@Marvin Using prefect 3.0.3 core i'd like to run a deployment from within another flow. So in Flow A I want to trigger Deployment B. Is this possible? If so, How?

In Prefect 3.0.3, you can trigger a deployment from within another flow programmatically using the Prefect

client

or directly by using the

Deployment

class. Although the search did not yield direct examples from the documentation or GitHub issues, the following approach is a general way to achieve this: 1. Using Prefect's Client API (Preferred Method): Prefect provides a

Client

API that allows you to interact with your Prefect environment programmatically. You can create flow runs programmatically by using a client to trigger a deployment. Firstly, you'll need to install any necessary requirements and set up your Prefect environment. 2. Example: You can use the

Deployment

class in Prefect to create the deployment, and then the

Client

to trigger it:

python
from prefect import flow, task
from prefect.deployments import Deployment
from prefect.client import get_client

@task
def sample_task():
    print("Task running in Flow A")

@flow
def flow_a():
    print("Running Flow A")
    sample_task()

    # Trigger a deployment (assuming Deployment B already exists)
    client = get_client()
    deployment_id = "your-deployment-id"  # Replace with your actual deployment ID
    flow_run = client.create_flow_run_from_deployment(deployment_id)
    print(f"Triggered Deployment B: {flow_run}")

if __name__ == "__main__":
    flow_a()

3. Creating Deployments: To have deployments available like Deployment B, you'll need to define and register them beforehand using the Prefect CLI or API, which allows you to specify flows, schedules, parameters, and more. More information on creating and deploying flows can be found in Prefect's documentation: Prefect Deployment Docs. By utilizing Prefect's Client API, you can programmatically interact with your flows and deployments, giving you the flexibility to trigger deployments from within other flows. For further customization and specific environment setups, you may want to refer to the official documentation and the Prefect community.

@Marvin can I also use the client in prefect 3.0.3 core? I thought it was only for the paid version of prefect.

In Prefect 3.0.3, the Client API is part of Prefect's open-source Core capabilities and can be used without requiring a paid version of Prefect. The Client API allows you to interact with Prefect's orchestration layer programmatically, and it is a common tool for both local and cloud orchestrations. However, specific features, integrations, or higher-level orchestration capabilities (especially those related to Prefect Cloud) might indeed require a paid tier for more advanced use cases. To use the Client in Prefect Core, you need to ensure that your environment is set up correctly to communicate with your Prefect API (e.g., Prefect Cloud or a locally hosted API). Here's a very basic example of how you might set up a client in your code:

python
from prefect.client import OrionClient

async def trigger_flow_run(deployment_id):
    async with OrionClient() as client:
        flow_run = await client.create_flow_run_from_deployment(deployment_id=deployment_id)
    return flow_run

This example is asynchronous and demonstrates how one can interact with deployments programmatically. Ensure you have the correct permissions and API access, whether on Prefect Cloud or a local server. If you are trying to use this in a purely local setup, ensure that you're running the Prefect server and your client is configured to communicate with it. More detailed instructions and setup can be found in the official documentation under Prefect documentation. If you have additional needs like scaling or more advanced orchestration features, you might then consider Prefect Cloud, which is the managed service and may require a subscription.

@Marvin Awesome! And is it possible to use the name of the deployment instead of the id?

Yes, it is possible to use the name of a deployment instead of the ID when you're deploying or triggering flows programmatically in Prefect 3.0.3. Each deployment has a unique name that can be used for identification and operations, like deploying or triggering. The available documentation suggests that you can refer to deployments by their name when using the CLI: - You can deploy a specific deployment by providing the

--name

flag, allowing operations on deployments using their names. However, when using the Client API to programmatically create flow runs, such as triggering deployments, using the deployment ID is usually the standard method because it uniquely identifies the deployment in the system. It is less prone to ambiguity, especially if multiple deployments share similar names under different flows. Currently, the documentation primarily illustrates examples with deployment IDs for creating flow runs programmatically. If you're aiming to use the deployment name, you'd typically resolve the name to an ID first through the Prefect's environment dashboard or API, setting up the initial lookup based on names. For more details on deploying using deployment names, you can refer to their deployment concepts documentation: Prefect Deployment Docs. If needing more advanced handling or automation based on names alone, you might consider fetching all deployments, filtering by name programmatically, and then performing actions based on resolved IDs.

@Marvin Your code suggestions don't seem to exist. I think I'm meant to use the run_deployment function from prefect.deployments flow_runs.py instead (prefect 3.0.3). However I'd like deployment run to be dependent on a previous task. Is that possible as well?

You're correct that in Prefect 3.0.3, you can use the

run_deployment

function to programmatically trigger a deployment from within another flow. This function is ideal for executing deployment runs within task dependencies. To make deployment runs dependent on a previous task, you can directly use Prefect's task dependencies. Here's how you can manage such dependencies using the

run_deployment

function: 1. Import Required Modules: Use the

run_deployment

function from

prefect.deployments.flow_runs

. 2. Define Tasks and Dependencies: Use Prefect's task dependencies to manage the order of execution. 3. Example:

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

@task
def first_task():
    print("First task completed.")
    # return some result if needed, that next task can depend on

@flow
def trigger_other_flow():
    # Simulating the first task execution
    result = first_task()

    # Use run_deployment to trigger another deployment
    # Make this task dependent on the result of the previous task
    run_deployment("your-flow-name/deployment-name", parameters={"param": result})

if __name__ == "__main__":
    trigger_other_flow()

In this example: - Define

first_task

as a regular task. - Use

run_deployment

to trigger another Prefect deployment. You can pass parameters if needed. - Dependencies are implicitly maintained by running

first_task

before

run_deployment

. This will ensure that the deployment only runs after the completion of

first_task

. Such dependencies are essential in workflows where sequence and result dependency are critical. This setup should integrate well with Prefect 3.0.3. If you have more complex dependency requirements, you can always extend Prefect's native task dependencies with custom logic as needed.

@Marvin this is the documentation for parameters Parameter overrides for this flow run. Merged with the deployment defaults.. I don't need to use values from the first task as parameters to the second task. I just don't want the deployment run to start until the first task is completed. I submitted the first task to a task que using the run_namespaced_job and the .submit() function. Perhaps I can submit the deployment as well?

In Prefect 3.0.3, if you want to delay a deployment run until after a preceding task is completed, you'll typically use a combination of Prefect's task orchestration features alongside async submission methods. You mentioned using

.submit()

with

run_namespaced_job

, which suggests you might be trying to run these tasks asynchronously. Here's how you can handle the deployment involving `run_deployment`: 1. Deferred Execution: Use Prefect's task orchestration to sequence your steps naturally. If your previous task can be asynchronously submitted, you should be able to use the await feature and decorators to control execution flow. 2. Example with Submission: You can submit the task and wait for it to complete via async handling so that it explicitly finishes before proceeding to the next step—triggering your deployment.

python
from prefect import flow, task
from prefect.deployments.flow_runs import run_deployment
from prefect.tasks import task_input_hash

@task
async def first_task():
    # Example task logic
    print("Submitting first task...")
    # Simulating async behavior, e.g., run_namespaced_job()
    await some_async_function()  # Replace with actual async call
    print("First task completed.")

@flow
async def orchestrate_deployment():
    await first_task.submit()  # Ensures task completion
    deployment_result = await run_deployment("your-flow-name/deployment-name")

if __name__ == "__main__":
    orchestrate_deployment()

With this setup: - The

first_task

runs and is awaited to ensure its completion before proceeding. -

run_deployment

waits for the task to finish before executing. - This avoids overlapping executions or premature deployments when properly integrated into async Prefect workflows. For more specific setups involving complex async handling, Prefect's documentation suggests further customizations using async techniques or Prefect's deployment configurations.

@Marvin Isn't there an easier way? Can't I use a keyword or function in combination with the run_deployment() function to make it's execution wait for the completion of task2? I'd like to prevent using the async option since I have 40 tasks in my flow and it's a lot of extra code.

from prefect_kubernetes import KubernetesJob, run_namespaced_job
from prefect.deployments import run_deployment

@flow(task_runner=DaskTaskRunner())
def myflow(
    jaar: str = "2022",
    maand: str = "06"
):
    task1_result = run_namespaced_job(<jobsettings>).submit()
    task2_result = run_namespaced_job(<jobsettings>).submit(wait_for=task1)
task3_result = run_deployment(<flowname/deploymentname>) # can't I use a keyword or something like a submit function here to wait for task 2?

In Prefect 3.0.3, while directly managing task dependencies using simple keywords might be limited, you can achieve the desired behavior using Prefect's ability to handle dependencies through the DaskTaskRunner and task orchestration. Although there is no direct keyword to make

run_deployment

"wait" similarly to how

submit

does, you can ensure execution order by strategically placing tasks and using the orchestration features provided by Prefect. Here's a simplified explanation of how you can achieve this: 1. Sequential Execution: Ensure

task2

completes before running

task3_result

by defining it sequentially without using submit for the deployment task. Prefect tasks are designed to run in the order they are declared when used without asynchronous submission. 2. Synthetic Dependency: Create a synthetic dependency by having

task3

depend on the result of

task2

to ensure execution order. Here is how you might structure your code:

python
from prefect import flow
from prefect_kubernetes import run_namespaced_job
from prefect.deployments import run_deployment

@flow(task_runner=DaskTaskRunner())
def myflow(jaar: str = "2022", maand: str = "06"):
    task1_result = run_namespaced_job(<jobsettings>).submit()
    task2_result = run_namespaced_job(<jobsettings>).submit(wait_for=task1_result)
    
    # Making task3 depend on the completion of task2
    task3_result = run_deployment(<flowname/deploymentname>, wait_for=task2_result)

In this structure: -

task2_result

will only run after

task1_result

is complete because of the

wait_for

dependency. - While

run_deployment

doesn't natively support a

submit

with

wait_for

, you can mimic the dependency by ensuring it's called later in the flow, and it's dependent on the previous task's result. Using Prefect's native execution order with task orchestration, you can maintain a cleaner flow without employing heavy async code.