win10+cuda8+cudnn5.1+Anaconda3+pytorch+torchvision

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 TEST
import torch
x = torch.Tensor([1.0])
xx = x.cuda()
print(xx)

# CUDNN TEST
from torch.backends import cudnn
print(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 torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable

# Hyper Parameters
input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_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)


# Model
class 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 out


model = 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 Model
for 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 Model
correct = 0
total = 0
for 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 Model
torch.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