在CentOS环境下使用PyTorch进行并行计算可以显著提高深度学习模型的训练速度和效率。以下是一些关键的并行计算技巧和实践步骤:
import torch
import torch.nn as nn
from torchvision import models
model = models.resnet50(pretrained=True)
if torch.cuda.device_count() > 1:
print(f"Let's use {torch.cuda.device_count()} GPUs!")
model = nn.DataParallel(model)
model.to('cuda')
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, DistributedSampler
from torchvision import datasets, transforms
dist.init_process_group(backend='nccl')
model = models.resnet50(pretrained=True).to(torch.device("cuda"))
model = DDP(model)
from torch.cuda.amp import autocast, GradScaler
model = MyModel().cuda()
optimizer = torch.optim.Adam(model.parameters())
scaler = GradScaler()
with autocast():
outputs = model(inputs)
loss = loss_fn(outputs, targets)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
确保你的CentOS系统上已经安装了PyTorch和CUDA。你可以使用以下命令安装PyTorch:
pip install torch torchvision torchaudio
通过以上步骤和技巧,你可以在CentOS上高效地使用PyTorch进行并行计算,显著提升深度学习模型的训练速度和扩展性。