Implementation of installing and uninstalling CUDA and CUDNN in Ubuntu

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

blacklist nouveau
options nouveau modeset=0

sudo update-initramfs -u

lsmod | grep nouveau

NVIDIA-Linux-x86_64-410.93.run

Ctrl-Alt+F1

sudo service lightdm stop

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

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

sudo service lightdm start

reboot

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

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

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

./cuda_10.0.130_410.48_linux.run

(Do you agree to the terms? You must agree to continue the installation)
accept/decline/quit: accept

(Do not install the driver here, because the latest driver has been installed. Otherwise, an old version of the graphics driver may be installed, resulting in repeated logins)
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
(y)es/(n)o/(q)uit: n

Install the CUDA 10.0 Toolkit? (Do you want to install CUDA 10? This must be installed.)
(y)es/(n)o/(q)uit: y

Enter Toolkit Location (installation path, use the default, just press Enter)
 [ default is /usr/local/cuda-10.0 ]: 

Do you want to install a symbolic link at /usr/local/cuda? (Agree to create a soft link)
(y)es/(n)o/(q)uit: y

Install the CUDA 10.0 Samples? (No need to install the test, it is already there)
(y)es/(n)o/(q)uit: n

Installing the CUDA Toolkit in /usr/local/cuda-10.0 ...

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

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

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

./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

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

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

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

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

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

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

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()