centos

CentOS环境下PyTorch并行计算实践

小樊
50
2025-08-02 19:35:39
栏目: 智能运维

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:

多GPU并行

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)

分布式训练

性能优化技术

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)

环境准备

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

注意事项

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。

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