 / 10.0
 CUDA Capability Major/Minor version number: 7.5
 Total amount of global memory:     7950 MBytes (8335982592 bytes)
 (36) Multiprocessors, ( 64) CUDA Cores/MP:  2304 CUDA Cores
 GPU Max Clock rate:       1620 MHz (1.62 GHz)
 Memory Clock rate:        7001 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:      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 = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS

下载和安装CUDNN

进入到CUDNN的下载官网:https://developer.nvidia.com/rdp/cudnn-download ,然点击Download开始选择下载版本,当然在下载之前还有登录,选择版本界面如下,我们选择cuDNN Library for Linux

下载之后是一个压缩包,如下:

cudnn-10.0-linux-x64-v7.4.2.24.tgz 

然后对它进行解压,命令如下:

tar -zxvf cudnn-10.0-linux-x64-v7.4.2.24.tgz 

解压之后可以得到以下文件:

cuda/include/cudnn.h
cuda/NVIDIA_SLA_cuDNN_Support.txt
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.7
cuda/lib64/libcudnn.so.7.4.2
cuda/lib64/libcudnn_static.a

使用以下两条命令复制这些文件到CUDA目录下:

cp cuda/lib64/* /usr/local/cuda-10.0/lib64/
cp cuda/include/* /usr/local/cuda-10.0/include/

拷贝完成之后,可以使用以下命令查看CUDNN的版本信息:

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

测试安装结果

到这里就已经完成了CUDA 10 和 CUDNN 7.4.2 的安装。可以安装对应的Pytorch的GPU版本测试是否可以正常使用了。安装如下:

pip3 install https://download.pytorch.org/whl/cu100/torch-1.0.0-cp35-cp35m-linux_x86_64.whl
pip3 install torchvision

然后使用以下的程序测试安装情况:

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms


class Net(nn.Module):
 def __init__(self):
  super(Net, self).__init__()
  self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
  self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
  self.conv2_drop = nn.Dropout2d()
  self.fc1 = nn.Linear(320, 50)
  self.fc2 = nn.Linear(50, 10)

 def forward(self, x):
  x = F.relu(F.max_pool2d(self.conv1(x), 2))
  x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
  x = x.view(-1, 320)
  x = F.relu(self.fc1(x))
  x = F.dropout(x, training=self.training)
  x = self.fc2(x)
  return F.log_softmax(x, dim=1)


def train(model, device, train_loader, optimizer, epoch):
 model.train()
 for batch_idx, (data, target) in enumerate(train_loader):
  data, target = data.to(device), target.to(device)
  optimizer.zero_grad()
  output = model(data)
  loss = F.nll_loss(output, target)
  loss.backward()
  optimizer.step()
  if batch_idx % 10 == 0:
   print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
    epoch, batch_idx * len(data), len(train_loader.dataset),
      100. * batch_idx / len(train_loader), loss.item()))

def main():
 cudnn.benchmark = True
 torch.manual_seed(1)
 device = torch.device("cuda")
 kwargs = {'num_workers': 1, 'pin_memory': True}
 train_loader = torch.utils.data.DataLoader(
  datasets.MNIST('../data', train=True, download=True,
      transform=transforms.Compose([
       transforms.ToTensor(),
       transforms.Normalize((0.1307,), (0.3081,))
      ])),
  batch_size=64, shuffle=True, **kwargs)

 model = Net().to(device)
 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

 for epoch in range(1, 11):
  train(model, device, train_loader, optimizer, epoch)


if __name__ == '__main__':
 main()

如果正常输出一下以下信息,证明已经安装成了:

Train Epoch: 1 [0/60000 (0%)] Loss: 2.365850
Train Epoch: 1 [640/60000 (1%)] Loss: 2.305295
Train Epoch: 1 [1280/60000 (2%)] Loss: 2.301407
Train Epoch: 1 [1920/60000 (3%)] Loss: 2.316538
Train Epoch: 1 [2560/60000 (4%)] Loss: 2.255809
Train Epoch: 1 [3200/60000 (5%)] Loss: 2.224511
Train Epoch: 1 [3840/60000 (6%)] Loss: 2.216569
Train Epoch: 1 [4480/60000 (7%)] Loss: 2.181396

参考资料

https://developer.nvidia.com

https://www.cnblogs.com/luofeel/p/8654964.html

加载全部内容

相关教程
猜你喜欢
用户评论