# Concepts#

## Engine#

The essence of the framework is the class Engine, an abstraction that loops a given number of times over provided data, executes a processing function and returns a result:

while epoch < max_epochs:
# run an epoch on data
data_iter = iter(data)
while True:
try:
batch = next(data_iter)
output = process_function(batch)
iter_counter += 1
except StopIteration:
data_iter = iter(data)

if iter_counter == epoch_length:
break


Thus, a model trainer is simply an engine that loops multiple times over the training dataset and updates model parameters. Similarly, model evaluation can be done with an engine that runs a single time over the validation dataset and computes metrics. For example, model trainer for a supervised task:

def update_model(trainer, batch):
model.train()
x, y = prepare_batch(batch)
y_pred = model(x)
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
return loss.item()

trainer = Engine(update_model)
trainer.run(data, max_epochs=100)


Note

By default, epoch length is defined by len(data). However, user can also manually define the epoch length as a number of iterations to loop. In this way the input data can be an iterator.

trainer.run(data, max_epochs=100, epoch_length=200)


## Events and Handlers#

To improve the Engine’s flexibility, an event system is introduced that facilitates interaction on each step of the run:

• engine is started/completed

• epoch is started/completed

• batch iteration is started/completed

Complete list of events can be found at Events.

Thus, user can execute a custom code as an event handler. Handlers can be any function: e.g. lambda, simple function, class method etc. The first argument can be optionally engine, but not necessary.

Let us consider in more detail what happens when run() is called:

fire_event(Events.STARTED)
while epoch < max_epochs:
fire_event(Events.EPOCH_STARTED)
# run once on data
for batch in data:
fire_event(Events.ITERATION_STARTED)

output = process_function(batch)

fire_event(Events.ITERATION_COMPLETED)
fire_event(Events.EPOCH_COMPLETED)
fire_event(Events.COMPLETED)


At first engine is started event is fired and all this event handlers are executed (we will see in the next paragraph how to add event handlers). Next, while loop is started and epoch is started event occurs, etc. Every time an event is “fired”, attached handlers are executed.

Attaching an event handler is simple using method add_event_handler() or on() decorator:

trainer = Engine(update_model)

# or
@trainer.on(Events.STARTED)
def on_training_started(engine):
print("Another message of start training")
# or even simpler, use only what you need !
@trainer.on(Events.STARTED)
def on_training_started():
print("Another message of start training")

# attach handler with args, kwargs
mydata = [1, 2, 3, 4]

def on_training_ended(data):
print("Training is ended. mydata={}".format(data))



Event handlers can be detached via remove_event_handler() or via the RemovableEventHandle reference returned by add_event_handler(). This can be used to reuse a configured engine for multiple loops:

model = ...

trainer = create_supervised_trainer(model, optimizer, loss)
evaluator = create_supervised_evaluator(model, metrics={'acc': Accuracy()})

def log_metrics(engine, title):
print("Epoch: {} - {} accuracy: {:.2f}"
.format(trainer.state.epoch, title, engine.state.metrics['acc']))

@trainer.on(Events.EPOCH_COMPLETED)
def evaluate(trainer):



Event handlers can be also configured to be called with a user pattern: every n-th events, once or using a custom event filtering function:

model = ...

trainer = create_supervised_trainer(model, optimizer, loss)

@trainer.on(Events.ITERATION_COMPLETED(every=50))
def log_training_loss_every_50_iterations():
print("{} / {} : {} - loss: {:.2f}"
.format(trainer.state.epoch, trainer.state.max_epochs, trainer.state.iteration, trainer.state.output))

@trainer.on(Events.EPOCH_STARTED(once=25))
def do_something_once_on_25_epoch():
# do something

def custom_event_filter(engine, event):
if event in [1, 2, 5, 10, 50, 100]:
return True
return False

@engine.on(Events.ITERATION_STARTED(event_filter=custom_event_filter))
def call_on_special_event(engine):
# do something on 1, 2, 5, 10, 50, 100 iterations



Note

User can also register custom events with register_events(), attach handlers and fire custom events calling fire_event() in any handler or process_function.

See the source code of create_supervised_tbptt_trainer for an example of usage of custom events.

### Timeline and events#

Below the events and some typical handlers are displayed on a timeline for a training loop with evaluation after every epoch:

## State#

A state is introduced in Engine to store the output of the process_function, current epoch, iteration and other helpful information. Each Engine contains a State, which includes the following:

• engine.state.seed: Seed to set at each data “epoch”.

• engine.state.epoch: Number of epochs the engine has completed. Initializated as 0 and the first epoch is 1.

• engine.state.iteration: Number of iterations the engine has completed. Initialized as 0 and the first iteration is 1.

• engine.state.max_epochs: Number of epochs to run for. Initializated as 1.

• engine.state.output: The output of the process_function defined for the Engine. See below.

• etc

Other attributes can be found in the docs of State.

In the code below, engine.state.output will store the batch loss. This output is used to print the loss at every iteration.

def update(engine, batch):
x, y = batch
y_pred = model(inputs)
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
return loss.item()

def on_iteration_completed(engine):
iteration = engine.state.iteration
epoch = engine.state.epoch
loss = engine.state.output
print("Epoch: {}, Iteration: {}, Loss: {}".format(epoch, iteration, loss))



Since there is no restrictions on the output of process_function, Ignite provides output_transform argument for its metrics and handlers. Argument output_transform is a function used to transform engine.state.output for intended use. Below we’ll see different types of engine.state.output and how to transform them.

In the code below, engine.state.output will be a list of loss, y_pred, y for the processed batch. If we want to attach Accuracy to the engine, output_transform will be needed to get y_pred and y from engine.state.output. Let’s see how that is done:

def update(engine, batch):
x, y = batch
y_pred = model(inputs)
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
return loss.item(), y_pred, y

trainer = Engine(update)

@trainer.on(Events.EPOCH_COMPLETED)
def print_loss(engine):
epoch = engine.state.epoch
loss = engine.state.output[0]
print ('Epoch {epoch}: train_loss = {loss}'.format(epoch=epoch, loss=loss))

accuracy = Accuracy(output_transform=lambda x: [x[1], x[2]])
accuracy.attach(trainer, 'acc')
trainer.run(data, max_epochs=10)


Similar to above, but this time the output of the process_function is a dictionary of loss, y_pred, y for the processed batch, this is how the user can use output_transform to get y_pred and y from engine.state.output. See below:

def update(engine, batch):
x, y = batch
y_pred = model(inputs)
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
return {'loss': loss.item(),
'y_pred': y_pred,
'y': y}

trainer = Engine(update)

@trainer.on(Events.EPOCH_COMPLETED)
def print_loss(engine):
epoch = engine.state.epoch
loss = engine.state.output['loss']
print ('Epoch {epoch}: train_loss = {loss}'.format(epoch=epoch, loss=loss))

accuracy = Accuracy(output_transform=lambda x: [x['y_pred'], x['y']])
accuracy.attach(trainer, 'acc')
trainer.run(data, max_epochs=10)


Note

A good practice is to use State also as a storage of user data created in update or handler functions. For example, we would like to save new_attribute in the state:

def user_handler_function(engine):
engine.state.new_attribute = 12345


## Deterministic training#

In general, it is rather difficult task to achieve deterministic and reproducible trainings as it relies on multiple aspects, e.g. data version, code version, software environment, hardware etc. According to PyTorch documentation: there are some steps to take in order to make computations deterministic on your specific problem on one specific platform and PyTorch release:

By default, these two options can be enough to run and rerun experiments in a deterministic way. Ignite’s engine does not impact this behaviour.

## Resuming the training#

It is also possible to resume the training from a checkpoint and approximatively reproduce original run’s behaviour. Using Ignite, this can be easily done using Checkpoint handler. Engine provides two methods to serialize and deserialize its internal state state_dict() and load_state_dict(). In addition to serializing model, optimizer, lr scheduler etc user can store the trainer and then resume the training. For example:

from ignite.engine import Engine, Events
from ignite.handlers import Checkpoint, DiskSaver

trainer = ...
model = ...
optimizer = ...
lr_scheduler = ...

to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}
handler = Checkpoint(to_save, DiskSaver('/tmp/training', create_dir=True))

ls /tmp/training
> "checkpoint_50000.pt"


We can then restore the training from the last checkpoint.

from ignite.handlers import Checkpoint

trainer = ...
model = ...
optimizer = ...
lr_scheduler = ...

to_load = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}



It is also possible to store checkpoints every N iterations and continue the training from one of these checkpoints, i.e from iteration.

### Dataflow synchronization#

Previous approach, however, does not synchronize the dataflow and the model does not see the same data samples when resuming from a checkpoint. Therefore, training curves will not be exactly the same.

Ignite provides an option to control the dataflow by synchronizing random state on epochs. In this way, for a given iteration/epoch the dataflow can be the same for a given seed. More precisely it is roughly looks like:

for e in range(num_epochs):
set_seed(seed + e)


In addition, if data provider is torch.utils.data.DataLoader, batch data indices can be made completely deterministic. Here is a trivial example of usage:

import torch
from ignite.engine import DeterministicEngine, Events
from ignite.utils import manual_seed

data = torch.arange(0, size)

def print_train_data(engine, batch):
i = engine.state.iteration
e = engine.state.epoch
print("train", e, i, batch.tolist())

trainer = DeterministicEngine(print_train_data)

print("Original Run")
manual_seed(56)

print("Resumed Run")
# Resume from 2nd epoch
trainer.load_state_dict({"epoch": 1, "epoch_length": 5, "max_epochs": 2, "rng_states": None})
manual_seed(56)

Original Run
train 1 1 [31, 13, 3, 4]
train 1 2 [23, 18, 6, 16]
train 1 3 [10, 8, 33, 36]
train 1 4 [1, 37, 19, 9]
train 1 5 [20, 30, 14, 26]
train 2 6 [29, 35, 38, 34]
train 2 7 [7, 22, 12, 17]
train 2 8 [25, 21, 24, 15]
train 2 9 [39, 5, 2, 28]
train 2 10 [27, 11, 32, 0]
Resumed Run
train 2 6 [29, 35, 38, 34]
train 2 7 [7, 22, 12, 17]
train 2 8 [25, 21, 24, 15]
train 2 9 [39, 5, 2, 28]
train 2 10 [27, 11, 32, 0]


We can see that the data samples are exactly the same between original and resumed runs.

Complete examples that simulates a crash on a defined iteration and resumes the training from a checkpoint can be found here:

Note

In case when input data is torch.utils.data.DataLoader, previous batches are skipped and the first provided batch corresponds to the batch after the checkpoint iteration. Internally, while resuming, previous datapoint indices are just skipped without fetching the data.

Warning

However, while resuming from iteration, random data augmentations are not synchronized in the middle of the epoch and thus batches remaining until the end of the epoch can be different of those from the initial run.

Warning

However, please, keep in mind that there can be an issue with dataflow synchronization on every epoch if user’s handler synchronizes the random state, for example, by calling periodically torch.manual_seed(seed) during the run. This can have an impact on the dataflow:

def random_train_data_generator():
while True:
yield torch.randint(0, 100, size=(1, ))

trainer = DeterministicEngine(print_train_data)

@trainer.on(Events.ITERATION_COMPLETED(every=3))
def user_handler(_):
# handler synchronizes the random state
torch.manual_seed(12)
a = torch.rand(1)

trainer.run(random_train_data_generator(), max_epochs=3, epoch_length=5);

train 1 1 [32]
train 1 2 [29]
train 1 3 [40]
train 1 4 [3]  <---
train 1 5 [22]
train 2 6 [77]
train 2 7 [3]  <---
train 2 8 [22]
train 2 9 [77]
train 2 10 [3] <---
train 3 11 [22]
train 3 12 [77]
train 3 13 [3] <---
train 3 14 [22]
train 3 15 [77]


Initially, the function random_train_data_generator() generates randomly data batches using the random state set up by trainer. This is intended behaviour until user_handler() is called. After user_handler() execution, random state is altered and thus random_train_data_generator() will produce random batches based on altered random state.

We provide helper decorator keep_random_state() to save and restore random states for torch, numpy and random. Therefore, we can deal with described issue using this decorator:

from ignite.engine.deterministic import keep_random_state

@trainer.on(Events.ITERATION_COMPLETED(every=3))
@keep_random_state
def user_handler(_):
# handler synchronizes the random state
torch.manual_seed(12)
a = torch.rand(1)