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Edit: training_config.py
# coding: utf-8 # Copyright (c) 2016, 2024, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. # NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20231130 from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class TrainingConfig(object): """ The fine-tuning method and hyperparameters used for fine-tuning a custom model. """ #: A constant which can be used with the training_config_type property of a TrainingConfig. #: This constant has a value of "TFEW_TRAINING_CONFIG" TRAINING_CONFIG_TYPE_TFEW_TRAINING_CONFIG = "TFEW_TRAINING_CONFIG" #: A constant which can be used with the training_config_type property of a TrainingConfig. #: This constant has a value of "VANILLA_TRAINING_CONFIG" TRAINING_CONFIG_TYPE_VANILLA_TRAINING_CONFIG = "VANILLA_TRAINING_CONFIG" #: A constant which can be used with the training_config_type property of a TrainingConfig. #: This constant has a value of "LORA_TRAINING_CONFIG" TRAINING_CONFIG_TYPE_LORA_TRAINING_CONFIG = "LORA_TRAINING_CONFIG" def __init__(self, **kwargs): """ Initializes a new TrainingConfig object with values from keyword arguments. This class has the following subclasses and if you are using this class as input to a service operations then you should favor using a subclass over the base class: * :class:`~oci.generative_ai.models.LoraTrainingConfig` * :class:`~oci.generative_ai.models.VanillaTrainingConfig` * :class:`~oci.generative_ai.models.TFewTrainingConfig` The following keyword arguments are supported (corresponding to the getters/setters of this class): :param training_config_type: The value to assign to the training_config_type property of this TrainingConfig. Allowed values for this property are: "TFEW_TRAINING_CONFIG", "VANILLA_TRAINING_CONFIG", "LORA_TRAINING_CONFIG", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type training_config_type: str :param total_training_epochs: The value to assign to the total_training_epochs property of this TrainingConfig. :type total_training_epochs: int :param learning_rate: The value to assign to the learning_rate property of this TrainingConfig. :type learning_rate: float :param training_batch_size: The value to assign to the training_batch_size property of this TrainingConfig. :type training_batch_size: int :param early_stopping_patience: The value to assign to the early_stopping_patience property of this TrainingConfig. :type early_stopping_patience: int :param early_stopping_threshold: The value to assign to the early_stopping_threshold property of this TrainingConfig. :type early_stopping_threshold: float :param log_model_metrics_interval_in_steps: The value to assign to the log_model_metrics_interval_in_steps property of this TrainingConfig. :type log_model_metrics_interval_in_steps: int """ self.swagger_types = { 'training_config_type': 'str', 'total_training_epochs': 'int', 'learning_rate': 'float', 'training_batch_size': 'int', 'early_stopping_patience': 'int', 'early_stopping_threshold': 'float', 'log_model_metrics_interval_in_steps': 'int' } self.attribute_map = { 'training_config_type': 'trainingConfigType', 'total_training_epochs': 'totalTrainingEpochs', 'learning_rate': 'learningRate', 'training_batch_size': 'trainingBatchSize', 'early_stopping_patience': 'earlyStoppingPatience', 'early_stopping_threshold': 'earlyStoppingThreshold', 'log_model_metrics_interval_in_steps': 'logModelMetricsIntervalInSteps' } self._training_config_type = None self._total_training_epochs = None self._learning_rate = None self._training_batch_size = None self._early_stopping_patience = None self._early_stopping_threshold = None self._log_model_metrics_interval_in_steps = None @staticmethod def get_subtype(object_dictionary): """ Given the hash representation of a subtype of this class, use the info in the hash to return the class of the subtype. """ type = object_dictionary['trainingConfigType'] if type == 'LORA_TRAINING_CONFIG': return 'LoraTrainingConfig' if type == 'VANILLA_TRAINING_CONFIG': return 'VanillaTrainingConfig' if type == 'TFEW_TRAINING_CONFIG': return 'TFewTrainingConfig' else: return 'TrainingConfig' @property def training_config_type(self): """ **[Required]** Gets the training_config_type of this TrainingConfig. The fine-tuning method for training a custom model. Allowed values for this property are: "TFEW_TRAINING_CONFIG", "VANILLA_TRAINING_CONFIG", "LORA_TRAINING_CONFIG", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The training_config_type of this TrainingConfig. :rtype: str """ return self._training_config_type @training_config_type.setter def training_config_type(self, training_config_type): """ Sets the training_config_type of this TrainingConfig. The fine-tuning method for training a custom model. :param training_config_type: The training_config_type of this TrainingConfig. :type: str """ allowed_values = ["TFEW_TRAINING_CONFIG", "VANILLA_TRAINING_CONFIG", "LORA_TRAINING_CONFIG"] if not value_allowed_none_or_none_sentinel(training_config_type, allowed_values): training_config_type = 'UNKNOWN_ENUM_VALUE' self._training_config_type = training_config_type @property def total_training_epochs(self): """ Gets the total_training_epochs of this TrainingConfig. The maximum number of training epochs to run for. :return: The total_training_epochs of this TrainingConfig. :rtype: int """ return self._total_training_epochs @total_training_epochs.setter def total_training_epochs(self, total_training_epochs): """ Sets the total_training_epochs of this TrainingConfig. The maximum number of training epochs to run for. :param total_training_epochs: The total_training_epochs of this TrainingConfig. :type: int """ self._total_training_epochs = total_training_epochs @property def learning_rate(self): """ Gets the learning_rate of this TrainingConfig. The initial learning rate to be used during training :return: The learning_rate of this TrainingConfig. :rtype: float """ return self._learning_rate @learning_rate.setter def learning_rate(self, learning_rate): """ Sets the learning_rate of this TrainingConfig. The initial learning rate to be used during training :param learning_rate: The learning_rate of this TrainingConfig. :type: float """ self._learning_rate = learning_rate @property def training_batch_size(self): """ Gets the training_batch_size of this TrainingConfig. The batch size used during training. :return: The training_batch_size of this TrainingConfig. :rtype: int """ return self._training_batch_size @training_batch_size.setter def training_batch_size(self, training_batch_size): """ Sets the training_batch_size of this TrainingConfig. The batch size used during training. :param training_batch_size: The training_batch_size of this TrainingConfig. :type: int """ self._training_batch_size = training_batch_size @property def early_stopping_patience(self): """ Gets the early_stopping_patience of this TrainingConfig. Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation. :return: The early_stopping_patience of this TrainingConfig. :rtype: int """ return self._early_stopping_patience @early_stopping_patience.setter def early_stopping_patience(self, early_stopping_patience): """ Sets the early_stopping_patience of this TrainingConfig. Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation. :param early_stopping_patience: The early_stopping_patience of this TrainingConfig. :type: int """ self._early_stopping_patience = early_stopping_patience @property def early_stopping_threshold(self): """ Gets the early_stopping_threshold of this TrainingConfig. How much the loss must improve to prevent early stopping. :return: The early_stopping_threshold of this TrainingConfig. :rtype: float """ return self._early_stopping_threshold @early_stopping_threshold.setter def early_stopping_threshold(self, early_stopping_threshold): """ Sets the early_stopping_threshold of this TrainingConfig. How much the loss must improve to prevent early stopping. :param early_stopping_threshold: The early_stopping_threshold of this TrainingConfig. :type: float """ self._early_stopping_threshold = early_stopping_threshold @property def log_model_metrics_interval_in_steps(self): """ Gets the log_model_metrics_interval_in_steps of this TrainingConfig. Determines how frequently to log model metrics. Every step is logged for the first 20 steps and then follows this parameter for log frequency. Set to 0 to disable logging the model metrics. :return: The log_model_metrics_interval_in_steps of this TrainingConfig. :rtype: int """ return self._log_model_metrics_interval_in_steps @log_model_metrics_interval_in_steps.setter def log_model_metrics_interval_in_steps(self, log_model_metrics_interval_in_steps): """ Sets the log_model_metrics_interval_in_steps of this TrainingConfig. Determines how frequently to log model metrics. Every step is logged for the first 20 steps and then follows this parameter for log frequency. Set to 0 to disable logging the model metrics. :param log_model_metrics_interval_in_steps: The log_model_metrics_interval_in_steps of this TrainingConfig. :type: int """ self._log_model_metrics_interval_in_steps = log_model_metrics_interval_in_steps def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other