kedro.context.KedroContext¶

class kedro.context.KedroContext(project_path, env=None)[source]¶

Bases: abc.ABC

KedroContext is the base class which holds the configuration and Kedro’s main functionality. Project-specific context class should extend this abstract class and implement the all abstract methods.

CONF_ROOT¶: Name of root directory containing project configuration.

Default name is "conf".

Example:

from kedro.context import KedroContext
from kedro.pipeline import Pipeline

class ProjectContext(KedroContext):
    @property
    def pipeline(self) -> Pipeline:
        return Pipeline([])

Attributes

`KedroContext.CONF_ROOT`
`KedroContext.catalog`	Read-only property referring to Kedro’s `DataCatalog` for this context.
`KedroContext.config_loader`	Read-only property referring to Kedro’s `ConfigLoader` for this context.
`KedroContext.io`	Read-only alias property referring to Kedro’s `DataCatalog` for this context.
`KedroContext.params`	Read-only property referring to Kedro’s parameters for this context.
`KedroContext.pipeline`	Read-only property for an instance of Pipeline.
`KedroContext.pipelines`	Read-only property for an instance of Pipeline.
`KedroContext.project_name`	Abstract property for Kedro project name.
`KedroContext.project_path`	Read-only property containing Kedro’s root project directory.
`KedroContext.project_version`	Abstract property for Kedro version.

Methods

`KedroContext.__init__`(project_path[, env])	Create a context object by providing the root of a Kedro project and the environment configuration subfolders (see `kedro.config.ConfigLoader`)
`KedroContext.run`([tags, runner, node_names, …])	Runs the pipeline with a specified runner.

CONF_ROOT = 'conf'

__init__(project_path, env=None)[source]¶

Create a context object by providing the root of a Kedro project and the environment configuration subfolders (see kedro.config.ConfigLoader)

Raises:	`KedroContextError` – If there is a mismatch between Kedro project version and package version.
Parameters:	project_path (`Union`[`Path`, `str`]) – Project path to define the context for. env (`Optional`[`str`]) – Optional argument for configuration default environment to be used for running the pipeline. If not specified, it defaults to “local”.

catalog¶

Read-only property referring to Kedro’s DataCatalog for this context.

Return type:	`DataCatalog`
Returns:	DataCatalog defined in catalog.yml.

config_loader¶

Read-only property referring to Kedro’s ConfigLoader for this context.

Return type:	`ConfigLoader`
Returns:	Instance of ConfigLoader.

io¶

Read-only alias property referring to Kedro’s DataCatalog for this context.

Return type:	`DataCatalog`
Returns:	DataCatalog defined in catalog.yml.

params¶

Read-only property referring to Kedro’s parameters for this context.

Return type:	`Dict`[`str`, `Any`]
Returns:	Parameters defined in parameters.yml.

pipeline¶

Read-only property for an instance of Pipeline.

Return type:	`Pipeline`
Returns:	Defined pipeline.

pipelines¶

Read-only property for an instance of Pipeline.

Return type:	`Dict`[`str`, `Pipeline`]
Returns:	A dictionary of defined pipelines.

project_name¶

Abstract property for Kedro project name.

Return type:	`str`
Returns:	Name of Kedro project.

project_path¶

Read-only property containing Kedro’s root project directory.

Return type:	`Path`
Returns:	Project directory.

project_version¶

Abstract property for Kedro version.

Return type:	`str`
Returns:	Kedro version.

run(tags=None, runner=None, node_names=None, from_nodes=None, to_nodes=None, from_inputs=None, load_versions=None, pipeline_name=None)[source]¶

Runs the pipeline with a specified runner.

Parameters:	tags (`Optional`[`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 (`Optional`[`AbstractRunner`]) – An optional parameter specifying the runner that you want to run the pipeline with. node_names (`Optional`[`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 (`Optional`[`Iterable`[`str`]]) – An optional list of node names which should be used as a starting point of the new `Pipeline`. to_nodes (`Optional`[`Iterable`[`str`]]) – An optional list of node names which should be used as an end point of the new `Pipeline`. from_inputs (`Optional`[`Iterable`[`str`]]) – An optional list of input datasets which should be used as a starting point of the new `Pipeline`. load_versions (`Optional`[`Dict`[`str`, `str`]]) – An optional flag to specify a particular dataset version timestamp to load. pipeline_name (`Optional`[`str`]) – Name of the `Pipeline` to execute. Defaults to “__default__”.
Raises:	`KedroContextError` – If the resulting `Pipeline` is empty or incorrect tags are provided.
Return type:	`Dict`[`str`, `Any`]
Returns:	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.