Configuration

class kedro_mlflow.config.kedro_mlflow_config.CreateExperimentOptions(*, artifact_location: str | None = None, tags: dict | None = None)

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'

artifact_location: str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

tags: dict | None

class kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions(*, pipelines: list[str] = [], disable_autologging: bool = True)

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'

disable_autologging: bool

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', create_experiment_kwargs: CreateExperimentOptions = CreateExperimentOptions(artifact_location=None, tags=None), restore_if_deleted: Annotated[bool, Strict(strict=True)] = True)

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'

create_experiment_kwargs: CreateExperimentOptions

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 initialize 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=[], disable_autologging=True), experiment=ExperimentOptions(name='Default', create_experiment_kwargs=CreateExperimentOptions(artifact_location=None, tags=None), 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

class Config

Bases: object

extra = 'forbid'

validate_assignment = True

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

ui: UiOptions

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

class Config

Bases: object

extra = 'forbid'

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

class Config

Bases: object

extra = 'forbid'

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 initialize 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=[], disable_autologging=True), experiment: ExperimentOptions = ExperimentOptions(name='Default', create_experiment_kwargs=CreateExperimentOptions(artifact_location=None, tags=None), 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

class Config

Bases: object

extra = 'forbid'

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

class Config

Bases: object

arbitrary_types_allowed = 'allowed'

extra = 'forbid'

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

class Config

Bases: object

extra = 'forbid'

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

class Config

Bases: object

extra = 'forbid'

host: str

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

port: str

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

class Config

Bases: object

extra = 'forbid'

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