ignite.contrib.engines#

Contribution module of engines and helper tools:

ignite.contrib.engines.tbptt

`Tbptt_Events`	Aditional tbptt events.
`create_supervised_tbptt_trainer`	Create a trainer for truncated backprop through time supervised models.

ignite.contrib.engines.common

`add_early_stopping_by_val_score`	Method setups early stopping handler based on the score (named by metric_name) provided by evaluator.
`gen_save_best_models_by_val_score`	Method adds a handler to `evaluator` to save `n_saved` of best models based on the metric (named by `metric_name`) provided by `evaluator` (i.e.
`save_best_model_by_val_score`	Method adds a handler to `evaluator` to save on a disk `n_saved` of best models based on the metric (named by `metric_name`) provided by `evaluator` (i.e.
`setup_any_logging`	Deprecated function.
`setup_clearml_logging`	Method to setup ClearML logging on trainer and a list of evaluators.
`setup_common_distrib_training_handlers`	Helper method to setup trainer with common handlers (it also supports distributed configuration):
`setup_common_training_handlers`	Helper method to setup trainer with common handlers (it also supports distributed configuration):
`setup_mlflow_logging`	Method to setup MLflow logging on trainer and a list of evaluators.
`setup_neptune_logging`	Method to setup Neptune logging on trainer and a list of evaluators.
`setup_plx_logging`	Method to setup Polyaxon logging on trainer and a list of evaluators.
`setup_tb_logging`	Method to setup TensorBoard logging on trainer and a list of evaluators.
`setup_trains_logging`	`setup_trains_logging` was renamed to `setup_clearml_logging()`.
`setup_visdom_logging`	Method to setup Visdom logging on trainer and a list of evaluators.
`setup_wandb_logging`	Method to setup WandB logging on trainer and a list of evaluators.

Truncated Backpropagation Throught Time#

class ignite.contrib.engines.tbptt.Tbptt_Events(value)[source]#

Aditional tbptt events.

Additional events for truncated backpropagation throught time dedicated trainer.

ignite.contrib.engines.tbptt.create_supervised_tbptt_trainer(model, optimizer, loss_fn, tbtt_step, dim=0, device=None, non_blocking=False, prepare_batch=<function _prepare_batch>)[source]#

Create a trainer for truncated backprop through time supervised models.

Training recurrent model on long sequences is computationally intensive as it requires to process the whole sequence before getting a gradient. However, when the training loss is computed over many outputs (X to many), there is an opportunity to compute a gradient over a subsequence. This is known as truncated backpropagation through time. This supervised trainer apply gradient optimization step every tbtt_step time steps of the sequence, while backpropagating through the same tbtt_step time steps.

Parameters

model (Module) – the model to train.
optimizer (Optimizer) – the optimizer to use.
loss_fn (Module) – the loss function to use.
tbtt_step (int) – the length of time chunks (last one may be smaller).
dim (int) – axis representing the time dimension.
device (Optional[str]) – device type specification (default: None). Applies to batches.
non_blocking (bool) – if True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect.
prepare_batch (Callable) – function that receives batch, device, non_blocking and outputs tuple of tensors (batch_x, batch_y).

Returns

a trainer engine with supervised update function.

Return type

Engine

Warning

The internal use of device has changed. device will now only be used to move the input data to the correct device. The model should be moved by the user before creating an optimizer.

For more information see:

Helper methods to setup trainer/evaluator#

ignite.contrib.engines.common.add_early_stopping_by_val_score(patience, evaluator, trainer, metric_name, score_sign=1.0)[source]#

Method setups early stopping handler based on the score (named by metric_name) provided by evaluator. Metric value should increase in order to keep training and not early stop.

Parameters

patience (int) – number of events to wait if no improvement and then stop the training.
evaluator (Engine) – evaluation engine used to provide the score
trainer (Engine) – trainer engine to stop the run if no improvement.
metric_name (str) – metric name to use for score evaluation. This metric should be present in evaluator.state.metrics.
score_sign (float) – sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0.

Returns

A EarlyStopping handler.

Return type

EarlyStopping

ignite.contrib.engines.common.gen_save_best_models_by_val_score(save_handler, evaluator, models, metric_name, n_saved=3, trainer=None, tag='val', score_sign=1.0, **kwargs)[source]#

Method adds a handler to evaluator to save n_saved of best models based on the metric (named by metric_name) provided by evaluator (i.e. evaluator.state.metrics[metric_name]). Models with highest metric value will be retained. The logic of how to store objects is delegated to save_handler.

Parameters

save_handler (Union[Callable, BaseSaveHandler]) – Method or callable class to use to save engine and other provided objects. Function receives two objects: checkpoint as a dictionary and filename. If save_handler is callable class, it can inherit of BaseSaveHandler and optionally implement remove method to keep a fixed number of saved checkpoints. In case if user needs to save engine’s checkpoint on a disk, save_handler can be defined with DiskSaver.
evaluator (Engine) – evaluation engine used to provide the score
models (Union[Module, Dict[str, Module]]) – model or dictionary with the object to save. Objects should have implemented state_dict and load_state_dict methods.
metric_name (str) – metric name to use for score evaluation. This metric should be present in evaluator.state.metrics.
n_saved (int) – number of best models to store
trainer (Optional[Engine]) – trainer engine to fetch the epoch when saving the best model.
tag (str) – score name prefix: {tag}_{metric_name}. By default, tag is “val”.
score_sign (float) – sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0.
kwargs (Any) – optional keyword args to be passed to construct Checkpoint.

Returns

A Checkpoint handler.

Return type

Checkpoint

ignite.contrib.engines.common.save_best_model_by_val_score(output_path, evaluator, model, metric_name, n_saved=3, trainer=None, tag='val', score_sign=1.0, **kwargs)[source]#

Method adds a handler to evaluator to save on a disk n_saved of best models based on the metric (named by metric_name) provided by evaluator (i.e. evaluator.state.metrics[metric_name]). Models with highest metric value will be retained.

Parameters

output_path (str) – output path to indicate where to save best models
evaluator (Engine) – evaluation engine used to provide the score
model (Module) – model to store
metric_name (str) – metric name to use for score evaluation. This metric should be present in evaluator.state.metrics.
n_saved (int) – number of best models to store
trainer (Optional[Engine]) – trainer engine to fetch the epoch when saving the best model.
tag (str) – score name prefix: {tag}_{metric_name}. By default, tag is “val”.
score_sign (float) – sign of the score: 1.0 or -1.0. For error-like metrics, e.g. smaller is better, a negative score sign should be used (objects with larger score are retained). Default, 1.0.
kwargs (Any) – optional keyword args to be passed to construct Checkpoint.

Returns

A Checkpoint handler.

Return type

Checkpoint

ignite.contrib.engines.common.setup_any_logging(logger, logger_module, trainer, optimizers, evaluators, log_every_iters)[source]#

Deprecated function.

Deprecated since version 0.4.0:

Please use instead: setup_tb_logging, setup_visdom_logging or setup_mlflow_logging etc.

Parameters

logger (BaseLogger) –
logger_module (Any) –
trainer (Engine) –
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer], Dict[None, Optimizer]]]) –
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) –
log_every_iters (int) –

Return type

None

ignite.contrib.engines.common.setup_clearml_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup ClearML logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

ClearMLLogger

Return type

ClearMLLogger

ignite.contrib.engines.common.setup_common_distrib_training_handlers(trainer, train_sampler=None, to_save=None, save_every_iters=1000, output_path=None, lr_scheduler=None, with_gpu_stats=False, output_names=None, with_pbars=True, with_pbar_on_iters=True, log_every_iters=100, stop_on_nan=True, clear_cuda_cache=True, save_handler=None, **kwargs)#

Helper method to setup trainer with common handlers (it also supports distributed configuration):

TerminateOnNan

handler to setup learning rate scheduling

ModelCheckpoint

RunningAverage on update_function output

Two progress bars on epochs and optionally on iterations

Parameters

trainer (Engine) – trainer engine. Output of trainer’s update_function should be a dictionary or sequence or a single tensor.
train_sampler (Optional[DistributedSampler]) – Optional distributed sampler used to call set_epoch method on epoch started event.
to_save (Optional[Mapping]) – dictionary with objects to save in the checkpoint. This argument is passed to Checkpoint instance.
save_every_iters (int) – saving interval. By default, to_save objects are stored each 1000 iterations.
output_path (Optional[str]) – output path to indicate where to_save objects are stored. Argument is mutually exclusive with save_handler.
lr_scheduler (Optional[Union[ParamScheduler, LRScheduler]]) – learning rate scheduler as native torch LRScheduler or ignite’s parameter scheduler.
with_gpu_stats (bool) – if True, GpuInfo is attached to the trainer. This requires pynvml<12 package to be installed.
output_names (Optional[Iterable[str]]) – list of names associated with update_function output dictionary.
with_pbars (bool) – if True, two progress bars on epochs and optionally on iterations are attached. Default, True.
with_pbar_on_iters (bool) – if True, a progress bar on iterations is attached to the trainer. Default, True.
log_every_iters (int) – logging interval for GpuInfo and for epoch-wise progress bar. Default, 100.
stop_on_nan (bool) – if True, TerminateOnNan handler is added to the trainer. Default, True.
clear_cuda_cache (bool) – if True, torch.cuda.empty_cache() is called every end of epoch. Default, True.
save_handler (Optional[Union[Callable, BaseSaveHandler]]) – Method or callable class to use to store to_save. See Checkpoint for more details. Argument is mutually exclusive with output_path.
kwargs (Any) – optional keyword args to be passed to construct Checkpoint.

Return type

None

ignite.contrib.engines.common.setup_common_training_handlers(trainer, train_sampler=None, to_save=None, save_every_iters=1000, output_path=None, lr_scheduler=None, with_gpu_stats=False, output_names=None, with_pbars=True, with_pbar_on_iters=True, log_every_iters=100, stop_on_nan=True, clear_cuda_cache=True, save_handler=None, **kwargs)[source]#

Helper method to setup trainer with common handlers (it also supports distributed configuration):

TerminateOnNan

handler to setup learning rate scheduling

ModelCheckpoint

RunningAverage on update_function output

Two progress bars on epochs and optionally on iterations

Parameters

trainer (Engine) – trainer engine. Output of trainer’s update_function should be a dictionary or sequence or a single tensor.
train_sampler (Optional[DistributedSampler]) – Optional distributed sampler used to call set_epoch method on epoch started event.
to_save (Optional[Mapping]) – dictionary with objects to save in the checkpoint. This argument is passed to Checkpoint instance.
save_every_iters (int) – saving interval. By default, to_save objects are stored each 1000 iterations.
output_path (Optional[str]) – output path to indicate where to_save objects are stored. Argument is mutually exclusive with save_handler.
lr_scheduler (Optional[Union[ParamScheduler, LRScheduler]]) – learning rate scheduler as native torch LRScheduler or ignite’s parameter scheduler.
with_gpu_stats (bool) – if True, GpuInfo is attached to the trainer. This requires pynvml<12 package to be installed.
output_names (Optional[Iterable[str]]) – list of names associated with update_function output dictionary.
with_pbars (bool) – if True, two progress bars on epochs and optionally on iterations are attached. Default, True.
with_pbar_on_iters (bool) – if True, a progress bar on iterations is attached to the trainer. Default, True.
log_every_iters (int) – logging interval for GpuInfo and for epoch-wise progress bar. Default, 100.
stop_on_nan (bool) – if True, TerminateOnNan handler is added to the trainer. Default, True.
clear_cuda_cache (bool) – if True, torch.cuda.empty_cache() is called every end of epoch. Default, True.
save_handler (Optional[Union[Callable, BaseSaveHandler]]) – Method or callable class to use to store to_save. See Checkpoint for more details. Argument is mutually exclusive with output_path.
kwargs (Any) – optional keyword args to be passed to construct Checkpoint.

Return type

None

ignite.contrib.engines.common.setup_mlflow_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup MLflow logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

MLflowLogger

Return type

MLflowLogger

ignite.contrib.engines.common.setup_neptune_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup Neptune logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

NeptuneLogger

Return type

NeptuneLogger

ignite.contrib.engines.common.setup_plx_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup Polyaxon logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

PolyaxonLogger

Return type

PolyaxonLogger

ignite.contrib.engines.common.setup_tb_logging(output_path, trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup TensorBoard logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

output_path (str) – logging directory path
trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

TensorboardLogger

Return type

TensorboardLogger

ignite.contrib.engines.common.setup_trains_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

setup_trains_logging was renamed to setup_clearml_logging().

Parameters

trainer (Engine) –
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) –
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) –
log_every_iters (int) –
kwargs (Any) –

Return type

ClearMLLogger

ignite.contrib.engines.common.setup_visdom_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup Visdom logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

VisdomLogger

Return type

VisdomLogger

ignite.contrib.engines.common.setup_wandb_logging(trainer, optimizers=None, evaluators=None, log_every_iters=100, **kwargs)[source]#

Method to setup WandB logging on trainer and a list of evaluators. Logged metrics are:

Training metrics, e.g. running average loss values

Learning rate(s)

Evaluation metrics

Parameters

trainer (Engine) – trainer engine
optimizers (Optional[Union[Optimizer, Dict[str, Optimizer]]]) – single or dictionary of torch optimizers. If a dictionary, keys are used as tags arguments for logging.
evaluators (Optional[Union[Engine, Dict[str, Engine]]]) – single or dictionary of evaluators. If a dictionary, keys are used as tags arguments for logging.
log_every_iters (int) – interval for loggers attached to iteration events. To log every iteration, value can be set to 1 or None.
kwargs (Any) – optional keyword args to be passed to construct the logger.

Returns

WandBLogger

Return type

WandBLogger