写一段代码，判断gpu是否可用

以下是判断GPU是否可用的代码示例：

#include <stdio.h>
#include <cuda_runtime.h>

int main() {
int deviceCount;
cudaError_t error = cudaGetDeviceCount(&deviceCount);
if (error != cudaSuccess) {
printf("CUDA error: %s\n", cudaGetErrorString(error));
return 1;
}
if (deviceCount == ) {
printf("There is no available GPU device.\n");
return 1;
}
printf("There are %d available GPU devices.\n", deviceCount);
return ;
}

相关问题

用python写一段代码，判断gpu是否可用

您好，我是 CSDN 开发的 C 知道，以下是回答您的问题：

您可以使用 Python 中的 TensorFlow 库来判断 GPU 是否可用。具体代码如下：

import tensorflow as tf

# 判断 GPU 是否可用
if tf.test.is_gpu_available():
print("GPU 可用")
else:
print("GPU 不可用")

希望能够帮助您解决问题。

在pytorch神经网络中，写一份代码，使用checkpoint实现参数保存

好的，我可以回答这个问题。以下是使用checkpoint实现参数保存的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
from torchvision import datasets, transforms

# 定义神经网络模型
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.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

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

# 定义训练函数
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 = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == :
            print('Train Epoch: {} [{}/{} ({:.f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 
    correct = 
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 定义训练参数
batch_size = 64
test_batch_size = 100
epochs = 10
lr = .01
momentum = .5
seed = 1
log_interval = 100

# 设置随机种子
torch.manual_seed(seed)

# 判断是否有可用的GPU
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# 加载数据集
train_loader = data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((.1307,), (.3081,))
                   ])),
    batch_size=batch_size, shuffle=True)

test_loader = data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((.1307,), (.3081,))
                   ])),
    batch_size=test_batch_size, shuffle=True)

# 初始化模型和优化器
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练和测试模型
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

    # 保存模型参数
    torch.save({
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss,
    }, 'checkpoint.pth')

这段代码实现了一个简单的卷积神经网络，使用MNIST数据集进行训练，并在每个epoch结束时保存模型参数到checkpoint.pth文件中。在下一次训练时，可以使用torch.load()函数加载checkpoint文件中的参数，继续训练模型。