Configuration

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', create: pydantic.types.StrictBool = True)

Bases: pydantic.main.BaseModel

class Config

Bases: object

extra = 'forbid'

create: pydantic.types.StrictBool

name: str

class kedro_mlflow.config.kedro_mlflow_config.HookOptions(*, node: kedro_mlflow.config.kedro_mlflow_config.NodeHookOptions = NodeHookOptions(flatten_dict_params=False, recursive=True, sep='.', long_parameters_strategy='fail'))

Bases: pydantic.main.BaseModel

class Config

Bases: object

extra = 'forbid'

node: kedro_mlflow.config.kedro_mlflow_config.NodeHookOptions

class kedro_mlflow.config.kedro_mlflow_config.KedroMlflowConfig(*, project_path: pathlib.Path, mlflow_tracking_uri: str = 'mlruns', credentials: str = None, disable_tracking: kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions = DisableTrackingOptions(pipelines=[]), experiment: kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions = ExperimentOptions(name='Default', create=True), run: kedro_mlflow.config.kedro_mlflow_config.RunOptions = RunOptions(id=None, name=None, nested=True), ui: kedro_mlflow.config.kedro_mlflow_config.UiOptions = UiOptions(port='5000', host='127.0.0.1'), hooks: kedro_mlflow.config.kedro_mlflow_config.HookOptions = HookOptions(node=NodeHookOptions(flatten_dict_params=False, recursive=True, sep='.', long_parameters_strategy='fail')))

Bases: pydantic.main.BaseModel

class Config

Bases: object

extra = 'forbid'

validate_assignment = True

credentials: Optional[str]

disable_tracking: kedro_mlflow.config.kedro_mlflow_config.DisableTrackingOptions

experiment: kedro_mlflow.config.kedro_mlflow_config.ExperimentOptions

hooks: kedro_mlflow.config.kedro_mlflow_config.HookOptions

mlflow_tracking_uri: str

project_path: pathlib.Path

run: kedro_mlflow.config.kedro_mlflow_config.RunOptions

setup(session: Optional[kedro.framework.session.session.KedroSession] = None)

Setup all the mlflow configuration

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.NodeHookOptions(*, flatten_dict_params: pydantic.types.StrictBool = False, recursive: pydantic.types.StrictBool = True, sep: str = '.', long_parameters_strategy: typing_extensions.Literal['fail', 'truncate', 'tag'] = 'fail')

Bases: pydantic.main.BaseModel

class Config

Bases: object

extra = 'forbid'

flatten_dict_params: pydantic.types.StrictBool

long_parameters_strategy: typing_extensions.Literal['fail', 'truncate', 'tag']

recursive: pydantic.types.StrictBool

sep: str

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(session: Optional[kedro.framework.session.session.KedroSession] = None)