import torch import apex.parallel as parallel # create a model and optimizer model = torch.nn.Linear(10, 1) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # create distributed data parallel model model = parallel.DistributedDataParallel(model) # get input and target data from dataset input_data, target_data = get_data_from_dataset() # train the model for i in range(num_epochs): output_data = model(input_data) loss = torch.nn.functional.mse_loss(output_data, target_data) loss.backward() optimizer.step() optimizer.zero_grad() model.sync_parameters()
import torch import apex.parallel as parallel # create a model and optimizer model = torch.nn.Linear(10, 1) optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # create distributed data parallel model model = parallel.DistributedDataParallel(model) # get input and target data from dataset input_data, target_data = get_data_from_dataset() # train the model across multiple processes parallel.spawn(train_process, args=(input_data, target_data, model, optimizer), nprocs=4) # define train process to be parallelized def train_process(rank, input_data, target_data, model, optimizer): torch.cuda.set_device(rank) model.cuda(rank) input_data = input_data.cuda(rank) target_data = target_data.cuda(rank) for i in range(num_epochs): output_data = model(input_data) loss = torch.nn.functional.mse_loss(output_data, target_data) optimizer.zero_grad() loss.backward() optimizer.step() model.sync_gradients()In this example, we create a linear model with 10 inputs and 1 output. We then create an optimizer using stochastic gradient descent. We use distributed data parallelism to create a version of the model that can be trained across multiple GPUs. We get input and target data from a dataset and train the model using MSE loss and gradient descent across multiple processes. The package library being used here is Apex, which is an open-source PyTorch extension library that provides a set of utilities and convenient wrappers for distributed training, mixed precision, and other applications.