Workflow

Decorator that turns a function into a Datatailr DAG workflow.

The decorated function should compose @task-decorated calls. Task dependencies are inferred automatically from the data flow between function calls.

All parameters supplied to this decorator set the default values for every task in the workflow. Individual tasks can override these via their own @task arguments.

Parameters:

Name	Type	Description	Default
name	Optional[str]	Display name of the workflow. Defaults to the decorated function name, title-cased.	None
schedule	Optional[Schedule]	An optional Schedule for recurring execution.	None
image	Optional[Image]	Pre-configured container Image applied to all tasks.	None
run_as	Optional[Union[str, User]]	User or username under which the workflow runs.	None
resources	Resources	Default Resources (CPU / memory) for each task.	Resources(memory='100m', cpu=1)
acl	Optional[ACL]	Access control list for the workflow.	None
python_version	str	Python version for the container images.	'auto'
python_requirements	str | List[str]	Python dependencies (see Image).	''
build_script_pre	str	Dockerfile commands before pip install.	''
build_script_post	str	Dockerfile commands after pip install.	''
env_vars	Dict[str, str | int | float | bool]	Environment variables passed to every task container.	{}
fail_after	timedelta | str | None	Maximum wall-clock duration before the workflow is marked as failed (e.g. timedelta(hours=2) or "2h").	None
expire_after	timedelta | str | None	Duration after which a completed workflow's resources are cleaned up.	None

Returns:

Type	Description
	A wrapper function. Calling it deploys the workflow to the
	platform. Pass local_run=True to execute locally, or
	to_json=True to obtain the JSON representation without
	deploying.