< Marvin> Using run deployment how can I trigger a series of Prefect Community #ask-marvin

<@ULVA73B9P> Using run_deployment(), how can I tri...

Aj Floersch

10/17/2024, 1:29 PM

@Marvin Using run_deployment(), how can I trigger a series of sub-flows to run in parallel (without waiting for the prior one to finish before starting the next), but have the parent flow wait for all sub-flows to finish before being marked complete?

Marvin

10/17/2024, 1:29 PM

To help you with your question regarding

run_deployment()

and triggering sub-flows in parallel, I need to determine the specific version of Prefect you're using since the syntax and available functions might differ. Could you please let me know if you're using Prefect 2.x or 3.x? If you're unsure, any detail about recent updates or other functionalities might help me infer the version.

Aj Floersch

10/17/2024, 1:32 PM

@Marvin Currently, using Prefect 2 - additionally, my sub-flows are performing I/O operations using a library based on the

requests

library, of which there is not an alternative. Adding this for context incase any of your recommendations involve async.

Marvin

10/17/2024, 1:33 PM

To run sub-flows in parallel using the

run_deployment()

function within Prefect 2.x, and ensure that the parent flow waits for all sub-flows to finish before completing, you can utilize the

submit

method to run these deployments concurrently. This approach leverages task concurrency provided by Prefect. Here's a simple guideline on how to achieve this: 1. Create a Concurrent Task for Each Sub-Flow: Use

task.submit()

to trigger each deployment as a task. This will allow the tasks (representing the sub-flow runs) to execute in parallel. 2. Use
wait_for
Parameter: Your parent task or flow should use the

wait_for

parameter to await the completion of all sub-flow tasks, ensuring that the parent flow run does not finish until every sub-flow run is complete. Here is an example using Prefect 2.x:

Copy code

python
from prefect import flow, task
from prefect.deployments import run_deployment

@flow
def parent_flow():
    # Submitting sub-flow deployments as tasks to run them in parallel
    subflow_run_1 = run_subflow.handle.run_deployment_task.submit("sub_flow_1", "my_deployment_1", {"param": "value1"})
    subflow_run_2 = run_subflow.handle.run_deployment_task.submit("sub_flow_2", "my_deployment_2", {"param": "value2"})

    # A task that waits for all parallel sub-flows to finish
    final_task(wait_for=[subflow_run_1, subflow_run_2])

@task
def final_task():
    print("All sub-flows are complete!")

@task
def run_subflow.handle.run_deployment_task(flow_name: str, deployment_name: str, parameters: dict):
    return run_deployment(name=f"{flow_name}/{deployment_name}", parameters=parameters)

parent_flow()

Notes: - Although Prefect allows you to run tasks asynchronously, in your case, if the sub-flows perform I/O operations with a synchronous library like

requests

, they won't benefit from async paradigms. However, Prefect's task-based concurrency should still help, as each task is scheduled independently. - Using the

wait_for

parameter in the final task ensures that it waits for all the sub-flow tasks to complete. For more detailed behavior on managing deployments and subflows, you can refer to these resources: 1. Prefect Documentation on Deployments 2. Handling Subflows and Concurrency Let me know if you have further questions or need more specific guidance!

Aj Floersch

10/17/2024, 1:39 PM

@Marvin Can you modify this solution to represent the subflows being generated in a loop with an indeterminant number of potentials subflows triggered?

Marvin

10/17/2024, 1:39 PM

Certainly! If you want to dynamically trigger an indeterminate number of sub-flows in a loop, you can adjust the solution to iterate over your sub-flows and use a list to gather their futures. This allows the parent flow to wait for all sub-flows run dynamically. Here's how you can do it:

Copy code

python
from prefect import flow, task
from prefect.deployments import run_deployment

@flow
def parent_flow(subflow_specs):
    # List to hold the future objects representing each subflow run
    subflow_runs = []

    for spec in subflow_specs:
        # Using submit to add each subflow run to the list
        subflow_run = run_subflow_task.submit(spec['flow_name'], spec['deployment_name'], spec['parameters'])
        subflow_runs.append(subflow_run)

    # A task that waits for all parallel sub-flows to finish
    final_task(wait_for=subflow_runs)

@task
def final_task():
    print("All sub-flows are complete!")

@task
def run_subflow_task(flow_name: str, deployment_name: str, parameters: dict):
    return run_deployment(name=f"{flow_name}/{deployment_name}", parameters=parameters)

# Example subflow specification
subflow_specs = [
    {"flow_name": "sub_flow_1", "deployment_name": "my_deployment_1", "parameters": {"param": "value1"}},
    {"flow_name": "sub_flow_2", "deployment_name": "my_deployment_2", "parameters": {"param": "value2"}},
    # Add more subflow_specs as needed
]

parent_flow(subflow_specs)

Key Modifications: - subflow_specs
: This is a list where each entry contains specifications for a sub-flow you want to run. You can populate this list based on runtime conditions or configurations. - Loop Through
subflow_specs
: The loop iterates over this list, submitting each subflow deployment run as a separate task. - List for Subflows: Collect futures in the

subflow_runs

list and use this in the

wait_for

parameter of the

final_task

. This approach allows you to trigger any number of sub-flows dynamically, and the parent flow will wait until all of them have finished running. Make sure the input specs for sub-flows (

subflow_specs

) correctly define all necessary parameters for your specific deployments.

Aj Floersch

10/17/2024, 1:45 PM

@Marvin Lastly, how would I ensure that the parent flow is marked successfully completed as long as the all sub flows are triggered? Meaning, so long as there wasn't an error in the parent flow itself, it will be marked completed - even if a subflow fails.

Marvin

10/17/2024, 1:45 PM

To ensure that your parent flow in Prefect is marked as successfully completed regardless of the success or failure of sub-flows, you need to handle potential exceptions from sub-flows gracefully. One way to do this is to catch exceptions within the task responsible for running the deployments and ensure it always returns a successful state. This can be done using a try-except block around the

run_deployment

call. Here's how you might modify the solution:

Copy code

python
from prefect import flow, task
from prefect.deployments import run_deployment
from prefect.states import State

@flow
def parent_flow(subflow_specs):
    # List to hold the future objects representing each sub-flow run
    subflow_runs = []

    for spec in subflow_specs:
        subflow_run = run_subflow_task.submit(spec['flow_name'], spec['deployment_name'], spec['parameters'])
        subflow_runs.append(subflow_run)

    # A task that waits for all parallel sub-flows to finish
    final_task(wait_for=subflow_runs)

@task
def final_task():
    print("All sub-flows are triggered!")

@task
def run_subflow_task(flow_name: str, deployment_name: str, parameters: dict):
    try:
        run_deployment(name=f"{flow_name}/{deployment_name}", parameters=parameters)
    except Exception as e:
        # Log the error - ensures that the parent task flow does not fail
        print(f"Sub-flow {flow_name}/{deployment_name} failed: {e}")
    # Return a custom state indicating the task completed to avoid failure propagation
    return State("Completed", message=f"Sub-flow {flow_name}/{deployment_name} execution attempted")

# Example subflow specification
subflow_specs = [
    {"flow_name": "sub_flow_1", "deployment_name": "my_deployment_1", "parameters": {"param": "value1"}},
    {"flow_name": "sub_flow_2", "deployment_name": "my_deployment_2", "parameters": {"param": "value2"}},
    # Add more subflow_specs as needed
]

parent_flow(subflow_specs)

Key Adjustments: - Error Handling in Sub-Flow Task: The

try-except

block in the

run_subflow_task

task catches exceptions and logs them, ensuring that the task itself completes successfully regardless of errors in the sub-flow deployments. - Custom Task State Return: Use

State("Completed")

to indicate that the task representing sub-flow execution has finished its operation regardless of operational success. This setup ensures that even if a sub-flow fails, your parent flow will complete successfully. It provides a way to log failures for sub-flows without impacting the state of the parent flow.

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