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

class Config

Bases: object

extra = 'forbid'
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

class Config

Bases: object

extra = 'forbid'
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

class Config

Bases: object

extra = 'forbid'
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

class Config

Bases: object

extra = 'forbid'
validate_assignment = True
project_path: pathlib.Path
server: kedro_mlflow.config.kedro_mlflow_config.MlflowServerOptions
setup(context)

Setup all the mlflow configuration

tracking: kedro_mlflow.config.kedro_mlflow_config.MlflowTrackingOptions
ui: kedro_mlflow.config.kedro_mlflow_config.UiOptions
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

class Config

Bases: object

extra = 'forbid'
dict_params: kedro_mlflow.config.kedro_mlflow_config.DictParamsOptions
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

class Config

Bases: object

extra = 'forbid'
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

class Config

Bases: object

extra = 'forbid'
disable_tracking: kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions
experiment: kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions
params: kedro_mlflow.config.kedro_mlflow_config.MlflowParamsOptions
run: kedro_mlflow.config.kedro_mlflow_config.RunOptions
class kedro_mlflow.config.kedro_mlflow_config.RunOptions(*, id: str = None, name: str = None, nested: pydantic.types.StrictBool = True)

Bases: pydantic.main.BaseModel

class Config

Bases: object

extra = 'forbid'
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

class Config

Bases: object

extra = 'forbid'
host: str
port: str
kedro_mlflow.config.kedro_mlflow_config.get_mlflow_config(context: kedro.framework.context.context.KedroContext)