@Marvin How to handle async functions and or tasks with prefect 3?

In Prefect 3.x, handling asynchronous tasks and functions is efficiently supported. Here’s how you can manage them: 1. Define Async Tasks: Prefect allows you to define asynchronous tasks using the

async

keyword. Here's a basic example:

python
   import asyncio
   from prefect import flow, task

   @task
   async def async_task(x, y):
       # Perform an asynchronous operation
       await asyncio.sleep(1)
       return x + y

You can then use these tasks within flows:

python
   @flow
   async def my_async_flow():
       results = await async_task(1, 2)
       print(results)

   if __name__ == "__main__":
       my_async_flow()

2. Concurrency Management: Prefect 3.x provides tools to handle concurrency, which can be particularly useful when dealing with multiple asynchronous tasks. You can limit the number of concurrent tasks to avoid overloading resources. Use the

concurrency

context manager to manage these limits. For instance:

python
   from prefect.concurrency.asyncio import concurrency

   @task
   async def limited_concurrent_task(x, y):
       async with concurrency("resource_name", occupy=1):
           return x + y

3. Asynchronous Client and Utilities: Prefect provides an asynchronous client for interacting with its API. This can be useful in deployment scripts or custom integrations. Keep in mind the distinction between synchronous and asynchronous methods. Prefect has naming conventions, such as using an

a

prefix for async methods (

Block.load

vs.

Block.aload

). 4. Handling Serialization: When using asynchronous operations, ensure that any context or data being serialized is done correctly. Prefect can run into issues if non-serializable objects are included, particularly in async contexts. If you encounter serialization errors, consider using alternative serializers like JSON or disable persistence for certain flows. For more details, you can refer to Prefect 3.x Global Concurrency Limits documentation and this GitHub issue on sync and async support. If you encounter specific issues or need further examples, I recommend checking Prefect's GitHub issues and blog posts for more insightful guides on deploying and running async tasks effectively.

@Marvin Based on your response: @flow async def my_async_flow(): results = await async_task(1, 2) print(results) if name == "__main__": my_async_flow() i tried your example and it throws the following error: /home/aprestamo/Documents/repos/poc_workflows/flows.py245 RuntimeWarning: coroutine 'run_flow_async' was never awaited test() RuntimeWarning: Enable tracemalloc to get the object allocation traceback

The error message you're seeing indicates that the async function

my_async_flow

is not being awaited properly. In Python, when you're working with async functions, you need to use

await

to execute them within an

async

context, or you can use

asyncio.run()

at the top level to run the async function. Here’s how you can fix the issue:

python
import asyncio
from prefect import flow, task

@task
async def async_task(x, y):
    await asyncio.sleep(1)
    return x + y

@flow
async def my_async_flow():
    results = await async_task(1, 2)
    print(results)

if __name__ == "__main__":
    asyncio.run(my_async_flow())

Explanation: - asyncio.run(my_async_flow()): This runs the asynchronous flow using Python’s

asyncio

event loop. It ensures that the coroutine is executed and awaited properly. By using

asyncio.run()

, the event loop is correctly initialized, allowing your async flow to execute without throwing a coroutine-not-awaited warning. Feel free to try this revised version, and let me know if you run into any other issues!