prefect.deployments.flow_runs

Functions

arun_deployment ^{<a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/deployments/flow_runs.py#L51" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a>}

arun_deployment(name: Union[str, UUID], client: Optional['PrefectClient'] = None, parameters: Optional[dict[str, Any]] = None, scheduled_time: Optional[datetime] = None, flow_run_name: Optional[str] = None, timeout: Optional[float] = None, poll_interval: Optional[float] = 5, tags: Optional[Iterable[str]] = None, idempotency_key: Optional[str] = None, work_queue_name: Optional[str] = None, as_subflow: Optional[bool] = True, job_variables: Optional[dict[str, Any]] = None) -> 'FlowRun'

Asynchronously create a flow run for a deployment and return it after completion or a timeout.

By default, this function blocks until the flow run finishes executing. Specify a timeout (in seconds) to wait for the flow run to execute before returning flow run metadata. To return immediately, without waiting for the flow run to execute, set timeout=0.

Note that if you specify a timeout, this function will return the flow run metadata whether or not the flow run finished executing.

If called within a flow or task, the flow run this function creates will be linked to the current flow run as a subflow. Disable this behavior by passing as_subflow=False.

Args:

• name: The deployment id or deployment name in the form: "flow name/deployment name"
• client: An optional PrefectClient to use for API requests.
• parameters: Parameter overrides for this flow run. Merged with the deployment defaults.
• scheduled_time: The time to schedule the flow run for, defaults to scheduling the flow run to start now.
• flow_run_name: A name for the created flow run
• timeout: The amount of time to wait (in seconds) for the flow run to complete before returning. Setting timeout to 0 will return the flow run metadata immediately. Setting timeout to None will allow this function to poll indefinitely. Defaults to None.
• poll_interval: The number of seconds between polls
• tags: A list of tags to associate with this flow run; tags can be used in automations and for organizational purposes.
• idempotency_key: A unique value to recognize retries of the same run, and prevent creating multiple flow runs.
• work_queue_name: The name of a work queue to use for this run. Defaults to the default work queue for the deployment.
• as_subflow: Whether to link the flow run as a subflow of the current flow or task run.
• job_variables: A dictionary of dot delimited infrastructure overrides that will be applied at runtime; for example env.CONFIG_KEY=config_value or namespace='prefect'

run_deployment ^{<a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/deployments/flow_runs.py#L244" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a>}

run_deployment(name: Union[str, UUID], client: Optional['PrefectClient'] = None, parameters: Optional[dict[str, Any]] = None, scheduled_time: Optional[datetime] = None, flow_run_name: Optional[str] = None, timeout: Optional[float] = None, poll_interval: Optional[float] = 5, tags: Optional[Iterable[str]] = None, idempotency_key: Optional[str] = None, work_queue_name: Optional[str] = None, as_subflow: Optional[bool] = True, job_variables: Optional[dict[str, Any]] = None) -> 'FlowRun'

Create a flow run for a deployment and return it after completion or a timeout.

This function will dispatch to arun_deployment when called from an async context.

By default, this function blocks until the flow run finishes executing. Specify a timeout (in seconds) to wait for the flow run to execute before returning flow run metadata. To return immediately, without waiting for the flow run to execute, set timeout=0.

Note that if you specify a timeout, this function will return the flow run metadata whether or not the flow run finished executing.

If called within a flow or task, the flow run this function creates will be linked to the current flow run as a subflow. Disable this behavior by passing as_subflow=False.

Args:

• name: The deployment id or deployment name in the form: "flow name/deployment name"
• client: An optional PrefectClient to use for API requests. This is ignored when called from a synchronous context.
• parameters: Parameter overrides for this flow run. Merged with the deployment defaults.
• scheduled_time: The time to schedule the flow run for, defaults to scheduling the flow run to start now.
• flow_run_name: A name for the created flow run
• timeout: The amount of time to wait (in seconds) for the flow run to complete before returning. Setting timeout to 0 will return the flow run metadata immediately. Setting timeout to None will allow this function to poll indefinitely. Defaults to None.
• poll_interval: The number of seconds between polls
• tags: A list of tags to associate with this flow run; tags can be used in automations and for organizational purposes.
• idempotency_key: A unique value to recognize retries of the same run, and prevent creating multiple flow runs.
• work_queue_name: The name of a work queue to use for this run. Defaults to the default work queue for the deployment.
• as_subflow: Whether to link the flow run as a subflow of the current flow or task run.
• job_variables: A dictionary of dot delimited infrastructure overrides that will be applied at runtime; for example env.CONFIG_KEY=config_value or namespace='prefect'