xlstm_jax.trainer.callbacks.callback ==================================== .. py:module:: xlstm_jax.trainer.callbacks.callback Classes ------- .. autoapisummary:: xlstm_jax.trainer.callbacks.callback.CallbackConfig xlstm_jax.trainer.callbacks.callback.Callback Module Contents --------------- .. py:class:: CallbackConfig Bases: :py:obj:`xlstm_jax.configs.ConfigDict` Base configuration of a callback. .. py:attribute:: every_n_epochs :type: int :value: 1 If the callback implements functions on a per-epoch basis (e.g. `on_training_epoch_start`, `on_training_epoch_end`, `on_validation_epoch_start`), this parameter specifies the frequency of calling these functions. .. py:attribute:: every_n_steps :type: int :value: -1 If the callback implements functions on a per-step basis (e.g. `on_training_step`), this parameter specifies the frequency of calling these functions. .. py:attribute:: main_process_only :type: bool :value: False Whether to call the callback only in the main process. .. py:method:: create(trainer, data_module = None) Creates the callback object. :param trainer: Trainer object. :param data_module: Data module object. :type data_module: optional :returns: Callback object. .. py:method:: get(key, default=None) .. py:method:: to_dict() Converts the config to a dictionary. Helpful for saving to disk or logging. .. py:method:: from_dict(config_class, data, strict_classname_parsing = False, ignore_extensive_attributes = True, none_to_zero_for_ints = False) :staticmethod: Utility for parsing dictionaries back into a nested dataclass structure, including arbitrary classes and types. Currently, this is tailored towards the current logging system with the "hardly" invertible to_dict. :param config_class: Typically a dataclass, but can be any other type as well If it is another type, the parser tries to create an object via config_class(**data) if data is a dictionary or config_class(data) else. :param data: Typically a dictionary that contains attributes of the dataclass. Can be any other kind of data. :param strict_classname_parsing: Parse class names strictly. :param ignore_extensive_attributes: Ignore attributes that are not defined in the dataclass. :param none_to_zero_for_ints: Convert None to 0 for integer types. :returns: An object of type `config_class` that contains the data as attributes. .. py:class:: Callback(config, trainer, data_module = None) Base class for callbacks. Callbacks are used to perform additional actions during training, validation, and testing. We provide a set of predefined functions, that can be overridden by subclasses to implement custom behavior. The predefined functions are called at the beginning and end of training, validation, and testing, as well as at the beginning and end of each epoch or step. Note: all counts of epoch index and step index are starting at 1 (i.e. the first epoch is 1 instead of 0). :param config: Configuration dictionary. :param trainer: Trainer object. :param data_module: Data module object. :type data_module: optional .. py:attribute:: config .. py:attribute:: trainer .. py:attribute:: data_module :value: None .. py:attribute:: _every_n_epochs .. py:attribute:: _every_n_steps .. py:attribute:: _main_process_only .. py:attribute:: _active_on_epochs .. py:attribute:: _active_on_steps .. py:method:: on_training_start() Called at the beginning of training. .. py:method:: on_training_end() Called at the end of training. .. py:method:: on_training_epoch_start(epoch_idx) Called at the beginning of each training epoch. :param epoch_idx: Index of the current epoch. .. py:method:: on_filtered_training_epoch_start(epoch_idx) Called at the beginning of each `every_n_epochs` training epoch. To be implemented by subclasses. :param epoch_idx: Index of the current epoch. .. py:method:: on_training_epoch_end(train_metrics, epoch_idx) Called at the end of each training epoch. :param train_metrics: Dictionary of training metrics of the current epoch. :param epoch_idx: Index of the current epoch. .. py:method:: on_filtered_training_epoch_end(train_metrics, epoch_idx) Called at the end of each `every_n_epochs` training epoch. To be implemented by subclasses. :param train_metrics: Dictionary of training metrics of the current epoch. :param epoch_idx: Index of the current epoch. .. py:method:: on_training_step(step_metrics, epoch_idx, step_idx) Called at the end of each training step. :param step_metrics: Dictionary of training metrics of the current step. :param epoch_idx: Index of the current epoch. :param step_idx: Index of the current step. .. py:method:: on_filtered_training_step(step_metrics, epoch_idx, step_idx) Called at the end of each `every_n_steps` training step. To be implemented by subclasses. :param step_metrics: Dictionary of training metrics of the current step. :param epoch_idx: Index of the current epo. :param step_idx: Index of the current step. .. py:method:: on_validation_epoch_start(epoch_idx, step_idx) Called at the beginning of validation. :param epoch_idx: Index of the current training epoch. :param step_idx: Index of the current training step. .. py:method:: on_filtered_validation_epoch_start(epoch_idx, step_idx) Called at the beginning of `every_n_epochs` validation. To be implemented by subclasses. :param epoch_idx: Index of the current training epoch. :param step_idx: Index of the current training step. .. py:method:: on_validation_epoch_end(eval_metrics, epoch_idx, step_idx) Called at the end of each validation epoch. :param eval_metrics: Dictionary of evaluation metrics of the current epoch. :param epoch_idx: Index of the current training epoch. :param step_idx: Index of the current training step. .. py:method:: on_filtered_validation_epoch_end(eval_metrics, epoch_idx, step_idx) Called at the end of each `every_n_epochs` validation epoch. To be implemented by subclasses. :param eval_metrics: Dictionary of evaluation metrics of the current epoch. :param epoch_idx: Index of the current training epoch. :param step_idx: Index of the current training step. .. py:method:: on_test_epoch_start(epoch_idx) Called at the beginning of testing. To be implemented by subclasses. :param epoch_idx: Index of the current epoch. .. py:method:: on_test_epoch_end(test_metrics, epoch_idx) Called at the end of each test epoch. To be implemented by subclasses. :param test_metrics: Dictionary of test metrics of the current epoch. :param epoch_idx: Index of the current epoch. .. py:method:: set_dataset(data_module) Sets the data module. :param data_module: Data module object. .. py:method:: finalize(status = None) Called at the end of the whole training process. To be implemented by subclasses. :param status: Status of the training process.