single-machine-and-single-GPU.py

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
from model import NeuralNetwork


def train(dataloader, model, loss_fn, optimizer, device):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)  # copy data from cpu to gpu

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")


def test(dataloader, model, loss_fn, device):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)  # copy data from cpu to gpu
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")


if __name__ == '__main__':
    # initialize dataset
    training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
    test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())

    # initialize data loader
    batch_size = 64
    train_dataloader = DataLoader(training_data, batch_size=batch_size)
    test_dataloader = DataLoader(test_data, batch_size=batch_size)

    # Get cpu or gpu device for training.
    device = "cuda:0" if torch.cuda.is_available() else "cpu"  # using GPU
    print(f"Using {device} device")

    # initialize model
    model = NeuralNetwork().to(device)  # copy model from cpu to gpu
    print(model)

    # initialize optimizer
    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

    # train on single-GPU
    epochs = 5
    for t in range(epochs):
        print(f"Epoch {t + 1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer, device)
        test(test_dataloader, model, loss_fn, device)
    print("Done!")

    # save model
    torch.save(model.state_dict(), "model.pth")
    print("Saved PyTorch Model State to model.pth")