# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Code generated by the Google Gen AI SDK generator DO NOT EDIT.
from typing import Optional, Union
from urllib.parse import urlencode
from . import _common
from . import _transformers as t
from . import types
from ._api_client import ApiClient
from ._common import get_value_by_path as getv
from ._common import set_value_by_path as setv
from .pagers import AsyncPager, Pager
def _GetTuningJobConfig_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
return to_object
def _GetTuningJobConfig_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
return to_object
def _GetTuningJobParameters_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['name']) is not None:
setv(to_object, ['_url', 'name'], getv(from_object, ['name']))
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_GetTuningJobConfig_to_mldev(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _GetTuningJobParameters_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['name']) is not None:
setv(to_object, ['_url', 'name'], getv(from_object, ['name']))
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_GetTuningJobConfig_to_vertex(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _ListTuningJobsConfig_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['page_size']) is not None:
setv(
parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
)
if getv(from_object, ['page_token']) is not None:
setv(
parent_object,
['_query', 'pageToken'],
getv(from_object, ['page_token']),
)
if getv(from_object, ['filter']) is not None:
setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
return to_object
def _ListTuningJobsConfig_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['page_size']) is not None:
setv(
parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
)
if getv(from_object, ['page_token']) is not None:
setv(
parent_object,
['_query', 'pageToken'],
getv(from_object, ['page_token']),
)
if getv(from_object, ['filter']) is not None:
setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
return to_object
def _ListTuningJobsParameters_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_ListTuningJobsConfig_to_mldev(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _ListTuningJobsParameters_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_ListTuningJobsConfig_to_vertex(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _TuningExample_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['text_input']) is not None:
setv(to_object, ['textInput'], getv(from_object, ['text_input']))
if getv(from_object, ['output']) is not None:
setv(to_object, ['output'], getv(from_object, ['output']))
return to_object
def _TuningExample_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['text_input']) is not None:
raise ValueError('text_input parameter is not supported in Vertex AI.')
if getv(from_object, ['output']) is not None:
raise ValueError('output parameter is not supported in Vertex AI.')
return to_object
def _TuningDataset_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
raise ValueError('gcs_uri parameter is not supported in Google AI.')
if getv(from_object, ['examples']) is not None:
setv(
to_object,
['examples', 'examples'],
[
_TuningExample_to_mldev(api_client, item, to_object)
for item in getv(from_object, ['examples'])
],
)
return to_object
def _TuningDataset_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
setv(
parent_object,
['supervisedTuningSpec', 'trainingDatasetUri'],
getv(from_object, ['gcs_uri']),
)
if getv(from_object, ['examples']) is not None:
raise ValueError('examples parameter is not supported in Vertex AI.')
return to_object
def _TuningValidationDataset_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
raise ValueError('gcs_uri parameter is not supported in Google AI.')
return to_object
def _TuningValidationDataset_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
setv(to_object, ['validationDatasetUri'], getv(from_object, ['gcs_uri']))
return to_object
def _CreateTuningJobConfig_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
if getv(from_object, ['validation_dataset']) is not None:
raise ValueError(
'validation_dataset parameter is not supported in Google AI.'
)
if getv(from_object, ['tuned_model_display_name']) is not None:
setv(
parent_object,
['displayName'],
getv(from_object, ['tuned_model_display_name']),
)
if getv(from_object, ['description']) is not None:
raise ValueError('description parameter is not supported in Google AI.')
if getv(from_object, ['epoch_count']) is not None:
setv(
parent_object,
['tuningTask', 'hyperparameters', 'epochCount'],
getv(from_object, ['epoch_count']),
)
if getv(from_object, ['learning_rate_multiplier']) is not None:
setv(
to_object,
['tuningTask', 'hyperparameters', 'learningRateMultiplier'],
getv(from_object, ['learning_rate_multiplier']),
)
if getv(from_object, ['adapter_size']) is not None:
raise ValueError('adapter_size parameter is not supported in Google AI.')
if getv(from_object, ['batch_size']) is not None:
setv(
parent_object,
['tuningTask', 'hyperparameters', 'batchSize'],
getv(from_object, ['batch_size']),
)
if getv(from_object, ['learning_rate']) is not None:
setv(
parent_object,
['tuningTask', 'hyperparameters', 'learningRate'],
getv(from_object, ['learning_rate']),
)
return to_object
def _CreateTuningJobConfig_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
if getv(from_object, ['validation_dataset']) is not None:
setv(
parent_object,
['supervisedTuningSpec'],
_TuningValidationDataset_to_vertex(
api_client, getv(from_object, ['validation_dataset']), to_object
),
)
if getv(from_object, ['tuned_model_display_name']) is not None:
setv(
parent_object,
['tunedModelDisplayName'],
getv(from_object, ['tuned_model_display_name']),
)
if getv(from_object, ['description']) is not None:
setv(parent_object, ['description'], getv(from_object, ['description']))
if getv(from_object, ['epoch_count']) is not None:
setv(
parent_object,
['supervisedTuningSpec', 'hyperParameters', 'epochCount'],
getv(from_object, ['epoch_count']),
)
if getv(from_object, ['learning_rate_multiplier']) is not None:
setv(
to_object,
['supervisedTuningSpec', 'hyperParameters', 'learningRateMultiplier'],
getv(from_object, ['learning_rate_multiplier']),
)
if getv(from_object, ['adapter_size']) is not None:
setv(
parent_object,
['supervisedTuningSpec', 'hyperParameters', 'adapterSize'],
getv(from_object, ['adapter_size']),
)
if getv(from_object, ['batch_size']) is not None:
raise ValueError('batch_size parameter is not supported in Vertex AI.')
if getv(from_object, ['learning_rate']) is not None:
raise ValueError('learning_rate parameter is not supported in Vertex AI.')
return to_object
def _CreateTuningJobParameters_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['base_model']) is not None:
setv(to_object, ['baseModel'], getv(from_object, ['base_model']))
if getv(from_object, ['training_dataset']) is not None:
setv(
to_object,
['tuningTask', 'trainingData'],
_TuningDataset_to_mldev(
api_client, getv(from_object, ['training_dataset']), to_object
),
)
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_CreateTuningJobConfig_to_mldev(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _CreateTuningJobParameters_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['base_model']) is not None:
setv(to_object, ['baseModel'], getv(from_object, ['base_model']))
if getv(from_object, ['training_dataset']) is not None:
setv(
to_object,
['supervisedTuningSpec', 'trainingDatasetUri'],
_TuningDataset_to_vertex(
api_client, getv(from_object, ['training_dataset']), to_object
),
)
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_CreateTuningJobConfig_to_vertex(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _DistillationDataset_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
raise ValueError('gcs_uri parameter is not supported in Google AI.')
return to_object
def _DistillationDataset_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
setv(
parent_object,
['distillationSpec', 'trainingDatasetUri'],
getv(from_object, ['gcs_uri']),
)
return to_object
def _DistillationValidationDataset_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
raise ValueError('gcs_uri parameter is not supported in Google AI.')
return to_object
def _DistillationValidationDataset_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['gcs_uri']) is not None:
setv(to_object, ['validationDatasetUri'], getv(from_object, ['gcs_uri']))
return to_object
def _CreateDistillationJobConfig_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
if getv(from_object, ['validation_dataset']) is not None:
raise ValueError(
'validation_dataset parameter is not supported in Google AI.'
)
if getv(from_object, ['tuned_model_display_name']) is not None:
setv(
parent_object,
['displayName'],
getv(from_object, ['tuned_model_display_name']),
)
if getv(from_object, ['epoch_count']) is not None:
setv(
parent_object,
['tuningTask', 'hyperparameters', 'epochCount'],
getv(from_object, ['epoch_count']),
)
if getv(from_object, ['learning_rate_multiplier']) is not None:
setv(
parent_object,
['tuningTask', 'hyperparameters', 'learningRateMultiplier'],
getv(from_object, ['learning_rate_multiplier']),
)
if getv(from_object, ['adapter_size']) is not None:
raise ValueError('adapter_size parameter is not supported in Google AI.')
if getv(from_object, ['pipeline_root_directory']) is not None:
raise ValueError(
'pipeline_root_directory parameter is not supported in Google AI.'
)
return to_object
def _CreateDistillationJobConfig_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['http_options']) is not None:
setv(to_object, ['httpOptions'], getv(from_object, ['http_options']))
if getv(from_object, ['validation_dataset']) is not None:
setv(
parent_object,
['distillationSpec'],
_DistillationValidationDataset_to_vertex(
api_client, getv(from_object, ['validation_dataset']), to_object
),
)
if getv(from_object, ['tuned_model_display_name']) is not None:
setv(
parent_object,
['tunedModelDisplayName'],
getv(from_object, ['tuned_model_display_name']),
)
if getv(from_object, ['epoch_count']) is not None:
setv(
parent_object,
['distillationSpec', 'hyperParameters', 'epochCount'],
getv(from_object, ['epoch_count']),
)
if getv(from_object, ['learning_rate_multiplier']) is not None:
setv(
parent_object,
['distillationSpec', 'hyperParameters', 'learningRateMultiplier'],
getv(from_object, ['learning_rate_multiplier']),
)
if getv(from_object, ['adapter_size']) is not None:
setv(
parent_object,
['distillationSpec', 'hyperParameters', 'adapterSize'],
getv(from_object, ['adapter_size']),
)
if getv(from_object, ['pipeline_root_directory']) is not None:
setv(
parent_object,
['distillationSpec', 'pipelineRootDirectory'],
getv(from_object, ['pipeline_root_directory']),
)
return to_object
def _CreateDistillationJobParameters_to_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['student_model']) is not None:
raise ValueError('student_model parameter is not supported in Google AI.')
if getv(from_object, ['teacher_model']) is not None:
raise ValueError('teacher_model parameter is not supported in Google AI.')
if getv(from_object, ['training_dataset']) is not None:
setv(
to_object,
['tuningTask', 'trainingData'],
_DistillationDataset_to_mldev(
api_client, getv(from_object, ['training_dataset']), to_object
),
)
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_CreateDistillationJobConfig_to_mldev(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _CreateDistillationJobParameters_to_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['student_model']) is not None:
setv(
to_object,
['distillationSpec', 'studentModel'],
getv(from_object, ['student_model']),
)
if getv(from_object, ['teacher_model']) is not None:
setv(
to_object,
['distillationSpec', 'baseTeacherModel'],
getv(from_object, ['teacher_model']),
)
if getv(from_object, ['training_dataset']) is not None:
setv(
to_object,
['distillationSpec', 'trainingDatasetUri'],
_DistillationDataset_to_vertex(
api_client, getv(from_object, ['training_dataset']), to_object
),
)
if getv(from_object, ['config']) is not None:
setv(
to_object,
['config'],
_CreateDistillationJobConfig_to_vertex(
api_client, getv(from_object, ['config']), to_object
),
)
return to_object
def _TunedModel_from_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['name']) is not None:
setv(to_object, ['model'], getv(from_object, ['name']))
if getv(from_object, ['name']) is not None:
setv(to_object, ['endpoint'], getv(from_object, ['name']))
return to_object
def _TunedModel_from_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['model']) is not None:
setv(to_object, ['model'], getv(from_object, ['model']))
if getv(from_object, ['endpoint']) is not None:
setv(to_object, ['endpoint'], getv(from_object, ['endpoint']))
return to_object
def _TuningJob_from_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['name']) is not None:
setv(to_object, ['name'], getv(from_object, ['name']))
if getv(from_object, ['state']) is not None:
setv(
to_object,
['state'],
t.t_tuning_job_status(api_client, getv(from_object, ['state'])),
)
if getv(from_object, ['createTime']) is not None:
setv(to_object, ['create_time'], getv(from_object, ['createTime']))
if getv(from_object, ['tuningTask', 'startTime']) is not None:
setv(
to_object,
['start_time'],
getv(from_object, ['tuningTask', 'startTime']),
)
if getv(from_object, ['tuningTask', 'completeTime']) is not None:
setv(
to_object,
['end_time'],
getv(from_object, ['tuningTask', 'completeTime']),
)
if getv(from_object, ['updateTime']) is not None:
setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
if getv(from_object, ['description']) is not None:
setv(to_object, ['description'], getv(from_object, ['description']))
if getv(from_object, ['baseModel']) is not None:
setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
if getv(from_object, ['_self']) is not None:
setv(
to_object,
['tuned_model'],
_TunedModel_from_mldev(
api_client, getv(from_object, ['_self']), to_object
),
)
if getv(from_object, ['experiment']) is not None:
setv(to_object, ['experiment'], getv(from_object, ['experiment']))
if getv(from_object, ['labels']) is not None:
setv(to_object, ['labels'], getv(from_object, ['labels']))
if getv(from_object, ['tunedModelDisplayName']) is not None:
setv(
to_object,
['tuned_model_display_name'],
getv(from_object, ['tunedModelDisplayName']),
)
return to_object
def _TuningJob_from_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['name']) is not None:
setv(to_object, ['name'], getv(from_object, ['name']))
if getv(from_object, ['state']) is not None:
setv(
to_object,
['state'],
t.t_tuning_job_status(api_client, getv(from_object, ['state'])),
)
if getv(from_object, ['createTime']) is not None:
setv(to_object, ['create_time'], getv(from_object, ['createTime']))
if getv(from_object, ['startTime']) is not None:
setv(to_object, ['start_time'], getv(from_object, ['startTime']))
if getv(from_object, ['endTime']) is not None:
setv(to_object, ['end_time'], getv(from_object, ['endTime']))
if getv(from_object, ['updateTime']) is not None:
setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
if getv(from_object, ['error']) is not None:
setv(to_object, ['error'], getv(from_object, ['error']))
if getv(from_object, ['description']) is not None:
setv(to_object, ['description'], getv(from_object, ['description']))
if getv(from_object, ['baseModel']) is not None:
setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
if getv(from_object, ['tunedModel']) is not None:
setv(
to_object,
['tuned_model'],
_TunedModel_from_vertex(
api_client, getv(from_object, ['tunedModel']), to_object
),
)
if getv(from_object, ['supervisedTuningSpec']) is not None:
setv(
to_object,
['supervised_tuning_spec'],
getv(from_object, ['supervisedTuningSpec']),
)
if getv(from_object, ['tuningDataStats']) is not None:
setv(
to_object, ['tuning_data_stats'], getv(from_object, ['tuningDataStats'])
)
if getv(from_object, ['encryptionSpec']) is not None:
setv(to_object, ['encryption_spec'], getv(from_object, ['encryptionSpec']))
if getv(from_object, ['distillationSpec']) is not None:
setv(
to_object,
['distillation_spec'],
getv(from_object, ['distillationSpec']),
)
if getv(from_object, ['partnerModelTuningSpec']) is not None:
setv(
to_object,
['partner_model_tuning_spec'],
getv(from_object, ['partnerModelTuningSpec']),
)
if getv(from_object, ['pipelineJob']) is not None:
setv(to_object, ['pipeline_job'], getv(from_object, ['pipelineJob']))
if getv(from_object, ['experiment']) is not None:
setv(to_object, ['experiment'], getv(from_object, ['experiment']))
if getv(from_object, ['labels']) is not None:
setv(to_object, ['labels'], getv(from_object, ['labels']))
if getv(from_object, ['tunedModelDisplayName']) is not None:
setv(
to_object,
['tuned_model_display_name'],
getv(from_object, ['tunedModelDisplayName']),
)
return to_object
def _ListTuningJobsResponse_from_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['nextPageToken']) is not None:
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
if getv(from_object, ['tunedModels']) is not None:
setv(
to_object,
['tuning_jobs'],
[
_TuningJob_from_mldev(api_client, item, to_object)
for item in getv(from_object, ['tunedModels'])
],
)
return to_object
def _ListTuningJobsResponse_from_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['nextPageToken']) is not None:
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
if getv(from_object, ['tuningJobs']) is not None:
setv(
to_object,
['tuning_jobs'],
[
_TuningJob_from_vertex(api_client, item, to_object)
for item in getv(from_object, ['tuningJobs'])
],
)
return to_object
def _TuningJobOrOperation_from_mldev(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['_self']) is not None:
setv(
to_object,
['tuning_job'],
_TuningJob_from_mldev(
api_client,
t.t_resolve_operation(api_client, getv(from_object, ['_self'])),
to_object,
),
)
return to_object
def _TuningJobOrOperation_from_vertex(
api_client: ApiClient,
from_object: Union[dict, object],
parent_object: dict = None,
) -> dict:
to_object = {}
if getv(from_object, ['_self']) is not None:
setv(
to_object,
['tuning_job'],
_TuningJob_from_vertex(
api_client,
t.t_resolve_operation(api_client, getv(from_object, ['_self'])),
to_object,
),
)
return to_object
class Tunings(_common.BaseModule):
def _get(
self,
*,
name: str,
config: Optional[types.GetTuningJobConfigOrDict] = None,
) -> types.TuningJob:
"""Gets a TuningJob.
Args:
name: The resource name of the tuning job.
Returns:
A TuningJob object.
"""
parameter_model = types._GetTuningJobParameters(
name=name,
config=config,
)
if self._api_client.vertexai:
request_dict = _GetTuningJobParameters_to_vertex(
self._api_client, parameter_model
)
path = '{name}'.format_map(request_dict.get('_url'))
else:
request_dict = _GetTuningJobParameters_to_mldev(
self._api_client, parameter_model
)
path = '{name}'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = self._api_client.request(
'get', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
else:
response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
return_value = types.TuningJob._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
def _list(
self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
) -> types.ListTuningJobsResponse:
"""Lists tuning jobs.
Args:
config: The configuration for the list request.
Returns:
A list of tuning jobs.
"""
parameter_model = types._ListTuningJobsParameters(
config=config,
)
if self._api_client.vertexai:
request_dict = _ListTuningJobsParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
else:
request_dict = _ListTuningJobsParameters_to_mldev(
self._api_client, parameter_model
)
path = 'tunedModels'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = self._api_client.request(
'get', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _ListTuningJobsResponse_from_vertex(
self._api_client, response_dict
)
else:
response_dict = _ListTuningJobsResponse_from_mldev(
self._api_client, response_dict
)
return_value = types.ListTuningJobsResponse._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
def _tune(
self,
*,
base_model: str,
training_dataset: types.TuningDatasetOrDict,
config: Optional[types.CreateTuningJobConfigOrDict] = None,
) -> types.TuningJobOrOperation:
"""Creates a supervised fine-tuning job.
Args:
base_model: The name of the model to tune.
training_dataset: The training dataset to use.
config: The configuration to use for the tuning job.
Returns:
A TuningJob object.
"""
parameter_model = types._CreateTuningJobParameters(
base_model=base_model,
training_dataset=training_dataset,
config=config,
)
if self._api_client.vertexai:
request_dict = _CreateTuningJobParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
else:
request_dict = _CreateTuningJobParameters_to_mldev(
self._api_client, parameter_model
)
path = 'tunedModels'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = self._api_client.request(
'post', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJobOrOperation_from_vertex(
self._api_client, response_dict
)
else:
response_dict = _TuningJobOrOperation_from_mldev(
self._api_client, response_dict
)
return_value = types.TuningJobOrOperation._from_response(
response_dict, parameter_model
).tuning_job
self._api_client._verify_response(return_value)
return return_value
def distill(
self,
*,
student_model: str,
teacher_model: str,
training_dataset: types.DistillationDatasetOrDict,
config: Optional[types.CreateDistillationJobConfigOrDict] = None,
) -> types.TuningJob:
"""Creates a distillation job.
Args:
student_model: The name of the model to tune.
teacher_model: The name of the model to distill from.
training_dataset: The training dataset to use.
config: The configuration to use for the distillation job.
Returns:
A TuningJob object.
"""
parameter_model = types._CreateDistillationJobParameters(
student_model=student_model,
teacher_model=teacher_model,
training_dataset=training_dataset,
config=config,
)
if not self._api_client.vertexai:
raise ValueError('This method is only supported in the Vertex AI client.')
else:
request_dict = _CreateDistillationJobParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = self._api_client.request(
'post', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
else:
response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
return_value = types.TuningJob._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
def list(
self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
) -> Pager[types.TuningJob]:
return Pager(
'tuning_jobs',
self._list,
self._list(config=config),
config,
)
def get(
self,
*,
name: str,
config: Optional[types.GetTuningJobConfigOrDict] = None,
) -> types.TuningJob:
job = self._get(name=name, config=config)
if job.experiment and self._api_client.vertexai:
_IpythonUtils.display_experiment_button(
experiment=job.experiment,
project=self._api_client.project,
)
return job
def tune(
self,
*,
base_model: str,
training_dataset: types.TuningDatasetOrDict,
config: Optional[types.CreateTuningJobConfigOrDict] = None,
) -> types.TuningJobOrOperation:
result = self._tune(
base_model=base_model,
training_dataset=training_dataset,
config=config,
)
if result.name and self._api_client.vertexai:
_IpythonUtils.display_model_tuning_button(tuning_job_resource=result.name)
return result
class AsyncTunings(_common.BaseModule):
async def _get(
self,
*,
name: str,
config: Optional[types.GetTuningJobConfigOrDict] = None,
) -> types.TuningJob:
"""Gets a TuningJob.
Args:
name: The resource name of the tuning job.
Returns:
A TuningJob object.
"""
parameter_model = types._GetTuningJobParameters(
name=name,
config=config,
)
if self._api_client.vertexai:
request_dict = _GetTuningJobParameters_to_vertex(
self._api_client, parameter_model
)
path = '{name}'.format_map(request_dict.get('_url'))
else:
request_dict = _GetTuningJobParameters_to_mldev(
self._api_client, parameter_model
)
path = '{name}'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = await self._api_client.async_request(
'get', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
else:
response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
return_value = types.TuningJob._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
async def _list(
self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
) -> types.ListTuningJobsResponse:
"""Lists tuning jobs.
Args:
config: The configuration for the list request.
Returns:
A list of tuning jobs.
"""
parameter_model = types._ListTuningJobsParameters(
config=config,
)
if self._api_client.vertexai:
request_dict = _ListTuningJobsParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
else:
request_dict = _ListTuningJobsParameters_to_mldev(
self._api_client, parameter_model
)
path = 'tunedModels'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = await self._api_client.async_request(
'get', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _ListTuningJobsResponse_from_vertex(
self._api_client, response_dict
)
else:
response_dict = _ListTuningJobsResponse_from_mldev(
self._api_client, response_dict
)
return_value = types.ListTuningJobsResponse._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
async def _tune(
self,
*,
base_model: str,
training_dataset: types.TuningDatasetOrDict,
config: Optional[types.CreateTuningJobConfigOrDict] = None,
) -> types.TuningJobOrOperation:
"""Creates a supervised fine-tuning job.
Args:
base_model: The name of the model to tune.
training_dataset: The training dataset to use.
config: The configuration to use for the tuning job.
Returns:
A TuningJob object.
"""
parameter_model = types._CreateTuningJobParameters(
base_model=base_model,
training_dataset=training_dataset,
config=config,
)
if self._api_client.vertexai:
request_dict = _CreateTuningJobParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
else:
request_dict = _CreateTuningJobParameters_to_mldev(
self._api_client, parameter_model
)
path = 'tunedModels'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = await self._api_client.async_request(
'post', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJobOrOperation_from_vertex(
self._api_client, response_dict
)
else:
response_dict = _TuningJobOrOperation_from_mldev(
self._api_client, response_dict
)
return_value = types.TuningJobOrOperation._from_response(
response_dict, parameter_model
).tuning_job
self._api_client._verify_response(return_value)
return return_value
async def distill(
self,
*,
student_model: str,
teacher_model: str,
training_dataset: types.DistillationDatasetOrDict,
config: Optional[types.CreateDistillationJobConfigOrDict] = None,
) -> types.TuningJob:
"""Creates a distillation job.
Args:
student_model: The name of the model to tune.
teacher_model: The name of the model to distill from.
training_dataset: The training dataset to use.
config: The configuration to use for the distillation job.
Returns:
A TuningJob object.
"""
parameter_model = types._CreateDistillationJobParameters(
student_model=student_model,
teacher_model=teacher_model,
training_dataset=training_dataset,
config=config,
)
if not self._api_client.vertexai:
raise ValueError('This method is only supported in the Vertex AI client.')
else:
request_dict = _CreateDistillationJobParameters_to_vertex(
self._api_client, parameter_model
)
path = 'tuningJobs'.format_map(request_dict.get('_url'))
query_params = request_dict.get('_query')
if query_params:
path = f'{path}?{urlencode(query_params)}'
# TODO: remove the hack that pops config.
config = request_dict.pop('config', None)
http_options = config.pop('httpOptions', None) if config else None
request_dict = _common.convert_to_dict(request_dict)
request_dict = _common.encode_unserializable_types(request_dict)
response_dict = await self._api_client.async_request(
'post', path, request_dict, http_options
)
if self._api_client.vertexai:
response_dict = _TuningJob_from_vertex(self._api_client, response_dict)
else:
response_dict = _TuningJob_from_mldev(self._api_client, response_dict)
return_value = types.TuningJob._from_response(
response_dict, parameter_model
)
self._api_client._verify_response(return_value)
return return_value
async def list(
self, *, config: Optional[types.ListTuningJobsConfigOrDict] = None
) -> AsyncPager[types.TuningJob]:
return AsyncPager(
'tuning_jobs',
self._list,
await self._list(config=config),
config,
)
async def get(
self,
*,
name: str,
config: Optional[types.GetTuningJobConfigOrDict] = None,
) -> types.TuningJob:
job = await self._get(name=name, config=config)
if job.experiment and self._api_client.vertexai:
_IpythonUtils.display_experiment_button(
experiment=job.experiment,
project=self._api_client.project,
)
return job
async def tune(
self,
*,
base_model: str,
training_dataset: types.TuningDatasetOrDict,
config: Optional[types.CreateTuningJobConfigOrDict] = None,
) -> types.TuningJobOrOperation:
result = await self._tune(
base_model=base_model,
training_dataset=training_dataset,
config=config,
)
if result.name and self._api_client.vertexai:
_IpythonUtils.display_model_tuning_button(tuning_job_resource=result.name)
return result
class _IpythonUtils:
"""Temporary class to hold the IPython related functions."""
displayed_experiments = set()
@staticmethod
def _get_ipython_shell_name() -> str:
import sys
if 'IPython' in sys.modules:
from IPython import get_ipython
return get_ipython().__class__.__name__
return ''
@staticmethod
def is_ipython_available() -> bool:
return bool(_IpythonUtils._get_ipython_shell_name())
@staticmethod
def _get_styles() -> None:
"""Returns the HTML style markup to support custom buttons."""
return """
<link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
<style>
.view-vertex-resource,
.view-vertex-resource:hover,
.view-vertex-resource:visited {
position: relative;
display: inline-flex;
flex-direction: row;
height: 32px;
padding: 0 12px;
margin: 4px 18px;
gap: 4px;
border-radius: 4px;
align-items: center;
justify-content: center;
background-color: rgb(255, 255, 255);
color: rgb(51, 103, 214);
font-family: Roboto,"Helvetica Neue",sans-serif;
font-size: 13px;
font-weight: 500;
text-transform: uppercase;
text-decoration: none !important;
transition: box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1) 0s;
box-shadow: 0px 3px 1px -2px rgba(0,0,0,0.2), 0px 2px 2px 0px rgba(0,0,0,0.14), 0px 1px 5px 0px rgba(0,0,0,0.12);
}
.view-vertex-resource:active {
box-shadow: 0px 5px 5px -3px rgba(0,0,0,0.2),0px 8px 10px 1px rgba(0,0,0,0.14),0px 3px 14px 2px rgba(0,0,0,0.12);
}
.view-vertex-resource:active .view-vertex-ripple::before {
position: absolute;
top: 0;
bottom: 0;
left: 0;
right: 0;
border-radius: 4px;
pointer-events: none;
content: '';
background-color: rgb(51, 103, 214);
opacity: 0.12;
}
.view-vertex-icon {
font-size: 18px;
}
</style>
"""
@staticmethod
def _parse_resource_name(marker: str, resource_parts: list[str]) -> str:
"""Returns the part after the marker text part."""
for i in range(len(resource_parts)):
if resource_parts[i] == marker and i + 1 < len(resource_parts):
return resource_parts[i + 1]
return ''
@staticmethod
def _display_link(
text: str, url: str, icon: Optional[str] = 'open_in_new'
) -> None:
"""Creates and displays the link to open the Vertex resource.
Args:
text: The text displayed on the clickable button.
url: The url that the button will lead to. Only cloud console URIs are
allowed.
icon: The icon name on the button (from material-icons library)
"""
CLOUD_UI_URL = 'https://console.cloud.google.com' # pylint: disable=invalid-name
if not url.startswith(CLOUD_UI_URL):
raise ValueError(f'Only urls starting with {CLOUD_UI_URL} are allowed.')
import uuid
button_id = f'view-vertex-resource-{str(uuid.uuid4())}'
# Add the markup for the CSS and link component
html = f"""
{_IpythonUtils._get_styles()}
<a class="view-vertex-resource" id="{button_id}" href="#view-{button_id}">
<span class="material-icons view-vertex-icon">{icon}</span>
<span>{text}</span>
</a>
"""
# Add the click handler for the link
html += f"""
<script>
(function () {{
const link = document.getElementById('{button_id}');
link.addEventListener('click', (e) => {{
if (window.google?.colab?.openUrl) {{
window.google.colab.openUrl('{url}');
}} else {{
window.open('{url}', '_blank');
}}
e.stopPropagation();
e.preventDefault();
}});
}})();
</script>
"""
from IPython.core.display import display
from IPython.display import HTML
display(HTML(html))
@staticmethod
def display_experiment_button(experiment: str, project: str) -> None:
"""Function to generate a link bound to the Vertex experiment.
Args:
experiment: The Vertex experiment name. Example format:
projects/{project_id}/locations/{location}/metadataStores/default/contexts/{experiment_name}
project: The project (alphanumeric) name.
"""
if (
not _IpythonUtils.is_ipython_available()
or experiment in _IpythonUtils.displayed_experiments
):
return
# Experiment gives the numeric id, but we need the alphanumeric project
# name. So we get the project from the api client object as an argument.
resource_parts = experiment.split('/')
location = resource_parts[3]
experiment_name = resource_parts[-1]
uri = (
'https://console.cloud.google.com/vertex-ai/experiments/locations/'
+ f'{location}/experiments/{experiment_name}/'
+ f'runs?project={project}'
)
_IpythonUtils._display_link('View Experiment', uri, 'science')
# Avoid repeatedly showing the button
_IpythonUtils.displayed_experiments.add(experiment)
@staticmethod
def display_model_tuning_button(tuning_job_resource: str) -> None:
"""Function to generate a link bound to the Vertex model tuning job.
Args:
tuning_job_resource: The Vertex tuning job name. Example format:
projects/{project_id}/locations/{location}/tuningJobs/{tuning_job_id}
"""
if not _IpythonUtils.is_ipython_available():
return
resource_parts = tuning_job_resource.split('/')
project = resource_parts[1]
location = resource_parts[3]
tuning_job_id = resource_parts[-1]
uri = (
'https://console.cloud.google.com/vertex-ai/generative/language/'
+ f'locations/{location}/tuning/tuningJob/{tuning_job_id}'
+ f'?project={project}'
)
_IpythonUtils._display_link('View Tuning Job', uri, 'tune')
