win10+cuda8+cudnn5.1+Anaconda3+pytorch+torchvision
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win10+cuda8+cudnn5.1+Anaconda3+pytorch+torchvision
1.windows安装cuda8和cudnn5.1
这个教程很多,我不赘述。自行百度谷歌。
2.windows安装Anaconda3
推荐参考:win10下安装使用pytorch以及cuda9、cudnn7.0,Anaconda3虚拟环境的设置真的很赞!我的虚拟环境设置如下,使用的是python3.6,路径在E:\ProgramingTools\Anaconda\Anaconda3\envs\my_pytorch。
3.安装pytorch+torchvision
3.1修改conda的安装源进行加速安装
$conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/$conda config --set show_channel_urls yes
3.2在虚拟环境中安装必要依赖
# -n my_pytorch 是指你的虚拟环境$conda install -n my_pytorch numpy pyyaml mkl setuptools cmake cffi
3.3使用安装包安装pytorch
pytorch安装包百度网盘地址:https://pan.baidu.com/s/1nvaamrn#list/path=%2F 。我下载的是pytorch-0.2.1-py36he6bf560_0.2.1cu80.tar.bz2,即python3.6-cuda8版本的安装包。下载完成后,进入该文件目录,
$conda install -n my_pytorch pytorch-0.2.1-py36he6bf560_0.2.1cu80.tar.bz2
等待一段时间后,应该就可以import torch了。
另外,注意numpy、scipy、matplotlib的前后依赖性,由于已经安装了numpy,所以只需再安装scipy、matplotlib即可,代码如下:
conda install -n my_pytorch scipy matplotlib
3.4pip安装torchvision
#进入虚拟环境,直接pip,不过网络要好,我是翻墙安装好的$activate my_pytorch(my_pytorch)$pip install torchvision
3.5测试代码
运行下面这段代码:
# CUDA TESTimport torchx = torch.Tensor([1.0])xx = x.cuda()print(xx)# CUDNN TESTfrom torch.backends import cudnnprint(cudnn.is_acceptable(xx))
结果像下面这样即是可以使用gpu加速了:
另外LogisticRegression代码测试:
import torchimport torch.nn as nnimport torchvision.datasets as dsetsimport torchvision.transforms as transformsfrom torch.autograd import Variable# Hyper Parametersinput_size = 784num_classes = 10num_epochs = 5batch_size = 100learning_rate = 0.001# MNIST Dataset (Images and Labels)train_dataset = dsets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)test_dataset = dsets.MNIST(root='./data', train=False, transform=transforms.ToTensor())# Dataset Loader (Input Pipline)train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)# Modelclass LogisticRegression(nn.Module): def __init__(self, input_size, num_classes): super(LogisticRegression, self).__init__() self.linear = nn.Linear(input_size, num_classes) def forward(self, x): out = self.linear(x) return outmodel = LogisticRegression(input_size, num_classes)# Loss and Optimizer# Softmax is internally computed.# Set parameters to be updated.criterion = nn.CrossEntropyLoss()optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)# Training the Modelfor epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = Variable(images.view(-1, 28 * 28)) labels = Variable(labels) # Forward + Backward + Optimize optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() if (i + 1) % 100 == 0: print ('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f' % (epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, loss.data[0]))# Test the Modelcorrect = 0total = 0for images, labels in test_loader: images = Variable(images.view(-1, 28 * 28)) outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum()print('Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))# Save the Modeltorch.save(model.state_dict(), 'model.pkl')
测试结果如下:
参考资源:
【1】win10下安装使用pytorch以及cuda9、cudnn7.0
【2】windows下超简单安装Anaconda配置环境以及虚拟环境配置
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