Linux: cannot read file data

报错：
Could not load library libcudnn_cnn_infer.so.8. Error: /home/qc/miniconda3/envs/DNAqc/lib/python3.10/site-packages/torch/lib/libcudnn_cnn_infer.so.8: cannot read file data
Please make sure libcudnn_cnn_infer.so.8 is in your library path!
Aborted (core dumped)

原因：libcudnn_cnn_infer.so.8这个文件有问题，找别人要一份
致谢：网上搜索，报错内容都是：Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory。试了删掉pytorch重装，试了新建一个不同python版本的虚拟环境，不对不对，全都不对，后来去搜“cannot read file data”，众多回答中看到这句话，当时心想这库是从官网下的，别人都没事，我的肯定也没问题，但是到最后各种搜索和尝试都无果，决定尝试，终于成了！感谢这位大哥，也感谢传我能用文件的朋友！

还有一位，成功之后去翻历史记录，没找到他的网站。他给了一段简单的代码，验证cuda和cudnn是否能用，现在把这段代码发在这里。

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") if torch.cuda.is_available() else torch.device("cpu")
    print("Using device: {}".format(device))
    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()

感谢！