kedro.framework.session.KedroSession

class documentation

class KedroSession: (source)

KedroSession is the object that is responsible for managing the lifecycle of a Kedro run. Use KedroSession.create() as a context manager to construct a new KedroSession with session data provided (see the example below).

Example:

>>> from kedro.framework.session import KedroSession
>>> from kedro.framework.startup import bootstrap_project
>>> from pathlib import Path

>>> # If you are creating a session outside of a Kedro project (i.e. not using
>>> # `kedro run` or `kedro jupyter`), you need to run `bootstrap_project` to
>>> # let Kedro find your configuration.
>>> bootstrap_project(Path("<project_root>"))
>>> with KedroSession.create() as session:
>>>     session.run()

Class Method	`create`	Create a new instance of `KedroSession` with the session data.
Method	`__enter__`	Undocumented
Method	`__exit__`	Undocumented
Method	`__init__`	Undocumented
Method	`close`	Close the current session and save its store to disk if `save_on_close` attribute is True.
Method	`load_context`	An instance of the project context.
Method	`run`	Runs the pipeline with a specified runner.
Instance Variable	`save_on_close`	Undocumented
Instance Variable	`session_id`	Undocumented
Property	`store`	Return a copy of internal store.
Method	`_get_config_loader`	An instance of the config loader.
Method	`_get_logging_config`	Undocumented
Method	`_init_store`	Undocumented
Method	`_log_exception`	Undocumented
Method	`_setup_logging`	Register logging specified in logging directory.
Instance Variable	`_conf_source`	Undocumented
Instance Variable	`_hook_manager`	Undocumented
Instance Variable	`_package_name`	Undocumented
Instance Variable	`_project_path`	Undocumented
Instance Variable	`_run_called`	Undocumented
Instance Variable	`_store`	Undocumented
Property	`_logger`	Undocumented

@classmethod
def create(cls, package_name: str = None, project_path: Union[Path, str] = None, save_on_close: bool = True, env: str = None, extra_params: Dict[str, Any] = None, conf_source: Optional[str] = None) -> KedroSession: (source) ¶

Create a new instance of KedroSession with the session data.

Parameters
package_name:`str`	Package name for the Kedro project the session is created for. The package_name argument will be removed in Kedro `0.19.0`.
project_path:`Union[Path, str]`	Path to the project root directory. Default is current working directory Path.cwd().
save_on_close:`bool`	Whether or not to save the session when it's closed.
env:`str`	Environment for the KedroContext.
extra_params:`Dict[str, Any]`	Optional dictionary containing extra project parameters for underlying KedroContext. If specified, will update (and therefore take precedence over) the parameters retrieved from the project configuration.
conf_source:`Optional[str]`	Path to a directory containing configuration
Returns
`KedroSession`	A new `KedroSession` instance.

def __enter__(self): (source) ¶

Undocumented

def __exit__(self, exc_type, exc_value, tb_): (source) ¶

Undocumented

def __init__(self, session_id: str, package_name: str = None, project_path: Union[Path, str] = None, save_on_close: bool = False, conf_source: Optional[str] = None): (source) ¶

Undocumented

def close(self): (source) ¶

Close the current session and save its store to disk if save_on_close attribute is True.

def load_context(self) -> KedroContext: (source) ¶

An instance of the project context.

def run(self, pipeline_name: str = None, tags: Iterable[str] = None, runner: AbstractRunner = None, node_names: Iterable[str] = None, from_nodes: Iterable[str] = None, to_nodes: Iterable[str] = None, from_inputs: Iterable[str] = None, to_outputs: Iterable[str] = None, load_versions: Dict[str, str] = None, namespace: str = None) -> Dict[str, Any]: (source) ¶

Runs the pipeline with a specified runner.

Parameters
pipeline_name:`str`	Name of the pipeline that is being run.
tags:`Iterable[str]`	An optional list of node tags which should be used to filter the nodes of the `Pipeline`. If specified, only the nodes containing any of these tags will be run.
runner:`AbstractRunner`	An optional parameter specifying the runner that you want to run the pipeline with.
node_names:`Iterable[str]`	An optional list of node names which should be used to filter the nodes of the `Pipeline`. If specified, only the nodes with these names will be run.
from_nodes:`Iterable[str]`	An optional list of node names which should be used as a starting point of the new `Pipeline`.
to_nodes:`Iterable[str]`	An optional list of node names which should be used as an end point of the new `Pipeline`.
from_inputs:`Iterable[str]`	An optional list of input datasets which should be used as a starting point of the new `Pipeline`.
to_outputs:`Iterable[str]`	An optional list of output datasets which should be used as an end point of the new `Pipeline`.
load_versions:`Dict[str, str]`	An optional flag to specify a particular dataset version timestamp to load.
namespace:`str`	The namespace of the nodes that is being run.
Returns
`Dict[str, Any]`	Any node outputs that cannot be processed by the `DataCatalog`. These are returned in a dictionary, where the keys are defined by the node outputs.
Raises
`ValueError`	If the named or `__default__` pipeline is not defined by `register_pipelines`.
`Exception`	Any uncaught exception during the run will be re-raised after being passed to `on_pipeline_error` hook.
`KedroSessionError`	If more than one run is attempted to be executed during a single session.