gluonts.mx.trainer.callback module#
- class gluonts.mx.trainer.callback.Callback[source]#
Bases:
objectAbstract Callback base class.
Callbacks control the training of the GluonTS trainer. To write a custom Callback, you can subclass Callback and overwrite one or more of the hook methods. Hook methods with boolean return value stop the training if False is returned.
- on_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer, best_epoch_info: Dict[str, Any], ctx: mxnet.context.Context) bool[source]#
Hook that is called after every epoch. As on_train_epoch_end and on_validation_epoch_end, it returns a boolean whether training should continue. This hook is always called after on_train_epoch_end and on_validation_epoch_end. It is called regardless of these hooks’ return values.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch if validation data was provided. The training loss otherwise.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
best_epoch_info – Aggregate information about the best epoch. Contains keys params_path, epoch_no and score. The score is the best validation loss if validation data is provided or the best training loss otherwise.
ctx – The MXNet context used.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_network_initializing_end(training_network: mxnet.gluon.block.HybridBlock) None[source]#
Hook that is called prior to training, after the training network has been initialized. This is the first hook where the network is passed.
- Parameters
training_network – The network that is being trained.
- on_train_batch_end(training_network: mxnet.gluon.block.HybridBlock) bool[source]#
Hook that is called after each training batch.
- Parameters
training_network – The network that is being trained.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_end(training_network: mxnet.gluon.block.HybridBlock, temporary_dir: str, ctx: Optional[mxnet.context.Context] = None) None[source]#
Hook that is called after training is finished. This is the last hook to be called.
- Parameters
training_network – The network that was trained.
temporary_dir – The directory where model parameters are logged throughout training.
ctx – An MXNet context used.
- on_train_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer) bool[source]#
Hook that is called after each training epoch. This method returns a boolean whether training should continue.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_epoch_start(training_network: mxnet.gluon.block.HybridBlock) None[source]#
Hook that is called prior to each training epoch.
- Parameters
training_network – The network that is being trained.
- on_train_start(max_epochs: int) None[source]#
Hook that is called prior to training. This is the very first hook to be called.
- Parameters
max_epochs – The maximum number of epochs that training is running. The actual number of epochs may be fewer if another callback hook stops training early.
- on_validation_batch_end(training_network: mxnet.gluon.block.HybridBlock) bool[source]#
Hook that is called after each validation batch. This hook is never called if no validation data is available during training.
- Parameters
training_network – The network that is being trained.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_validation_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer) bool[source]#
Hook that is called after each validation epoch. Similar to on_train_epoch_end, this method returns a boolean whether training should continue. Note that it is always called after on_train_epoch_end within a single epoch. If on_train_epoch_end returned False, this method will not be called.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- class gluonts.mx.trainer.callback.CallbackList(callbacks: List[gluonts.mx.trainer.callback.Callback])[source]#
Bases:
gluonts.mx.trainer.callback.CallbackUsed to chain a list of callbacks to one Callback. Boolean hook methods are logically joined with AND, meaning that if at least one callback method returns False, the training is stopped.
- callbacks#
A list of gluonts.mx.trainer.callback.Callback’s.
- on_epoch_end(*args: Any, **kwargs: Any) bool[source]#
Hook that is called after every epoch. As on_train_epoch_end and on_validation_epoch_end, it returns a boolean whether training should continue. This hook is always called after on_train_epoch_end and on_validation_epoch_end. It is called regardless of these hooks’ return values.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch if validation data was provided. The training loss otherwise.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
best_epoch_info – Aggregate information about the best epoch. Contains keys params_path, epoch_no and score. The score is the best validation loss if validation data is provided or the best training loss otherwise.
ctx – The MXNet context used.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_network_initializing_end(*args: Any, **kwargs: Any) None[source]#
Hook that is called prior to training, after the training network has been initialized. This is the first hook where the network is passed.
- Parameters
training_network – The network that is being trained.
- on_train_batch_end(*args: Any, **kwargs: Any) bool[source]#
Hook that is called after each training batch.
- Parameters
training_network – The network that is being trained.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_end(*args: Any, **kwargs: Any) None[source]#
Hook that is called after training is finished. This is the last hook to be called.
- Parameters
training_network – The network that was trained.
temporary_dir – The directory where model parameters are logged throughout training.
ctx – An MXNet context used.
- on_train_epoch_end(*args: Any, **kwargs: Any) bool[source]#
Hook that is called after each training epoch. This method returns a boolean whether training should continue.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_epoch_start(*args: Any, **kwargs: Any) None[source]#
Hook that is called prior to each training epoch.
- Parameters
training_network – The network that is being trained.
- on_train_start(*args: Any, **kwargs: Any) None[source]#
Hook that is called prior to training. This is the very first hook to be called.
- Parameters
max_epochs – The maximum number of epochs that training is running. The actual number of epochs may be fewer if another callback hook stops training early.
- on_validation_batch_end(*args: Any, **kwargs: Any) bool[source]#
Hook that is called after each validation batch. This hook is never called if no validation data is available during training.
- Parameters
training_network – The network that is being trained.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_validation_epoch_end(*args: Any, **kwargs: Any) bool[source]#
Hook that is called after each validation epoch. Similar to on_train_epoch_end, this method returns a boolean whether training should continue. Note that it is always called after on_train_epoch_end within a single epoch. If on_train_epoch_end returned False, this method will not be called.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- class gluonts.mx.trainer.callback.TerminateOnNaN[source]#
Bases:
gluonts.mx.trainer.callback.Callback- on_train_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer) bool[source]#
Hook that is called after each training epoch. This method returns a boolean whether training should continue.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- class gluonts.mx.trainer.callback.TrainingHistory[source]#
Bases:
gluonts.mx.trainer.callback.Callback- on_train_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer) bool[source]#
Hook that is called after each training epoch. This method returns a boolean whether training should continue.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_validation_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer) bool[source]#
Hook that is called after each validation epoch. Similar to on_train_epoch_end, this method returns a boolean whether training should continue. Note that it is always called after on_train_epoch_end within a single epoch. If on_train_epoch_end returned False, this method will not be called.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- class gluonts.mx.trainer.callback.TrainingTimeLimit(*, time_limit: float, stop_within_epoch: bool = False)[source]#
Bases:
pydantic.v1.main.BaseModel,gluonts.mx.trainer.callback.CallbackLimit time spent for training.
This is useful when ensuring that training for a given model doesn’t exceed a budget, for example when doing AutoML.
If stop_within_epoch is set to true, training can be stopped after each batch, otherwise it stops after the end of the epoch.
- on_epoch_end(epoch_no: int, epoch_loss: float, training_network: mxnet.gluon.block.HybridBlock, trainer: mxnet.gluon.trainer.Trainer, best_epoch_info: Dict[str, Any], ctx: mxnet.context.Context) bool[source]#
Hook that is called after every epoch. As on_train_epoch_end and on_validation_epoch_end, it returns a boolean whether training should continue. This hook is always called after on_train_epoch_end and on_validation_epoch_end. It is called regardless of these hooks’ return values.
- Parameters
epoch_no – The current epoch (the first epoch has epoch_no = 0).
epoch_loss – The validation loss that was recorded in the last epoch if validation data was provided. The training loss otherwise.
training_network – The network that is being trained.
trainer – The trainer which is running the training.
best_epoch_info – Aggregate information about the best epoch. Contains keys params_path, epoch_no and score. The score is the best validation loss if validation data is provided or the best training loss otherwise.
ctx – The MXNet context used.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_batch_end(training_network: mxnet.gluon.block.HybridBlock) bool[source]#
Hook that is called after each training batch.
- Parameters
training_network – The network that is being trained.
- Returns
A boolean whether the training should continue. Defaults to True.
- Return type
bool
- on_train_start(max_epochs: int) None[source]#
Hook that is called prior to training. This is the very first hook to be called.
- Parameters
max_epochs – The maximum number of epochs that training is running. The actual number of epochs may be fewer if another callback hook stops training early.
- stop_within_epoch: bool#
- time_limit: float#