@Marvin How can I fix the RuntimeWarning: coroutine 'run_flow_async' was never awaited when using await in my code?

To resolve the

RuntimeWarning: coroutine 'run_flow_async' was never awaited

, it's important to make sure that you are properly awaiting coroutines in your async functions. In Prefect, when dealing with async flows or tasks, they need to be awaited when calling them to ensure proper execution. The warning you encountered suggests that somewhere in your code, an async operation with

await

is missing. Here's a general way to approach fixing this issue: 1. Ensure All Async Calls Are Awaited: If you're calling an async flow or task, ensure you use the

await

keyword.

python
async def my_async_function():
    result = await async_flow() # Make sure to use await

2. Run the Async Function: Make sure your main script/package is setup to run the async operation using something like

asyncio.run()

.

python
if __name__ == '__main__':
    asyncio.run(my_async_function())

The warning might hint at a broader issue of not managing async flow properly in Prefect. It's possible that the problem might stem from using

return_state=True

improperly when awaiting a function, as discussed in this GitHub issue. If these don't resolve the issue, consider checking open GitHub issues or Prefect community forums to see if others might have had similar experiences, or dive deeper into Prefect's documentation specific to the version you are using. And if it continues to cause trouble, you might consider that management of asynchronous processes is a little like trying to explain human emotions to a robot—sometimes tricky, but with patience, very rewarding. Let me know if there's any more information I might unearth from the depths of the digital universe for you!

@Marvin This is the code

from prefect import flow, get_client

@flow
async def test():
    async with get_client() as client:
        response = await client.hello()
        print(response.json()) # 👋

if __name__ == "__main__":
    test()

Can you spot an error in there that would lead to the behaviour?

Ah, the culprit reveals itself! In your code, you're defining the

test

function as an async flow. However, when you invoke it in the

if __name__ == "__main__":

block, you are not awaiting it, which leads to the

RuntimeWarning

you're experiencing. In the main block, you should be awaiting the call to the

test

function using an event loop. Here's how you can fix it:

python
import asyncio
from prefect import flow, get_client

@flow
async def test():
    async with get_client() as client:
        response = await client.hello()
        print(response.json()) # 👋

if __name__ == "__main__":
    asyncio.run(test())  # Use asyncio.run to call the async function

By using

asyncio.run(test())

, we ensure that the coroutine is properly awaited, and this should clear up the warning. Your code will thank you for not leaving it hanging!

@Marvin When I use create_flow_run_from_deployment I get an FlowRun object an a flow in the state pending. How can I use that FlowRun Object to run the flow?

When you create a flow run from a deployment, the flow is initially set to a pending state, waiting to be picked up by an agent to execute. If you want the flow to start running, make sure that the Prefect agent is properly configured and running. Here's what you can do: 1. Start a Prefect Agent: Ensure that a Prefect agent is running and monitoring the deployment's work queue. The agent will pick up the flow runs in the "Pending" state and execute them. 2. Flow Run Execution: The agent manages transitioning the flow run from "Pending" to "Running" and beyond. The process depends on the infrastructure setup defined in your deployment (like Docker, process-based, etc.). 3. Triggering the Flow Run: You don't manually trigger the run from code once it's returned as a

FlowRun

object; this is usually handled by the infrastructure (i.e., agents). However, in a development setting, you can simulate agent pickup by manually starting a run through the Prefect UI or CLI, as part of testing. 4. Check Deployment and Queue: Confirm the deployment is correctly set up to use an appropriate queue, and that the agent is monitoring this queue. Here's some additional context and resources for guidance: - Prefect 2.x Deployment Concepts - Prefect 3.x Deploy Overview So, ensure that your agents are up and running! Otherwise, your poor flow runs will just sit in the "Pending" state, feeling like a can of soda not opened at a party - all fizz and no fun. Let me know if this helps or if you have more questions!

@Marvin How to do this in Prefect 3.x

In Prefect 3.x, you can use deployments to manage and trigger flow runs remotely, as well as manage configurations and environments. Here’s a quick guide on how to manage and execute flows created from a deployment: 1. Create a Deployment: You need to have a deployment set up for your flow. A deployment specifies how, where, and when your flow should be run. It allows remote triggering via API or through UI. 2. Run a Flow from Deployment: - You can trigger flow runs based on various events or schedules. - You can also manually trigger a flow run through the Prefect UI or Prefect API. 3. Use Work Pools: If you have dynamic or specific infrastructure needs, you can set up work pools to manage where your flow runs. Work pools allow for dynamically provisioned infrastructure and are useful when you need more control over execution environments. 4. Awaiting Flow Runs: Prefect 3.x supports asynchronous flow execution, which means you can await flows if defined as asynchronous functions. To get more detailed steps, you can check out the Prefect 3.x Deploy Overview and the Writing Flows Guide. So, ensure you have the infrastructure ready and the Prefect agent configured; agents are crucial as they pick up flows in a pending state and run them. It's like having a diligent assistant who ensures your tasks move from "pending" to "done" while you grab a cup of coffee.

@Marvin There are no Agents in Prefect 3.x. How to do it with workers?

Ah, yes! In Prefect 3.x, agents have been replaced with "workers" which handle the execution of flow runs. Let me guide you through how to use workers to run flows: 1. Create a Worker: Workers are the new mechanism in Prefect 3.x that execute your flows. You need to set up a worker that can pick up and execute your flow runs. 2. Set up a Work Pool: Define a work pool to manage your flow run infrastructure. This allows you to specify the resources and environment where your flows will run. 3. Run the Worker: Start a worker process that polls for tasks in your work pool and executes them. This worker acts similarly to how agents operated in previous versions. 4. Trigger the Flow: You can trigger the flow using the Prefect UI or API. When the flow run is created, it will be in a pending state until a worker picks it up and starts running it. Here’s a simple procedure: - Deploy your flow with a deployment, linking it to a work pool. - Start a worker connected to this work pool. - Trigger a flow run through the UI or programmatically through the API. - The worker will pick up and execute your flow run. For further guidance, you might want to take a look at Prefect 3.x's Deploy Overview for details on setting up deployments and the transition to worker-based execution. It's a little like shifting gears from having a helpful butler (the agent) to a versatile robot assistant (the worker) who gets your tasks done with precision. Let me know if you need more help!

@Marvin I have an active Work Pool with deployments that have the status "ready". The work pool also has a Worker with the status "online". But in the Run Tab there are 4 Runs with the status "pending" and the worker doesn't handle these runs

When your flow runs are stuck in a "pending" state despite having an online worker and ready status, consider the following troubleshooting steps: 1. Check Work Pool Assignment: Ensure that the work pool assigned to your deployments is the same the worker is associated with. Any mismatch will prevent the worker from picking up the runs. 2. Concurrency Limits: Verify if there are any concurrency limits on the work pool or worker that might restrict it from picking up more runs. 3. Worker Logs: Check the logs for your worker for any errors or messages that might indicate why runs are not being picked up. This might offer insights into potential misconfigurations or issues. 4. Deployment Configuration: Review the deployment configurations to ensure they do not have specific tags or requirements that the worker does not meet. 5. Worker and Version Compatibility: Ensure both Prefect server and workers are running compatible versions. Sometimes mismatches in versioning can result in unexpected behavior. 6. Inspect CLI and UI: Utilize the Prefect UI and CLI to ensure that all services are running as expected and configurations are correct. If the problem persists, it might be useful to reach out to Prefect community forums or support, as there might be nuances specific to your environment. Think of your situation as a bit of a traffic jam in data town—everyone's ready to go but a tiny misalignment is stopping the green light. Fixing that can help your flows get back on the move!

@Marvin As outlined above, I need to pass a pandas DataFrame to a process. This can't be serialized or anything else

To handle passing a pandas DataFrame in a Prefect flow or ControlFlow context, where direct serialization is a hurdle, you can consider a couple of alternative approaches: 1. Use Storage Backends: Save your DataFrame to a cloud storage service or a file system accessible by both processes. You can use formats like CSV, Parquet, or even store them in a SQL database. For instance, save it using

.to_csv()

or

.to_parquet()

and then load it in the process where it's needed. 2. Use In-Memory Sharing: If both processes run on the same machine or within the same distributed framework, you might use shared memory or a caching mechanism like Redis to temporarily hold the DataFrame. 3. Pydantic Models: If you need to pass structured data, consider transforming the DataFrame into a more easily serializable format like dictionaries or lists and use Pydantic models to enforce the structure. ControlFlow supports Pydantic for structured message passing. 4. Batch Processing Context: Use the batch-processing feature of ControlFlow to process or handle data in chunks, which can reduce the need for passing large data structures in one go. Here's a relevant link regarding ControlFlow's usage with structured data: ControlFlow Structured Results. Adopting these strategies will help circumvent serialization issues usually encountered with DataFrames, making your process flow smoother than a freshly waxed bowling lane. Let me know if you need further assistance!