Programming Language: Python

Namespace/Package Name: apex.parallel

Class/Type: DistributedDataParallel

Method/Function: DistributedDataParallel

Examples at hotexamples.com: 30

DistributedDataParallel is a package library in the python apex.parallel module that helps in distributed data parallelism. This library helps in synchronizing the gradients of different GPUs working on a single model. The main aim of this library is to speed up the training of deep learning models by dividing the work among multiple GPUs.

Here are some code examples:

**Example 1:** Using DDP for training a model:

In this example, we are creating a linear model with 10 input features and 1 output. We are using the apex.parallel.DistributedDataParallel library to distribute the training of the model across multiple GPUs. We are also using stochastic gradient descent (SGD) as our optimizer and mean squared error (MSE) as the loss function.

**Example 2:** Using DDP for image classification:

Here are some code examples:

import torch import apex model = torch.nn.Linear(10, 1) model = apex.parallel.DistributedDataParallel(model) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(10): for input, target in train_data: output = model(input) loss = torch.nn.functional.mse_loss(output, target) optimizer.zero_grad() loss.backward() optimizer.step()

In this example, we are creating a linear model with 10 input features and 1 output. We are using the apex.parallel.DistributedDataParallel library to distribute the training of the model across multiple GPUs. We are also using stochastic gradient descent (SGD) as our optimizer and mean squared error (MSE) as the loss function.

import torch import torchvision import torchvision.transforms as transforms import apex transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) train_set = torchvision.datasets.MNIST('./data', train=True, download=True, transform=transform) train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True) model = torchvision.models.resnet50() model.fc = torch.nn.Linear(2048, 10) model = apex.parallel.DistributedDataParallel(model) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) for epoch in range(10): for i, data in enumerate(train_loader, 0): inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, labels) loss.backward() optimizer.step()In this example, we are using the MNIST dataset to train a ResNet-50 model for image classification. We are using the apex.parallel.DistributedDataParallel library to distribute the training of the model across multiple GPUs. We are also using stochastic gradient descent (SGD) as our optimizer and cross-entropy loss as the loss function.

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