[源码解析] PyTorch 分布式(14) --使用 Distributed Autograd 和 Distributed Optimizer 0x00 摘要 0x01 说明 0x02 启动 0x03 Trainer 0x04 模型 4.1 组件 4.1.1 参考代码 4.1.2 分布式修改 4.2 RNN 模型 4.3 分布式优化器 4.4 比对 0xFF 参考 0x00 摘要 在前面的文
[loss])# 执行分布式后向传播# run distributed optimizeropt.step(context_id)# 分布式优化器进行更新# not necessary to zero grads since they are# accumulated into the distributed autograd context# which is reset every iteration.print("Training epoch {}".format(epoch)) ...
当使用DistributedSampler时,DataLoader将调用该采样器来获取每个进程应处理的索引。 例如,如果有 3 个进程,DistributedSampler会将数据集的索引分成 3 份,并将这些索引分配给每个进程。 加载数据: DataLoader根据DistributedSampler提供的索引来加载数据。每个进程会根据自己的索引从数据集中提取数据,并将其批量化(batchify)...
loss = criterion(output, target) # run distributed backward pass dist_autograd.backward(context_id, [loss]) # 执行分布式后向传播 # run distributed optimizer opt.step(context_id) # 分布式优化器进行更新 # not necessary to zero grads since they are # accumulated into the distributed autograd cont...
from apex.parallel import DistributedDataParallel as DDP from apex import amp model = ConvNet() torch.cuda.set_device(gpu) model.cuda(gpu) optimizer = torch.optim.SGD(model.parameters(), 1e-4) model, optimizer = amp.initialize(model, optimizer, ...
from torch.nn.parallel import DistributedDataParallel as DDPfrom torchvision import models as modelsmodel = models.resnet34(pretrained=True)loss_fn = nn.CrossEntropyLoss()model.cuda(current_gpu_index)model = DDP(model)loss_fn.cuda(current_gpu_index)optimizer = optim.Adam(filter(lambda p: p....
即,在每个不同的Ref所有者之上创建一个 Optimizer()实例,然后运行step()相应更新参数。当用户进行分布式前向和后向传播时,参数和梯度将分散在多个 worker 中,因此需要对每个相关 worker 进行优化。Distributed Optimizer 将所有这些本地优化器合而为一,并提供了简洁的构造函数...
model = LeNet()# first synchronization of initial weightssync_initial_weights(model, rank, world_size)optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.85)model.train()for epoch in range(1, epochs + 1):for data, target in train_loader: optimizer.zero_grad() output = model...
model= DistributedDataParallel(model, device_ids=[local_rank])#要调用model内的函数或者属性. model.module.xxxx 这样在多卡训练时,每个进程有一个model副本和optimizer,使用自己的数据进行训练,之后反向传播计算完梯度的时候,所有进程的梯度会进行all-reduce操作进行同步,进而保证每个卡上的模型更新梯度是一样的,模型...
""" Distributed Synchronous SGD Example """defrun(rank, size):torch.manual_seed(1234)train_set, bsz = partition_dataset()model = Net()optimizer = optim.SGD(model.parameters(),lr=0.01, momentum=0.5) num_batches = ceil(len(train_set.dataset...