PyTorch例子: importtorchimporttorch.nnasnn# 假设我们有一个简单的CNN 叫做 ConvNet# 训练时使用多卡只需要使用:deftrain(gpu,args):model=ConvNet()model=nn.DataParallel(model)torch.cuda.set_device(gpu)model.cuda(gpu)# 以下省略 Q:如果loss计算和back propagation只在主GPU上进行,那么是不是主显存很容易...
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])#均值,标准差trainset= torchvision.datasets.CIFAR10(root='../../data', train=True, download=True, transform=transform)#The output of torchvision datasets are PILImage images of range [0, 1].#We transform them to Tensors of norm...
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=transform) # 下载训练数据 trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) # 按照 batch_size 加载(组合)训练数据 testset = torchvision.datasets.CIFAR10(...
在遍历 dataloader 时,使用X, y = X.to(device), y.to(device)将数据 X, y 复制到 GPU 设备上去计算。 def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) #...
# -1: train on all gpustrainer = Trainer(gpus=-1)trainer = Trainer(gpus='-1') # equivalent # combine with num_nodes to train on multiple GPUs across nodes# uses 8 gpus in totaltrainer = Trainer(gpus=2, num_nodes=4) # train only on GPUs 1 an...
https://towardsdatascience.com/how-to-scale-training-on-multiple-gpus-dae1041f49d2 建议 5: 如果你拥有两个及以上的 GPU 能节省多少时间很大程度上取决于你的方案，我观察到，在 4x1080Ti 上训练图像分类 pipeline 时，大概可以节约 20% 的时间。另外值得一提的是，你也可以用 nn.DataParallel 和 nn....
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=False, num_workers=n_worker, pin_memory=True, sampler=train_sampler) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=n_worker, pin_memory=True,...
'train.py': single training process on one GPU only. 'train_parallel.py': signle training process on multiple GPUs usingDataparallel(包括不同GPU之间的负载均衡). 'train_distributed.py' (recommended): multiple training processes on multiple GPUs usingNvidia Apex&Distributed Training: ...
https://towardsdatascience.com/how-to-scale-training-on-multiple-gpus-dae1041f49d2 建议5: 如果你拥有两个及以上的 GPU 能节省多少时间很大程度上取决于你的方案,我观察到,在 4x1080Ti 上训练图像分类 pipeline 时,大概可以节约 20% 的时间。另外值得一提的是,你也可以用 nn.DataParallel 和 nn.Distribut...
Depending on your use-case, e.g., if you just train a model on multiple GPUs, it might be worth usingDataParallelinstead ofmultiprocessing. stalebotadded thestalelabelJun 18, 2021 stalebotclosed this ascompletedJun 28, 2021