目前正在写一个训练框架，需要有以下几个功能：
1.保存模型
2.断点继续训练
3.加载模型
4.tensorboard 查询训练记录的功能
命令：
tensorboard --logdir=runs --host=192.168.112.5
效果：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter

# 假设有一个训练数据集和其对应的标签
# 例如：
data = torch.tensor([[1.0], [2.0], [3.0]])
labels = torch.tensor([[2.0], [4.0], [6.0]])

class MYdataset(torch.utils.data.Dataset):
    def __init__(self, features, labels):
        self.features = features
        self.labels = labels
    def __getitem__(self, index):
        return self.features[index], self.labels[index]

    def __len__(self):
        return len(self.features)

BATCH_SIZE= 1

train_ds = MYdataset(data, labels)


train_dl = torch.utils.data.DataLoader(
    train_ds,
    batch_size=BATCH_SIZE,
    shuffle = True
)

 
# 定义模型、损失函数和优化器
model = nn.Linear(1, 1)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
 
# 创建summary writer
writer = SummaryWriter('runs/loss_visualization')
 
for epoch in range(10):  # 假设我们只训练10个周期
    for i, (data,labels) in enumerate(train_dl):  # 假设train_loader是数据加载器
        # 前向传播
        outputs = model(data)
        loss = criterion(outputs, labels)
 
        # 反向传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        # 将损失记录到TensorBoard中
        print("Loss:",loss.item())
        writer.add_scalar('Loss', loss.item(), epoch * len(train_dl) + i)
 
# 关闭summary writer
writer.close()