chore: 添加虚拟环境到仓库
- 添加 backend_service/venv 虚拟环境 - 包含所有Python依赖包 - 注意：虚拟环境约393MB，包含12655个文件
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import time
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from http import HTTPStatus
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from typing import Iterator, Union
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from dashscope.client.base_api import (CancelMixin, CreateMixin, DeleteMixin,
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GetStatusMixin, ListMixin, LogMixin,
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StreamEventMixin)
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from dashscope.common.constants import TaskStatus
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from dashscope.customize.customize_types import (FineTune, FineTuneCancel,
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FineTuneDelete, FineTuneEvent,
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FineTuneList)
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class FineTunes(CreateMixin, CancelMixin, DeleteMixin, ListMixin,
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GetStatusMixin, StreamEventMixin, LogMixin):
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SUB_PATH = 'fine-tunes'
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@classmethod
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def call(cls,
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model: str,
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training_file_ids: Union[list, str],
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validation_file_ids: Union[list, str] = None,
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mode: str = None,
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hyper_parameters: dict = {},
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTune:
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"""Create fine-tune job
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Args:
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model (str): The model to be fine-tuned
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training_file_ids (list, str): Ids of the fine-tune training data,
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which can be pre-uploaded using the File API.
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validation_file_ids ([list,str], optional): Ids of the fine-tune
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validating data, which can be pre-uploaded using the File API.
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mode (str): The fine-tune mode, sft or efficient_sft.
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hyper_parameters (dict, optional): The fine-tune hyper parameters.
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Defaults to empty.
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api_key (str, optional): The api key. Defaults to None.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The request result.
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"""
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if isinstance(training_file_ids, str):
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training_file_ids = [training_file_ids]
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if validation_file_ids and isinstance(validation_file_ids, str):
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validation_file_ids = [validation_file_ids]
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request = {
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'model': model,
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'training_file_ids': training_file_ids,
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'validation_file_ids': validation_file_ids,
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'hyper_parameters': hyper_parameters if hyper_parameters else {},
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}
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if mode is not None:
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request['training_type'] = mode
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if 'finetuned_output' in kwargs:
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request['finetuned_output'] = kwargs['finetuned_output']
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resp = super().call(request,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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return FineTune(**resp)
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@classmethod
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def cancel(cls,
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job_id: str,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTuneCancel:
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"""Cancel a running fine-tune job.
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Args:
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job_id (str): The fine-tune job id.
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api_key (str, optional): The api api_key, can be None,
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if None, will get by default rule(TODO: api key doc).
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The request result.
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"""
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rsp = super().cancel(job_id,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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return FineTuneCancel(**rsp)
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@classmethod
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def list(cls,
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page_no=1,
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page_size=10,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTuneList:
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"""List fine-tune job.
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Args:
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api_key (str, optional): The api key
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page_no (int, optional): Page number. Defaults to 1.
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page_size (int, optional): Items per page. Defaults to 10.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The fine-tune jobs in the result.
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"""
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response = super().list(page_no=page_no,
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page_size=page_size,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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return FineTuneList(**response)
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@classmethod
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def get(cls,
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job_id: str,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTune:
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"""Get fine-tune job information.
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Args:
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job_id (str): The fine-tune job id
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api_key (str, optional): The api key. Defaults to None.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The job info
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"""
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response = super().get(job_id,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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return FineTune(**response)
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@classmethod
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def delete(cls,
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job_id: str,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTuneDelete:
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"""Delete a fine-tune job.
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Args:
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job_id (str): The fine-tune job id.
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api_key (str, optional): The api key. Defaults to None.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The delete result.
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"""
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rsp = super().delete(job_id,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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return FineTuneDelete(**rsp)
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@classmethod
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def stream_events(cls,
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job_id: str,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> Iterator[FineTuneEvent]:
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"""Get fine-tune job events.
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Args:
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job_id (str): The fine-tune job id
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api_key (str, optional): the api key. Defaults to None.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The job log events.
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"""
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responses = super().stream_events(job_id,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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for rsp in responses:
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yield FineTuneEvent(**rsp)
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@classmethod
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def logs(cls,
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job_id: str,
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*,
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offset=1,
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line=1000,
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api_key: str = None,
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workspace: str = None,
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**kwargs) -> FineTune:
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"""Get log of the job.
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Args:
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job_id (str): The job id(used for fine-tune)
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offset (int, optional): start log line. Defaults to 1.
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line (int, optional): total line return. Defaults to 1000.
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api_key (str, optional): The api key. Defaults to None.
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workspace (str): The dashscope workspace id.
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Returns:
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FineTune: The response
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"""
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return super().logs(job_id,
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offset=offset,
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line=line,
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workspace=workspace,
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api_key=api_key)
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@classmethod
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def wait(cls,
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job_id: str,
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api_key: str = None,
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workspace: str = None,
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**kwargs):
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try:
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while True:
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rsp = FineTunes.get(job_id,
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api_key=api_key,
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workspace=workspace,
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**kwargs)
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if rsp.status_code == HTTPStatus.OK:
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if rsp.output['status'] in [
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TaskStatus.FAILED, TaskStatus.CANCELED,
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TaskStatus.SUCCEEDED
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]:
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return rsp
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else:
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time.sleep(30)
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else:
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return rsp
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except Exception:
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raise Exception(
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'You can stream output via: dashscope fine_tunes.stream -j %s'
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% job_id)
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