当前位置：首页 > news >正文

用Pip配置Pytorch环境 (Pytorch==2.3.0)

news 来源：原创 2024/9/29 8:16:46

用Pip配置Pytorch环境 (Pytorch==2.3.0)

本文主要讲解: 如何用Conda搭建Pytorch环境，用Conda的方式安装，需要单独去安装Cuda。

1. 下载Python安装包

安装Python 3.10.11，下载地址 Python 3.10.11

2. CUDA 安装

安装CUDA 12.1，查看官网：CUDA 12.1
下载地址 CUDA 12.1

cuda安装完之后，已经配置好环境路径了，直接在cmd中查看

nvcc -V

3. Cudnn 8.x 安装

安装Cudnn 8.x，查看官网：Cudnn 8.x
下载地址 Cudnn 8.x

把cudnn8.x解压出来的文件，拷贝到cuda下，有对应的文件下名称，对应拷贝过去。

4. 安装Pytorch

pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121

5. 安装常用包

pip install scikit-learn einops ipywidgets pandas tqdm jupyterlab matplotlib seaborn

6. pip设置清华源

pip config list
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

7. 一个分类网络的例子

测试Pytorch环境是否Okay

python mnist.py

文件mnist.py内容：

# Task
# Our task is simple, recognize handwritten digits. We will use MNIST dataset for this tutorial.
# # # Import necessary library
# In this tutorial, we are going to use pytorch, the cutting-edge deep learning framework to complete our task.# In[2]:import torch
import torchvision# In[3]:## Create dataloader, in PyTorch, we feed the trainer data with use of dataloader
## We create dataloader with dataset from torchvision, 
## and we dont have to download it seperately, all automatically done# Define batch size, batch size is how much data you feed for training in one iteration
batch_size_train = 64 # We use a small batch size here for training
batch_size_test = 1024 ## define how image transformed
image_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,), (0.3081,))])
#image datasets
train_dataset = torchvision.datasets.MNIST('dataset/', train=True, download=True,transform=image_transform)
test_dataset = torchvision.datasets.MNIST('dataset/', train=False, download=True,transform=image_transform)
#data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size_test, shuffle=True)# In[64]:# import library
# We can check the dataloader
_, (example_datas, labels) = next(enumerate(test_loader))
sample = example_datas[0][0]
# show the data# In[60]:## Now we can start to build our CNN model
## We first import the pytorch nn module and optimizer
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
## Then define the model class
class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()#input channel 1, output channel 10self.conv1 = nn.Conv2d(1, 10, kernel_size=5, stride=1)#input channel 10, output channel 20self.conv2 = nn.Conv2d(10, 20, kernel_size=5, stride=1)#dropout layerself.conv2_drop = nn.Dropout2d()#fully connected layerself.fc1 = nn.Linear(320, 5000)self.fc2 = nn.Linear(5000, 10)def forward(self, x):x = self.conv1(x)x = F.max_pool2d(x, 2)x = F.relu(x)x = self.conv2(x)x = self.conv2_drop(x)x = F.max_pool2d(x, 2)x = F.relu(x)x = x.view(-1, 320)x = self.fc1(x)x = F.relu(x)x = F.dropout(x)x = self.fc2(x)return F.log_softmax(x)# In[61]:## create model and optimizer
learning_rate = 0.01
momentum = 0.5
device = "cuda"
model = CNN().to(device) #using cpu here
optimizer = optim.SGD(model.parameters(), lr=learning_rate,momentum=momentum)# In[78]:##define train function
def train(model, device, train_loader, optimizer, epoch, log_interval=10000):model.train()counter = 0for 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()counter += 1print("loss:", loss.item())
##define test function
def test(model, device, test_loader):model.eval()test_loss = 0correct = 0with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch losspred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probabilitycorrect += pred.eq(target.view_as(pred)).sum().item()test_loss /= len(test_loader.dataset)print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(test_loss, correct, len(test_loader.dataset),100. * correct / len(test_loader.dataset)))# In[79]:num_epoch = 10
for epoch in range(1, num_epoch + 1):train(model, device, train_loader, optimizer, epoch)test(model, device, test_loader)# In[70]:# from torchsummary import summary
# summary(model, (1, 28, 28))