DeterministicEngine

class ignite.engine.deterministic.DeterministicEngine(process_function)

Deterministic engine derived from Engine.

“Deterministic” run is done by adding additional handlers to synchronize the dataflow and overriding some methods of Engine:

for e in range(num_epochs):
    set_seed(seed_offset + e)
    if resume:
        setup_saved_rng_states()
    do_single_epoch_iterations(dataloader)

If input data provider is DataLoader, its batch sampler is replaced by ReproducibleBatchSampler.

for e in range(num_epochs):
    set_seed(seed_offset + e)
    setup_sampling(dataloader)
    if resume:
        setup_saved_rng_states()
    do_single_epoch_iterations(dataloader)

Internally, torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False are also applied.

For more details about dataflow synchronization, please see Dataflow synchronization.

Note

This class can produce exactly the same dataflow when resuming the run from an epoch (or more precisely from dataflow restart) and using torch DataLoader with num_workers > 1 as data provider.

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.

Methods

state_dict

Returns a dictionary containing engine's state: "epoch_length", "iteration", one of "max_epochs" or "max_iters", and other state values defined by engine.state_dict_user_keys.

state_dict()

Returns a dictionary containing engine’s state: "epoch_length", "iteration", one of "max_epochs" or "max_iters", and other state values defined by engine.state_dict_user_keys.

engine = Engine(...)
engine.state_dict_user_keys.append("alpha")
engine.state_dict_user_keys.append("beta")
...

@engine.on(Events.STARTED)
def init_user_value(_):
     engine.state.alpha = 0.1
     engine.state.beta = 1.0

@engine.on(Events.COMPLETED)
def save_engine(_):
    state_dict = engine.state_dict()
    assert "alpha" in state_dict and "beta" in state_dict
    torch.save(state_dict, "/tmp/engine.pt")

Returns:

a dictionary containing engine’s state

Return type:

OrderedDict

Changed in version 0.5.5: Added support for serializing max_iters.