Configuration
- class kedro_mlflow.config.kedro_mlflow_config.DictParamsOptions(*, flatten: Annotated[bool, Strict(strict=True)] = False, recursive: Annotated[bool, Strict(strict=True)] = True, sep: str = '.')
Bases:
BaseModel- flatten: Annotated[bool, Strict(strict=True)]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- recursive: Annotated[bool, Strict(strict=True)]
- sep: str
- class kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions(*, pipelines: List[str] = [])
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- pipelines: List[str]
- class kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions(*, name: str = 'Default', restore_if_deleted: Annotated[bool, Strict(strict=True)] = True)
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_post_init(context: Any, /) None
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that’s what pydantic-core passes when calling it.
- Parameters:
self – The BaseModel instance.
context – The context.
- name: str
- restore_if_deleted: Annotated[bool, Strict(strict=True)]
- class kedro_mlflow.config.kedro_mlflow_config.KedroMlflowConfig(*, server: MlflowServerOptions = MlflowServerOptions(mlflow_tracking_uri=None, mlflow_registry_uri=None, credentials=None, request_header_provider=RequestHeaderProviderOptions(type=None, pass_context=False, init_kwargs={})), tracking: 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: UiOptions = UiOptions(port='5000', host='127.0.0.1'))
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'validate_assignment': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- server: MlflowServerOptions
- setup(context)
Setup all the mlflow configuration
- tracking: MlflowTrackingOptions
- class kedro_mlflow.config.kedro_mlflow_config.MlflowParamsOptions(*, dict_params: DictParamsOptions = DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy: Literal['fail', 'truncate', 'tag'] = 'fail')
Bases:
BaseModel- dict_params: DictParamsOptions
- long_params_strategy: Literal['fail', 'truncate', 'tag']
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class kedro_mlflow.config.kedro_mlflow_config.MlflowServerOptions(*, mlflow_tracking_uri: str | None = None, mlflow_registry_uri: str | None = None, credentials: str | None = None, request_header_provider: RequestHeaderProviderOptions = RequestHeaderProviderOptions(type=None, pass_context=False, init_kwargs={}))
Bases:
BaseModel- credentials: str | None
- mlflow_registry_uri: str | None
- mlflow_tracking_uri: str | None
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- model_post_init(context: Any, /) None
This function is meant to behave like a BaseModel method to initialise private attributes.
It takes context as an argument since that’s what pydantic-core passes when calling it.
- Parameters:
self – The BaseModel instance.
context – The context.
- request_header_provider: RequestHeaderProviderOptions
- class kedro_mlflow.config.kedro_mlflow_config.MlflowTrackingOptions(*, disable_tracking: DisableTrackingOptions = DisableTrackingOptions(pipelines=[]), experiment: ExperimentOptions = ExperimentOptions(name='Default', restore_if_deleted=True), run: RunOptions = RunOptions(id=None, name=None, nested=True), params: MlflowParamsOptions = MlflowParamsOptions(dict_params=DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy='fail'))
Bases:
BaseModel- disable_tracking: DisableTrackingOptions
- experiment: ExperimentOptions
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: MlflowParamsOptions
- run: RunOptions
- class kedro_mlflow.config.kedro_mlflow_config.RequestHeaderProviderOptions(*, type: str | None = None, pass_context: bool = False, init_kwargs: Dict[str, str] = {})
Bases:
BaseModel- init_kwargs: Dict[str, str]
- model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': 'allowed', 'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- pass_context: bool
- type: str | None
- class kedro_mlflow.config.kedro_mlflow_config.RunOptions(*, id: str | None = None, name: str | None = None, nested: Annotated[bool, Strict(strict=True)] = True)
Bases:
BaseModel- id: str | None
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- name: str | None
- nested: Annotated[bool, Strict(strict=True)]
- class kedro_mlflow.config.kedro_mlflow_config.UiOptions(*, port: str = '5000', host: str = '127.0.0.1')
Bases:
BaseModel- host: str
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- port: str