# Copyright (c) 2023 Baidu, Inc. All Rights Reserved.
#
# 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.
"""
FineTune API
"""
from typing import Any, Dict, Optional, Union
from qianfan.consts import Consts
from qianfan.resources.console import consts as console_consts
from qianfan.resources.console.utils import _get_console_v2_query, console_api_request
from qianfan.resources.typing import QfRequest
[docs]class FineTune(object):
"""
Class for FineTune API
"""
[docs] @classmethod
@console_api_request
def get_job(cls, task_id: int, job_id: int, **kwargs: Any) -> QfRequest:
"""
Retrieves a job for model fine-tuning.
This method is responsible for retrieving a job for the specified fine-tuning
task and job IDs.
Parameters:
task_id (int):
The ID of the task associated with the fine-tuning job.
job_id (int):
The ID of the fine-tuning job to retrieve.
kwargs (Any):
Additional keyword arguments that can be passed to customize the request.
Note:
The `@console_api_request` decorator is applied to this method, enabling it to
send the generated QfRequest and return a QfResponse to the user.
API Doc: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/wlmrgowee
"""
req = QfRequest(method="POST", url=Consts.FineTuneGetJobAPI)
req.json_body = {"taskId": task_id, "jobId": job_id, **kwargs}
return req
[docs] @classmethod
@console_api_request
def create_task(
cls,
name: str,
base_train_type: str,
train_type: str,
description: Optional[str] = None,
**kwargs: Any,
) -> QfRequest:
"""
Create a model fine-tuning task.
This function is used to create a model fine-tuning task. The task can be
customized with a name and description.
Parameters:
name (str):
The name of the fine-tuning task.
base_train_type (str):
The base training type of the fine-tuning task. e.g. "ERNIE-Bot-turbo"
train_type (str):
The training type of the fine-tuning task. e.g. "ERNIE-Bot-turbo-0922
description (Optional[str]):
An optional description for the fine-tuning task.
kwargs (Any):
Additional keyword arguments that can be passed to customize the request.
Note:
The `@console_api_request` decorator is applied to this method, enabling it to
send the generated QfRequest and return a QfResponse to the user.
API Doc: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/almrgn397
"""
req = QfRequest(method="POST", url=Consts.FineTuneCreateTaskAPI)
req.json_body = {
"name": name,
"baseTrainType": base_train_type,
"trainType": train_type,
**kwargs,
}
if description is not None:
req.json_body["description"] = description
return req
[docs] @classmethod
@console_api_request
def create_job(cls, job: Dict[str, Any], **kwargs: Any) -> QfRequest:
"""
Create a job for fine-tuning a model.
This function creates a job for fine-tuning a model.
Parameters:
job (Dict[str, Any]):
A dictionary containing job details and configurations. The fields are same
with the API doc.
kwargs (Any):
Additional keyword arguments that can be passed to customize the request.
Note:
The `@console_api_request` decorator is applied to this method, enabling it to
send the generated QfRequest and return a QfResponse to the user.
API Doc: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/mlmrgo4yx
"""
req = QfRequest(method="POST", url=Consts.FineTuneCreateJobAPI)
req.json_body = {**job, **kwargs}
return req
[docs] @classmethod
@console_api_request
def stop_job(cls, task_id: str, job_id: str, **kwargs: Any) -> QfRequest:
"""
Stop a fine-tuning job.
This function allows the stopping of a fine-tuning job associated with a
specific task.
Parameters:
task_id (str):
The identifier of the task associated with the fine-tuning job.
job_id (str):
The identifier of the fine-tuning job to be stopped.
kwargs:
Additional keyword arguments that can be passed to customize the request.
Note:
The `@console_api_request` decorator is applied to this method, enabling it to
send the generated QfRequest and return a QfResponse to the user.
API Doc: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/2lnlebz15
"""
req = QfRequest(method="POST", url=Consts.FineTuneStopJobAPI)
req.json_body = {"taskId": task_id, "jobId": job_id, **kwargs}
return req
[docs] class V2:
"""
this class provides methods to interact with the fine-tuning V2 API.
"""
[docs] @classmethod
def base_api_route(cls) -> str:
"""
base api url route for fine-tuning V2.
Returns:
str: base api url route
"""
return Consts.FineTuneV2BaseRouteAPI
[docs] @classmethod
@console_api_request
def create_job(
cls,
name: str,
model: str,
train_mode: Union[str, console_consts.TrainMode],
description: Optional[str] = None,
**kwargs: Any,
) -> QfRequest:
"""
create a fine-tuning job.
This function create a fine-tuning job. job may be associated with
many tasks.
Parameters:
name (str):
The name of job.
model (str):
The identifier of the fine-tuning job to be stopped.
e.g. "ERNIE-Speed"
train_mode (Union[str, console_consts.TrainMode]):
The train mode of the fine-tuning job, including "SFT" and
"PostPreTrain" and so on.
description (Optional[str]):
The description of the fine-tuning job.
kwargs:
Additional keyword arguments that can be passed to customize the
request.
Note:
The `@console_api_request` decorator is applied to this method, enabling
it to send the generated QfRequest and return a QfResponse to the user.
"""
req = QfRequest(
method="POST",
url=cls.base_api_route(),
query=_get_console_v2_query(Consts.FineTuneCreateJobAction),
)
req.json_body = {**kwargs, "name": name, "model": model}
if isinstance(train_mode, console_consts.TrainMode):
req.json_body["trainMode"] = train_mode.value
elif isinstance(train_mode, str):
req.json_body["trainMode"] = train_mode
else:
raise TypeError(
"train_mode must be a string or TrainMode, but got"
f" {type(train_mode)}"
)
if description is not None:
req.json_body["description"] = description
return req
[docs] @classmethod
@console_api_request
def create_task(
cls,
job_id: str,
params_scale: Union[str, console_consts.TrainParameterScale],
hyper_params: Dict[str, Any],
dataset_config: Dict[str, Any],
incrementTaskId: Optional[str] = None,
**kwargs: Any,
) -> QfRequest:
"""
create a fine-tuning task.
This function create a fine-tuning task associated with a
specific job.
Parameters:
name (str):
The name of job.
model (str):
The identifier of the fine-tuning job to be stopped.
e.g. "ERNIE-Speed"
train_mode (Union[str, console_consts.TrainMode]):
The train mode of the fine-tuning job, including "SFT",
"PostPreTrain" and so on.
description (Optional[str]):
The description of the fine-tuning job.
kwargs:
Additional keyword arguments that can be passed to customize
the request.
Note:
The `@console_api_request` decorator is applied to this method, enabling
it to send the generated QfRequest and return a QfResponse to the user.
"""
req = QfRequest(
method="POST",
url=cls.base_api_route(),
query=_get_console_v2_query(Consts.FineTuneCreateTaskAction),
)
req.json_body = {
**kwargs,
"jobId": job_id,
"parameterScale": (
params_scale.value
if isinstance(params_scale, console_consts.TrainParameterScale)
else params_scale
),
"hyperParameterConfig": hyper_params,
"datasetConfig": dataset_config,
}
if incrementTaskId is not None:
req.json_body["incrementTaskId"] = incrementTaskId
return req
[docs] @classmethod
@console_api_request
def job_list(
cls,
train_model: Optional[Union[str, console_consts.TrainMode]] = None,
marker: Optional[str] = None,
max_keys: Optional[int] = None,
page_reverse: Optional[bool] = None,
**kwargs: Any,
) -> QfRequest:
req = QfRequest(
method="POST",
url=cls.base_api_route(),
query=_get_console_v2_query(Consts.FineTuneJobListAction),
)
req.json_body = {
k: v
for k, v in {
**kwargs,
"trainModel": (
train_model.value
if isinstance(train_model, console_consts.TrainMode)
else train_model
),
"maker": marker,
"maxKeys": max_keys,
"pageReverse": page_reverse,
}.items()
if v is not None
}
return req
[docs] @classmethod
@console_api_request
def task_list(
cls,
job_id: str,
marker: Optional[str] = None,
max_keys: Optional[int] = None,
page_reverse: Optional[bool] = None,
**kwargs: Any,
) -> QfRequest:
req = QfRequest(
method="POST",
url=cls.base_api_route(),
query=_get_console_v2_query(Consts.FineTuneTaskListAction),
)
req.json_body = {
k: v
for k, v in {
**kwargs,
"jobId": job_id,
"maker": marker,
"maxKeys": max_keys,
"pageReverse": page_reverse,
}.items()
if v is not None
}
return req
[docs] @classmethod
@console_api_request
def task_detail(
cls,
task_id: str,
**kwargs: Any,
) -> QfRequest:
req = QfRequest(
method="POST",
url=cls.base_api_route(),
query=_get_console_v2_query(Consts.FineTuneTaskDetailAction),
)
req.json_body = {
**kwargs,
"taskId": task_id,
}
return req