Simple training loop pseudocode

simple_train.py

model = create_model(params)
phases = create_train_valid_data()
opt = optim.SGD(model.params, lr=1e-3)

model.to(device)
    
for epoch in range(1, epochs + 1):

    for phase in phases:
        n = len(phase.loader)
        is_training = phase.grad
        model.train(is_training)

        for batch in phase.loader:
            x, y = place_and_unwrap(batch, device)

            with torch.set_grad_enabled(is_training):
                out = model(x)
                loss = loss_fn(out, y)

            if is_training:
                opt.zero_grad()
                loss.backward()
                opt.step()

            phase.batch_loss = loss.item()