Configuration
- class kedro_mlflow.config.kedro_mlflow_config.DictParamsOptions(*, flatten: pydantic.types.StrictBool = False, recursive: pydantic.types.StrictBool = True, sep: str = '.')
Bases:
pydantic.main.BaseModel
- flatten: pydantic.types.StrictBool
- recursive: pydantic.types.StrictBool
- sep: str
- class kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions(*, pipelines: List[str] = [])
Bases:
pydantic.main.BaseModel
- pipelines: List[str]
- class kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions(*, name: str = 'Default', restore_if_deleted: pydantic.types.StrictBool = True)
Bases:
pydantic.main.BaseModel
- name: str
- restore_if_deleted: pydantic.types.StrictBool
- class kedro_mlflow.config.kedro_mlflow_config.KedroMlflowConfig(*, project_path: pathlib.Path, server: kedro_mlflow.config.kedro_mlflow_config.MlflowServerOptions = MlflowServerOptions(mlflow_tracking_uri='mlruns', stores_environment_variables={}, credentials=None), tracking: kedro_mlflow.config.kedro_mlflow_config.MlflowTrackingOptions = MlflowTrackingOptions(disable_tracking=DisableTrackingOptions(pipelines=[]), experiment=ExperimentOptions(name='Default', restore_if_deleted=True), run=RunOptions(id=None, name=None, nested=True), params=MlflowParamsOptions(dict_params=DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy='fail')), ui: kedro_mlflow.config.kedro_mlflow_config.UiOptions = UiOptions(port='5000', host='127.0.0.1'))
Bases:
pydantic.main.BaseModel
- project_path: pathlib.Path
- setup(context)
Setup all the mlflow configuration
- exception kedro_mlflow.config.kedro_mlflow_config.KedroMlflowConfigError
Bases:
Exception
Error occurred when loading the configuration
- class kedro_mlflow.config.kedro_mlflow_config.MlflowParamsOptions(*, dict_params: kedro_mlflow.config.kedro_mlflow_config.DictParamsOptions = DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy: Literal['fail', 'truncate', 'tag'] = 'fail')
Bases:
pydantic.main.BaseModel
- long_params_strategy: Literal['fail', 'truncate', 'tag']
- class kedro_mlflow.config.kedro_mlflow_config.MlflowServerOptions(*, mlflow_tracking_uri: str = 'mlruns', stores_environment_variables: Dict[str, str] = {}, credentials: str = None)
Bases:
pydantic.main.BaseModel
- credentials: Optional[str]
- mlflow_tracking_uri: str
- stores_environment_variables: Dict[str, str]
- class kedro_mlflow.config.kedro_mlflow_config.MlflowTrackingOptions(*, disable_tracking: kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions = DisableTrackingOptions(pipelines=[]), experiment: kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions = ExperimentOptions(name='Default', restore_if_deleted=True), run: kedro_mlflow.config.kedro_mlflow_config.RunOptions = RunOptions(id=None, name=None, nested=True), params: kedro_mlflow.config.kedro_mlflow_config.MlflowParamsOptions = MlflowParamsOptions(dict_params=DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy='fail'))
Bases:
pydantic.main.BaseModel
- disable_tracking: kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions
- class kedro_mlflow.config.kedro_mlflow_config.RunOptions(*, id: str = None, name: str = None, nested: pydantic.types.StrictBool = True)
Bases:
pydantic.main.BaseModel
- id: Optional[str]
- name: Optional[str]
- nested: pydantic.types.StrictBool
- class kedro_mlflow.config.kedro_mlflow_config.UiOptions(*, port: str = '5000', host: str = '127.0.0.1')
Bases:
pydantic.main.BaseModel
- host: str
- port: str
- kedro_mlflow.config.kedro_mlflow_config.get_mlflow_config(context: kedro.framework.context.context.KedroContext)