！注意 安装时一定要指定–index-url https://download.pytorch.org/whl/torch/ ，否则安装的是cpu版本，可以访问https://download.pytorch.org/whl/torch/，找到需要的版本如torch-2.0.1+cu117-cp38-cp38-win_amd64.whl 用迅雷下载比较快

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 

按官方 quickstart拼起來的代码,如果有GPU且安装的GPU版本pytorch则跑GPU上否则CPU（所有CPU），本地测试20CPU与1 个geforce GPU耗时差不多20s

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor#####################
# Download training data from open datasets.
training_data = datasets.FashionMNIST(root="data",train=True,download=True,transform=ToTensor(),
)# Download test data from open datasets.
test_data = datasets.FashionMNIST(root="data",train=False,download=True,transform=ToTensor(),
)
#####################
batch_size = 64# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:print(f"Shape of X [N, C, H, W]: {X.shape}")print(f"Shape of y: {y.shape} {y.dtype}")break
#####################
# Get cpu, gpu or mps device for training.
device = ("cuda"if torch.cuda.is_available()else "mps"if torch.backends.mps.is_available()else "cpu"
)
print(f"Using {device} device")# Define model
class NeuralNetwork(nn.Module):def __init__(self):super().__init__()self.flatten = nn.Flatten()self.linear_relu_stack = nn.Sequential(nn.Linear(28 * 28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10))def forward(self, x):x = self.flatten(x)logits = self.linear_relu_stack(x)return logitsmodel = NeuralNetwork().to(device)
print(model)
#######################################
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)########################################
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)model.train()for batch, (X, y) in enumerate(dataloader):X, y = X.to(device), y.to(device)# Compute prediction errorpred = model(X)loss = loss_fn(pred, y)# Backpropagationloss.backward()optimizer.step()optimizer.zero_grad()if batch % 100 == 0:loss, current = loss.item(), (batch + 1) * len(X)print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")#######################################
def test(dataloader, model, loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)model.eval()test_loss, correct = 0, 0with torch.no_grad():for X, y in dataloader:X, y = X.to(device), y.to(device)pred = model(X)test_loss += loss_fn(pred, y).item()correct += (pred.argmax(1) == y).type(torch.float).sum().item()test_loss /= num_batchescorrect /= sizeprint(f"Test Error: \n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")if __name__ == '__main__':epochs = 5for t in range(epochs):start = time.time()print(f"Epoch {t + 1}\n-------------------------------")train(train_dataloader, model, loss_fn, optimizer)test(test_dataloader, model, loss_fn)end = time.time()print(f"epoch Done:{end-start}")print("Done!")

D:\python2023>nvidia-smi
Fri Jul 28 00:42:59 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 497.29       Driver Version: 497.29       CUDA Version: 11.5     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:03:00.0  On |                  N/A |
| 27%   37C    P5     9W / 120W |   1007MiB /  3072MiB |      9%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     11536    C+G   ...se6\Application\360se.exe    N/A      |
|    0   N/A  N/A     14264    C+G   ...\qbblinktrial\browser.exe    N/A      |
|    0   N/A  N/A     15648      C   ...ython\Python38\python.exe    N/A      |
+-----------------------------------------------------------------------------+