@Marvin does

run_deployment

still work in prefect 3.x

The function

run_deployment

does not seem to exist explicitly in Prefect 3.x documentation based on my search results. In Prefect 3.x, the focus is on using deployments to manage and execute workflows, including triggering flow runs remotely. For more detailed guidance on handling deployments and running flows, you can check out the Prefect 3.x documentation here: Prefect 3.x Deployments Overview. If you have specific needs or encounter issues in Prefect 3.x deployment processes, it might be beneficial to check the latest discussions or open issues directly on Prefect's GitHub repository, though I'm currently unable to access those directly due to permission issues. Isn't technology a delight? Like trying to pet a cat that doesn't really want to be caught.

@Marvin is

create_flow_run_from_deployment

in 3.x the same as

run_deployment

in 2.x

In Prefect 3.x, the function

create_flow_run_from_deployment

is used to trigger new runs based on existing deployments. This function essentially handles what

run_deployment

did in Prefect 2.x by initiating flow runs. The emphasis in 3.x is more on managing deployments through server-side representations that define when, where, and how workflows execute. While the underlying mechanics may differ slightly, conceptually,

create_flow_run_from_deployment

serves the same purpose as orchestrating and running workflows as

run_deployment

would in the earlier version. For more information on handling deployments and flow runs in Prefect 3.x, check out their documentation here: Prefect 3.x Deployments Overview. It's like a cozy library of knowledge without the musty smell! If only finding information on all topics was as straightforward.

@Marvin how can i use

create_flow_run_from_deployment

in python

To use

create_flow_run_from_deployment

in Python with Prefect 3.x, you typically need to interact with Prefect's API to trigger a flow run from an existing deployment. Unfortunately, the search didn't bring back comprehensive examples specific to that function. However, here's a general outline of how you might achieve this:

python
from prefect.client import get_client

async def trigger_flow_run(deployment_id):
    async with get_client() as client:
        # Create a flow run from the deployment
        flow_run = await client.create_flow_run_from_deployment(deployment_id=deployment_id)
        print("Flow run created:", flow_run)

# Replace 'your-deployment-id' with your actual deployment id
await trigger_flow_run(deployment_id='your-deployment-id')

Here's the main logic: 1. Fetch the Prefect client using

get_client()

. 2. Use

create_flow_run_from_deployment

to initiate the flow run based on the deployment id. You can explore more about managing deployments and flow runs in Prefect's deployment documentation. Just remember, consulting documentation is akin to asking Santa for gifts—it takes a bit of belief that you’ll find what you need!

@Marvin do i have to do this asyncronously, or can i do something like

python
from prefect.client import get_client

def _run_deployment(overrides):
with get_client() as client:
state =    client.create_flow_run_from_deployment(
        deployment_id=GCMC_DEPLOYMENT_NAME,
        parameters={"hydra_overrides": overrides},
        poll_interval=30,
        as_subflow=True,
    ).state

In Prefect 3.x, the

create_flow_run_from_deployment

function is asynchronous, and typically, you would handle it asynchronously within an

async

context. However, you can execute asynchronous functions in a synchronous context using a library like

asyncio

, or you could modify the logic to work with an asynchronous pattern. Here’s a simple way to wrap your asynchronous call to

create_flow_run_from_deployment

so that it can be used in a synchronous function:

python
import asyncio
from prefect.client import get_client

def run_deployment(overrides):
    async def _run():
        async with get_client() as client:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id="GCMC_DEPLOYMENT_NAME",
                parameters={"hydra_overrides": overrides},
                poll_interval=30,
                as_subflow=True,
            )
            return flow_run.state
    
    return asyncio.run(_run())

This wraps the async function call in an

asyncio.run

, which allows you to call

run_deployment()

in a regular synchronous Python environment. It's like a data engineering teleportation device, getting your async code to sync places without breaking a sweat! You might want to refer to the Prefect 3.x documentation for more detailed understanding and specific use cases.