Configuration

class kedro_mlflow.config.kedro_mlflow_config.DictParamsOptions(*, flatten: bool = False, recursive: bool = True, sep: str = '.')

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'
flatten: bool
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'flatten': FieldInfo(annotation=bool, required=False, default=False, metadata=[Strict(strict=True)]), 'recursive': FieldInfo(annotation=bool, required=False, default=True, metadata=[Strict(strict=True)]), 'sep': FieldInfo(annotation=str, required=False, default='.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

recursive: bool
sep: str
class kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions(*, pipelines: List[str] = [])

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'pipelines': FieldInfo(annotation=List[str], required=False, default=[])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

pipelines: List[str]
class kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions(*, name: str = 'Default', restore_if_deleted: bool = True)

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'name': FieldInfo(annotation=str, required=False, default='Default'), 'restore_if_deleted': FieldInfo(annotation=bool, required=False, default=True, metadata=[Strict(strict=True)])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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: bool
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

class Config

Bases: object

extra = 'forbid'
validate_assignment = True
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'server': FieldInfo(annotation=MlflowServerOptions, required=False, default=MlflowServerOptions(mlflow_tracking_uri=None, mlflow_registry_uri=None, credentials=None, request_header_provider=RequestHeaderProviderOptions(type=None, pass_context=False, init_kwargs={}))), 'tracking': FieldInfo(annotation=MlflowTrackingOptions, required=False, default=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': FieldInfo(annotation=UiOptions, required=False, default=UiOptions(port='5000', host='127.0.0.1'))}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'dict_params': FieldInfo(annotation=DictParamsOptions, required=False, default=DictParamsOptions(flatten=False, recursive=True, sep='.')), 'long_params_strategy': FieldInfo(annotation=Literal['fail', 'truncate', 'tag'], required=False, default='fail')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'credentials': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'mlflow_registry_uri': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'mlflow_tracking_uri': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'request_header_provider': FieldInfo(annotation=RequestHeaderProviderOptions, required=False, default=RequestHeaderProviderOptions(type=None, pass_context=False, init_kwargs={}))}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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

class Config

Bases: object

extra = 'forbid'
disable_tracking: DisableTrackingOptions
experiment: ExperimentOptions
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'disable_tracking': FieldInfo(annotation=DisableTrackingOptions, required=False, default=DisableTrackingOptions(pipelines=[])), 'experiment': FieldInfo(annotation=ExperimentOptions, required=False, default=ExperimentOptions(name='Default', restore_if_deleted=True)), 'params': FieldInfo(annotation=MlflowParamsOptions, required=False, default=MlflowParamsOptions(dict_params=DictParamsOptions(flatten=False, recursive=True, sep='.'), long_params_strategy='fail')), 'run': FieldInfo(annotation=RunOptions, required=False, default=RunOptions(id=None, name=None, nested=True))}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

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_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'init_kwargs': FieldInfo(annotation=Dict[str, str], required=False, default={}), 'pass_context': FieldInfo(annotation=bool, required=False, default=False), 'type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

pass_context: bool
type: str | None
class kedro_mlflow.config.kedro_mlflow_config.RunOptions(*, id: str | None = None, name: str | None = None, nested: bool = True)

Bases: BaseModel

class Config

Bases: object

extra = 'forbid'
id: str | None
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'nested': FieldInfo(annotation=bool, required=False, default=True, metadata=[Strict(strict=True)])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str | None
nested: bool
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_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'host': FieldInfo(annotation=str, required=False, default='127.0.0.1'), 'port': FieldInfo(annotation=str, required=False, default='5000')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

port: str