PyTorch 中实现模型训练看板实时监控训练过程中的关键指标

在 PyTorch 中实现模型训练看板（Dashboard）可以帮助你实时监控训练过程中的关键指标（如损失、准确率、学习率等）。常用的工具包括 TensorBoard、Weights & Biases (W&B) 和 Matplotlib。以下是使用这些工具实现训练看板的详细方法。

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1. 使用 TensorBoard

TensorBoard 是 PyTorch 官方推荐的训练可视化工具，支持实时监控训练过程中的各种指标。

安装 TensorBoard

pip install tensorboard

在 PyTorch 中使用 TensorBoard

以下是一个简单的示例，展示如何在训练过程中记录损失和准确率。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms

# 定义模型
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(28 * 28, 10)

    def forward(self, x):
        return self.fc(x.view(x.size(0), -1))

# 初始化模型、损失函数和优化器
model = SimpleModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 加载数据集
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 初始化 TensorBoard
writer = SummaryWriter('runs/experiment_1')

# 训练循环
for epoch in range(5):
    running_loss = 0.0
    correct = 0
    total = 0

    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 记录损失
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        if i % 100 == 99:  # 每 100 个 batch 记录一次
            avg_loss = running_loss / 100
            accuracy = 100 * correct / total
            print(f'Epoch {epoch + 1}, Batch {i + 1}: Loss={avg_loss:.4f}, Accuracy={accuracy:.2f}%')

            # 将损失和准确率写入 TensorBoard
            writer.add_scalar('Training Loss', avg_loss, epoch * len(train_loader) + i)
            writer.add_scalar('Training Accuracy', accuracy, epoch * len(train_loader) + i)

            running_loss = 0.0
            correct = 0
            total = 0

# 关闭 TensorBoard
writer.close()

启动 TensorBoard

在终端运行以下命令启动 TensorBoard：

tensorboard --logdir=runs

然后在浏览器中打开 http://localhost:6006 查看训练看板。

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2. 使用 Weights & Biases (W&B)

Weights & Biases 是一个强大的实验跟踪工具，支持实时监控训练过程，并且可以与其他团队成员共享结果。

安装 W&B

pip install wandb

在 PyTorch 中使用 W&B

以下是一个简单的示例：

import wandb
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 初始化 W&B
wandb.init(project="pytorch-dashboard-example", config={
    "learning_rate": 0.01,
    "epochs": 5,
    "batch_size": 64
})

# 定义模型
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(28 * 28, 10)

    def forward(self, x):
        return self.fc(x.view(x.size(0), -1))

# 初始化模型、损失函数和优化器
model = SimpleModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=wandb.config.learning_rate)

# 加载数据集
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=wandb.config.batch_size, shuffle=True)

# 训练循环
for epoch in range(wandb.config.epochs):
    running_loss = 0.0
    correct = 0
    total = 0

    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 记录损失和准确率
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        if i % 100 == 99:  # 每 100 个 batch 记录一次
            avg_loss = running_loss / 100
            accuracy = 100 * correct / total
            print(f'Epoch {epoch + 1}, Batch {i + 1}: Loss={avg_loss:.4f}, Accuracy={accuracy:.2f}%')

            # 将指标记录到 W&B
            wandb.log({
                "Epoch": epoch + 1,
                "Batch": i + 1,
                "Training Loss": avg_loss,
                "Training Accuracy": accuracy
            })

            running_loss = 0.0
            correct = 0
            total = 0

# 完成训练
wandb.finish()

查看 W&B 看板

运行代码后，可以在 W&B 的网页端查看实时训练看板。

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3. 使用 Matplotlib

如果你需要一个简单的本地可视化工具，可以使用 Matplotlib 绘制训练过程中的指标。

安装 Matplotlib

pip install matplotlib

在 PyTorch 中使用 Matplotlib

以下是一个简单的示例：

import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义模型
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(28 * 28, 10)

    def forward(self, x):
        return self.fc(x.view(x.size(0), -1))

# 初始化模型、损失函数和优化器
model = SimpleModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 加载数据集
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

# 记录训练过程中的指标
losses = []
accuracies = []

# 训练循环
for epoch in range(5):
    running_loss = 0.0
    correct = 0
    total = 0

    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 记录损失和准确率
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

        if i % 100 == 99:  # 每 100 个 batch 记录一次
            avg_loss = running_loss / 100
            accuracy = 100 * correct / total
            print(f'Epoch {epoch + 1}, Batch {i + 1}: Loss={avg_loss:.4f}, Accuracy={accuracy:.2f}%')

            losses.append(avg_loss)
            accuracies.append(accuracy)

            running_loss = 0.0
            correct = 0
            total = 0

# 绘制损失和准确率曲线
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(losses, label='Training Loss')
plt.xlabel('Batch')
plt.ylabel('Loss')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(accuracies, label='Training Accuracy')
plt.xlabel('Batch')
plt.ylabel('Accuracy')
plt.legend()

plt.show()

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总结

• TensorBoard：适合本地实时监控，功能强大，支持多种可视化。
• Weights & Biases：适合团队协作和实验跟踪，支持云端存储和共享。
• Matplotlib：适合简单的本地可视化，无需额外依赖。

根据你的需求选择合适的工具来实现模型训练看板。