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# 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 FineTuneDetails(object):
"""
Details about fine-tuning a custom model.
"""
def __init__(self, **kwargs):
"""
Initializes a new FineTuneDetails object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param training_dataset:
The value to assign to the training_dataset property of this FineTuneDetails.
:type training_dataset: oci.generative_ai.models.Dataset
:param dedicated_ai_cluster_id:
The value to assign to the dedicated_ai_cluster_id property of this FineTuneDetails.
:type dedicated_ai_cluster_id: str
:param training_config:
The value to assign to the training_config property of this FineTuneDetails.
:type training_config: oci.generative_ai.models.TrainingConfig
"""
self.swagger_types = {
'training_dataset': 'Dataset',
'dedicated_ai_cluster_id': 'str',
'training_config': 'TrainingConfig'
}
self.attribute_map = {
'training_dataset': 'trainingDataset',
'dedicated_ai_cluster_id': 'dedicatedAiClusterId',
'training_config': 'trainingConfig'
}
self._training_dataset = None
self._dedicated_ai_cluster_id = None
self._training_config = None
@property
def training_dataset(self):
"""
**[Required]** Gets the training_dataset of this FineTuneDetails.
:return: The training_dataset of this FineTuneDetails.
:rtype: oci.generative_ai.models.Dataset
"""
return self._training_dataset
@training_dataset.setter
def training_dataset(self, training_dataset):
"""
Sets the training_dataset of this FineTuneDetails.
:param training_dataset: The training_dataset of this FineTuneDetails.
:type: oci.generative_ai.models.Dataset
"""
self._training_dataset = training_dataset
@property
def dedicated_ai_cluster_id(self):
"""
**[Required]** Gets the dedicated_ai_cluster_id of this FineTuneDetails.
The OCID of the dedicated AI cluster this fine-tuning runs on.
:return: The dedicated_ai_cluster_id of this FineTuneDetails.
:rtype: str
"""
return self._dedicated_ai_cluster_id
@dedicated_ai_cluster_id.setter
def dedicated_ai_cluster_id(self, dedicated_ai_cluster_id):
"""
Sets the dedicated_ai_cluster_id of this FineTuneDetails.
The OCID of the dedicated AI cluster this fine-tuning runs on.
:param dedicated_ai_cluster_id: The dedicated_ai_cluster_id of this FineTuneDetails.
:type: str
"""
self._dedicated_ai_cluster_id = dedicated_ai_cluster_id
@property
def training_config(self):
"""
Gets the training_config of this FineTuneDetails.
:return: The training_config of this FineTuneDetails.
:rtype: oci.generative_ai.models.TrainingConfig
"""
return self._training_config
@training_config.setter
def training_config(self, training_config):
"""
Sets the training_config of this FineTuneDetails.
:param training_config: The training_config of this FineTuneDetails.
:type: oci.generative_ai.models.TrainingConfig
"""
self._training_config = training_config
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