import os import torch import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import StepLR from sagemaker.remote_function import remote from model import Net from load_data import load_data # Set path to config file os.environ["SAGEMAKER_USER_CONFIG_OVERRIDE"] = os.path.abspath("../config/config.yaml") def train(model, device, train_loader, optimizer, epoch, log_interval, dry_run): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if dry_run: break def test(model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) @remote(include_local_workdir=True) def perform_train(train_data, test_data, *, batch_size: int = 64, test_batch_size: int = 1000, epochs: int = 3, lr: float = 1.0, gamma: float = 0.7, no_cuda: bool = True, no_mps: bool = True, dry_run: bool = False, seed: int = 1, log_interval: int = 10, ): """PyTorch MNIST Example :param train_data: the training data set :param test_data: the test data set :param batch_size: input batch size for training (default: 64) :param test_batch_size: input batch size for testing (default: 1000) :param epochs: number of epochs to train (default: 14) :param lr: learning rate (default: 1.0) :param gamma: Learning rate step gamma (default: 0.7) :param no_cuda: disables CUDA training :param no_mps: disables macOS GPU training :param dry_run: quickly check a single pass :param seed: random seed (default: 1) :param log_interval: how many batches to wait before logging training status :return: the trained model """ use_cuda = not no_cuda and torch.cuda.is_available() use_mps = not no_mps and torch.backends.mps.is_available() torch.manual_seed(seed) if use_cuda: device = torch.device("cuda") elif use_mps: device = torch.device("mps") else: device = torch.device("cpu") train_kwargs = {'batch_size': batch_size} test_kwargs = {'batch_size': test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 1, 'pin_memory': True, 'shuffle': True} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_data, **train_kwargs) test_loader = torch.utils.data.DataLoader(test_data, **test_kwargs) model = Net().to(device) optimizer = optim.Adadelta(model.parameters(), lr=lr) scheduler = StepLR(optimizer, step_size=1, gamma=gamma) for epoch in range(1, epochs + 1): train(model, device, train_loader, optimizer, epoch, log_interval, dry_run) test(model, device, test_loader) scheduler.step() return model if __name__ == "__main__": training_data, test_data = load_data() perform_train(training_data, test_data, dry_run=True)