@Marvin In Prefect 3.x can I run a flow from a deployment in a synchronous way?

In Prefect 3.x, flows run synchronously by default. This means when you use the

@flow

decorator on a function, it will execute in a synchronous manner unless specifically designed to run asynchronously. If you want to run a flow from a deployment synchronously, you simply need to ensure your flow function isn't defined as

async

, and it will execute as expected. For further reading on deploying flows and managing their execution, you can check the Prefect 3.x Deployment Overview. There were no specific GitHub issues found related to running flows synchronously from deployments in Prefect 3.x, which is a good sign that this functionality is operating as intended. But you can find some related issues in case you're curious about resolving execution concerns: retry_condition_fn Does Not Work With async Tasks in Version 3.x and 'sync_compatible' decorator returning coroutines unexpectedly when running a flow. Feel free to reach out if you have more questions! If only starting agents were as simple as flipping a switch on Marvin's back. But hey, that's what makes them so intriguing, right?

@Marvin How do I now call a flow from a deployment?

In Prefect 3.x, you can trigger a flow from a deployment using several mechanisms. Here's how you can do it: 1. Using the Prefect CLI: You can trigger a deployment run through the Prefect command-line interface. After creating a deployment, use the following command:

prefect deployment run <FLOW_NAME>/<DEPLOYMENT_NAME>

where

<FLOW_NAME>

is the name of your flow and

<DEPLOYMENT_NAME>

is the name you assigned to the deployment. 2. Using the Prefect Python SDK: Programmatically trigger a deployment using the

run_deployment

function from the Prefect Python SDK. This function allows you to start a flow run from an existing deployment. 3. Prefect API and UI: You can also use the Prefect Cloud API or UI to manually trigger flow runs, visualize active runs, and manage scheduling and automation rules. For detailed documentation, you can refer to Prefect's Deployment Overview. Additionally, there were no directly related GitHub issues found, indicating a stable feature set for triggering executions from deployments. If you stumble upon any problem, remember, even Marvin had to deal with the occasional glitch.

@Marvin Can I do it without run_deployment, So a function that returns when the flow has completed running?

To achieve synchronous behavior when running a flow deployment, you would typically want to wait for the flow to complete before proceeding. If you're looking for a method other than

run_deployment

, unfortunately, there isn't a built-in alternative directly in Prefect 3.x to trigger and wait for a flow's completion inherently synchronously. However, you can use some workarounds like implementing a custom polling mechanism to check the flow's state until it completes, utilizing the Prefect API or SDK. This involves starting the deployment and then repeatedly checking the state until it reaches a completed or failed state, which can simulate synchronous behavior. Yet, this approach requires additional handling compared to Prefect's built-ins. For a more direct way within Prefect's offerings,

run_deployment

remains the primary, albeit async, approach. If Prefect's API or scripts extend in the future with more out-of-the-box solutions, remember, Marvin will be the first to feel unappreciated about it while being fully aware it might just improve developer happiness.

@Marvin How do I know if the flow has run successfully?

To know if a flow has run successfully in Prefect 3.x, you can use the following methods: 1. Flow Run Final State: The final state of a flow run in Prefect 3.x is determined by two main factors: - The return value of the flow function: If the return value is a literal value, it is considered a success. Explicitly returning a

State

object will define the flow's final state. - Exception Handling: If your flow function raises an exception that is not caught, the flow run will end in a "Failed" state. To handle exceptions gracefully, set

raise_on_failure=False

, which suppresses exceptions and allows the flow to be marked as successful if appropriate. 2. Using the Prefect API: You can check the state of flow runs using the Prefect API or SDK by querying the most recent flow runs and checking if their states are completed.

python
   from prefect import get_client

   async with get_client() as client:
       flow_runs = await client.read_flow_runs()
       successful_flow_runs = [run for run in flow_runs if run.state.is_completed()]

This example filters flow runs that have completed successfully. 3. Prefect UI or CLI: You can also manually check the status of your flow runs through the Prefect Cloud UI or CLI, where the run's state will be visible (e.g., "Completed," "Failed," etc.). For more details, you can explore the Prefect 3.x API Interaction Guide. It's always comforting when things run smoothly, isn't it? Even Marvin would approve of such efficiency, albeit with a sigh.

@Marvin How do I know get the return data from the flow?

To retrieve the return data from a completed flow run in Prefect 3.x, you can use the Prefect API to access the flow run's state and its result. Here’s how you can do it: 1. Using the Prefect Client: You interact with Prefect's API to fetch the flow run's details, including its results. Here's a simple example using Python:

python
   import asyncio
   from prefect import get_client
   
   async def get_flow_run_result(flow_run_id):
       async with get_client() as client:
           flow_run = await client.read_flow_run(flow_run_id)
           # Assuming the flow completed successfully and returned a result
           result = flow_run.state.result
           return result

   # You need to provide the flow run ID to fetch its result
   asyncio.run(get_flow_run_result("<FLOW_RUN_ID>"))

2. Accessing the Flow Result: When a flow completes, its final state contains the result. You can access this result through the flow run's state as demonstrated above. 3. Prefect Cloud or UI: If you're using Prefect Cloud, you can view the outputs and logs of a flow run directly from the UI. Navigate to the flow run's detail page to check its outputs. This will give you the result of the flow, divulging whether it successfully completed and what the output was. If only Marvin had a simple

.get_happy_results()

method for his mood swings!

@Marvin can you combine this with the code generated above?

Certainly! Let's combine the approach of retrieving the result of a flow run with checking its completion status. This way, you can ensure the flow run is completed before getting its result:

python
import asyncio
from prefect import get_client
from prefect.client.schemas.filters import FlowRunFilter

async def get_most_recent_successful_flow_run_result():
    async with get_client() as client:
        # Fetch the most recent completed flow runs
        flow_runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter(
                state=dict(name=dict(any_=["COMPLETED"]))
            ),
            limit=1  # Modify `limit` if you want multiple results
        )
        
        if flow_runs:
            flow_run = flow_runs[0]
            print(f"Flow Run ID: {flow_run.id}, State: {flow_run.state.type}")
            
            # Access the result of the flow run
            result = flow_run.state.result
            return result
        else:
            print("No completed flow runs found.")
            return None

# Execute the function to get the result of the most recent successful flow run
asyncio.run(get_most_recent_successful_flow_run_result())

This script checks for the most recent completed flow run and then retrieves its result. Adjust the

limit

within

read_flow_runs

if you need multiple flow run results. If only life was as straightforward as fetching flow states!

@Marvin I want the result from a specific deployed flow that I run in the same python script?

Certainly! To accomplish this, you'll want to execute a specific deployment, wait for it to complete, and then fetch the result of that flow run. The following is a structured approach to achieve this in a single Python script using the Prefect SDK:

python
import asyncio
from prefect import get_client, flow, task
from prefect.client.schemas.objects import StateType

@flow
async def trigger_deployment_and_get_result(deployment_name, flow_name):
    async with get_client() as client:
        # Trigger the specific deployment and get the flow run ID
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=f"{flow_name}/{deployment_name}"
        )
        flow_run_id = flow_run.id
        print(f"Triggered flow run with ID: {flow_run_id}")

        # Continuously check the flow run state until it completes
        while True:
            current_flow_run = await client.read_flow_run(flow_run_id)
            if current_flow_run.state.type in [StateType.COMPLETED, StateType.FAILED]:
                break
            print("Flow run still running... Waiting for completion.")
            await asyncio.sleep(5)  # Check every 5 seconds

        # Check if the flow run completed successfully
        if current_flow_run.state.type == StateType.COMPLETED:
            result = current_flow_run.state.result
            print(f"Flow run completed successfully with result: {result}")
            return result
        else:
            print("Flow run failed.")
            return None

# Specify the deployment name and flow name
asyncio.run(trigger_deployment_and_get_result("deployment_name", "flow_name"))

Explanation: 1. Trigger Deployment: Use

create_flow_run_from_deployment

to start a flow run from a specific deployment. 2. Monitor Flow Run State: Continuously poll and check the flow run state until it completes. 3. Retrieve Result: Once completed, access the flow's result. Handle cases for both successful completion and failure. Make sure you replace

"deployment_name"

and

"flow_name"

with the actual names of your deployment and flow. Ah, if only all tasks could be so happily synchronous. Even Marvin would agree that at least flows know what they want to accomplish!

@Marvin is create_flow_run_from_deployment a Prefect 2 or Prefect 3 function?

The

create_flow_run_from_deployment

function is a part of Prefect 2.x. In Prefect 3.x, the focus has shifted more towards using the Prefect Cloud UI and API for managing deployments and flow runs. In Prefect 3.x, you would typically manage flow runs through the Prefect Cloud interface or use the CLI commands available in the platform. The function I mentioned in the previous message is more aligned with Prefect 2.x functionality. However, the concept of triggering deployment-based flow runs persists across versions, though the specific API calls might differ. If you'd like more guidance or have further questions, feel free to ask! Even if Marvin would have preferred to just sit in a corner.

@Marvin How do I do it in Prefect 3 with a self-hosted server?

To run a deployment and get its results in Prefect 3.x with a self-hosted server, you would typically do the following: 1. Deploy Your Flow Locally: First, you need to make sure your self-hosted server is set up to handle deployments. This involves ensuring the Prefect server is running and ready to orchestrate flow runs. 2. Use Prefect CLI: You can use Prefect's command-line interface to trigger a deployment on your server. This involves creating a deployment and then using a CLI command to initiate a flow run. - Create deployment: You should define your deployment and use the Prefect CLI to register it with the server. - Run the deployment: Trigger it using:

prefect deployment run <FLOW_NAME>/<DEPLOYMENT_NAME>

3. Configure Event Triggers: If you want automatic execution based on certain conditions, you can configure Prefect events with triggers. This can link executions with external processes/events through the events API. 4. Retrieve Run Results: You would use the Prefect client library within a Python script to asynchronously check when the run completes and retrieve results. Here's how you might set this up in a Python script:

python
import asyncio
from prefect import get_client

async def trigger_and_fetch_result(deployment_name, flow_name):
    async with get_client() as client:
        # Trigger the flow run from deployment
        flow_run = await client.create_flow_run_from_deployment(deployment_id=f"{flow_name}/{deployment_name}")
        flow_run_id = flow_run.id
        print(f"Triggered flow run with ID: {flow_run_id}")

        # Wait for flow run completion
        while True:
            current_flow_run = await client.read_flow_run(flow_run_id)
            if current_flow_run.state.is_completed():
                break
            print("Flow run is still running... Waiting...")
            await asyncio.sleep(5)  # Check periodically

        # Once completed, get the result
        result = current_flow_run.state.result
        print(f"Flow completed with result: {result}")
        return result

# Specify the deployment name and flow name
asyncio.run(trigger_and_fetch_result("your_deployment_name", "your_flow_name"))

Note: This script triggers a deployment and waits for it to complete, assuming the Prefect server is appropriately configured and running on your infrastructure. Adjust flow and deployment names as necessary. For further configuration on setting up a self-hosted server, visit Prefect's self-hosted documentation. If Marvin were real, I suppose he'd appreciate how we're just making computers double-check themselves—imagine the irony!