ENGINE

CLASSignite.engine.engine.Engine(process_function)

Runs a given process_function over each batch of a dataset, emitting events as it goes. Parameters

process_function (Callable[[Engine, Any], Any]) – A function receiving a handle to the engine and the current batch in each iteration, and returns data to be stored in the engine’s state.

state object that is used to pass internal and user-defined state between event handlers. It is created with the engine and its attributes (e.g. state.iteration, state.epoch etc) are reset on every run().

last_event_name last event name triggered by the engine.

NOTE Engine implementation has changed in v0.4.10 with “interrupt/resume” feature. Engine may behave differently on certain corner cases compared to the one from v0.4.9 and before. In such case, you can set Engine.interrupt_resume_enabled = False to restore previous behaviour.

Examples

Create a basic trainer

def update_model(engine, batch):
    inputs, targets = batch
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()
    return loss.item()

trainer = Engine(update_model)

@trainer.on(Events.ITERATION_COMPLETED(every=100))
def log_training(engine):
    batch_loss = engine.state.output
    lr = optimizer.param_groups[0]['lr']
    e = engine.state.epoch
    n = engine.state.max_epochs
    i = engine.state.iteration
    print(f"Epoch {e}/{n} : {i} - batch loss: {batch_loss}, lr: {lr}")

trainer.run(data_loader, max_epochs=5)

> Epoch 1/5 : 100 - batch loss: 0.10874069479016124, lr: 0.01
> ...
> Epoch 2/5 : 1700 - batch loss: 0.4217900575859437, lr: 0.01