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<@ULVA73B9P> I get errors: ```python -c 'from util...
# ask-marvin
m
@Marvin I get errors:
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python -c 'from utility import get_aws_secret; import asyncio; res = asyncio.run(get_aws_secret("/ikeloa/databricks.desa-lab.token"))
  File "/Users/merlinr/Library/Caches/pypoetry/virtualenvs/templisher-4hu7XBP5-py3.11/lib/python3.11/site-packages/prefect/cache_policies.py", line 297, in compute_key
    return hash_objects(hashed_inputs, raise_on_failure=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/merlinr/Library/Caches/pypoetry/virtualenvs/templisher-4hu7XBP5-py3.11/lib/python3.11/site-packages/prefect/utilities/hashing.py", line 89, in hash_objects
    raise HashError(msg)
prefect.exceptions.HashError: Unable to create hash - objects could not be serialized.
  JSON error: Unable to serialize unknown type: <class 'coroutine'>
  Pickle error: cannot pickle 'coroutine' object
m
Your message was too long by 58 tokens...
The error you're encountering is related to Prefect's hashing mechanism, which is attempting to serialize a coroutine object. This typically occurs when Prefect tries to compute a cache key for a function that involves asynchronous operations. Here's a breakdown of the issue and potential solutions: 1. Understanding the Error: - Prefect uses hashing to manage caching and state. It attempts to serialize inputs to create a unique key. - Coroutines, which are a key part of Python's asynchronous programming, cannot be directly serialized using JSON or Pickle, hence the error. 2. Potential Solutions: - Avoid Direct Coroutine Use: Ensure that any function you pass to Prefect does not directly return a coroutine. Instead, use 
asyncio.run()
or 
await
the coroutine within an 
async
function. - Modify the Function: If 
get_aws_secret
is an asynchronous function, ensure that you're calling it within an 
async
context and using 
await
. 3. Example Adjustment: - If 
get_aws_secret
is an async function, wrap it in an async function and call it using `await`:
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python
     import asyncio
     from utility import get_aws_secret

     async def main():
         res = await get_aws_secret("/ikeloa/databricks.desa-lab.token")
         return res

     asyncio.run(main())
4. Prefect Task Decorator: If you're using Prefect's task decorator, ensure it's compatible with async functions: - Use 
@task
decorator from Prefect for async functions:
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python
     from prefect import task

     @task
     async def get_secret_task():
         return await get_aws_secret("/ikeloa/databricks.desa-lab.token")
5. Check Documentation: Always refer to the latest Prefect documentation for handling async functions and tasks. If you continue to have issues, please provide more details about how you're integrating this function with Prefect, and I can help further.
m
@Marvin I am calling with asyncio.run:
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python -c 'from utility import get_aws_secret; import asyncio; res = asyncio.run(get_aws_secret("/ikeloa/databricks.desa-lab.token"))
Here is my function definition
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from prefect_aws import AwsCredentials
from prefect_aws.secrets_manager import read_secret
@flow
async def get_aws_secret(secret_name, return_string=False):
    secret_string = await read_secret(
        secret_name=secret_name
        , aws_credentials=AwsCredentials.load(PREFECT_AWS_SERVICE_USER)
        )
    return secret_string
m
The error you're encountering is due to the use of 
asyncio.run
with an asynchronous Prefect flow. Here's what you need to know and how you can resolve the issue: ### Key Insights: 1. Asynchronous Flows: Prefect supports asynchronous execution. Flows that are defined as asynchronous functions can be awaited or run concurrently. However, using 
asyncio.run()
within an async context or on an async function can cause issues. 2. Prefect 3.x Changes: Prefect 3.x has several changes, including the removal of some features and the introduction of new ones. For instance, 
.map
and 
.submit
methods are always synchronous, and agents have been replaced by workers. ### Solution: To resolve the error, you should handle the asynchronous flow correctly without using 
asyncio.run
directly on it. Instead, use Prefect's own mechanisms to manage async flows. Here's how you can adjust your code:
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python
from prefect import flow
from prefect_aws import AwsCredentials
from prefect_aws.secrets_manager import read_secret

@flow
async def get_aws_secret(secret_name, return_string=False):
    secret_string = await read_secret(
        secret_name=secret_name,
        aws_credentials=AwsCredentials.load("PREFECT_AWS_SERVICE_USER")
    )
    return secret_string

# To run the flow
if __name__ == "__main__":
    import asyncio
    asyncio.run(get_aws_secret("/ikeloa/databricks.desa-lab.token"))
### Additional Resources: - Prefect Asynchronous Execution: Prefect 3.x Asynchronous Execution - Prefect Secrets Management: Accessing Third-Party Secrets By following these guidelines, you should be able to manage the asynchronous flow execution without encountering serialization issues. If you need further assistance, feel free to ask!
m
@Marvin you are literally telling me to no use asyncio.run, and then you tell me to use asyncio.run.
m
You're right, that was contradictory. Let's clarify the correct approach for handling asynchronous Prefect flows without causing serialization issues: ### Correct Approach: 1. Direct Flow Execution: When dealing with Prefect flows, especially async ones, you should avoid wrapping them in 
asyncio.run()
if you're already using Prefect's flow management. 2. Using Prefect's Flow Management: - Prefect handles async flows internally, so you can invoke an async flow directly, and Prefect will manage the async context for you. Here's how you should structure your code:
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python
from prefect import flow
from prefect_aws import AwsCredentials
from prefect_aws.secrets_manager import read_secret

@flow
async def get_aws_secret(secret_name, return_string=False):
    secret_string = await read_secret(
        secret_name=secret_name,
        aws_credentials=AwsCredentials.load("PREFECT_AWS_SERVICE_USER")
    )
    return secret_string

# To run the flow
if __name__ == "__main__":
    # Directly call the async flow
    result = get_aws_secret("/ikeloa/databricks.desa-lab.token")
    print(result)
### Key Points: - Avoid 
asyncio.run
: Directly calling the flow as shown above will let Prefect manage the execution context. - Prefect Handles Async: Prefect's flow and task decorators are designed to handle async functions, so you don't need to manually manage the event loop. I hope this resolves the confusion. Let me know if you have any further questions!
m
@Marvin The code above returns:
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<coroutine object run_flow_async at 0x1049af780>
sys:1: RuntimeWarning: coroutine 'run_flow_async' was never awaited
m
The error you're encountering indicates that the coroutine is not being awaited properly. In Prefect 3.x, when you define an asynchronous flow, you need to await it to get the result. Here's how you can properly execute an asynchronous flow and await its result: ### Correct Execution of Async Flows
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python
from prefect import flow
from prefect_aws import AwsCredentials
from prefect_aws.secrets_manager import read_secret
import asyncio

@flow
async def get_aws_secret(secret_name, return_string=False):
    secret_string = await read_secret(
        secret_name=secret_name,
        aws_credentials=AwsCredentials.load("PREFECT_AWS_SERVICE_USER")
    )
    return secret_string

# To run the flow
if __name__ == "__main__":
    async def main():
        result = await get_aws_secret("/ikeloa/databricks.desa-lab.token")
        print(result)

    # Run the main coroutine
    asyncio.run(main())
### Key Points: - Await the Flow: Ensure that the flow is awaited within an async function. - Use 
asyncio.run
on Main Coroutine: Wrap the flow call inside a main coroutine and use 
asyncio.run()
to execute it. This setup should resolve the coroutine warning and properly execute your async Prefect flow. Let me know if you encounter any more issues!
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