深度学习模型训练速度提升技巧汇总

深度学习模型训练速度提升技巧汇总

1. 使用混合精度训练

import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler, autocast

model = YourModel().cuda()
scaler = GradScaler()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(epochs):
    for batch in dataloader:
        optimizer.zero_grad()
        with autocast():
            output = model(batch)
            loss = criterion(output, target)
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

2. 数据加载优化

from torch.utils.data import DataLoader

dataloader = DataLoader(
    dataset,
    batch_size=64,
    num_workers=8,
    pin_memory=True,
    persistent_workers=True,
    prefetch_factor=2
)

3. 模型并行化

# 使用DataParallel
model = nn.DataParallel(model)

# 或使用DistributedDataParallel
from torch.nn.parallel import DistributedDataParallel as DDP
dist.init_process_group(backend='nccl')
model = DDP(model, device_ids=[rank])

性能测试数据：混合精度训练可提升20-40%训练速度，数据加载优化提升30%以上，模型并行化在8卡GPU上可提升50%+。建议按实际硬件配置选择合适策略。