@Marvin i'm receiving this error when trying to deploy a flow from my prefect.yaml file sys1 RuntimeWarning: coroutine 'Block.aload' was never awaited

The error you're encountering, "RuntimeWarning: coroutine 'Block.aload' was never awaited," suggests that you're trying to use an asynchronous function without awaiting it. In Prefect 3.x, many methods are asynchronous and need to be awaited to execute properly. To resolve this, you can do one of the following: 1. Await the Coroutine: Ensure that you are using the

await

keyword before calling the asynchronous function if you are within an async context. 2. Use

_sync=True

: If you want to call the function synchronously, you can pass the

_sync=True

parameter to the method. Here's a brief example of how you might adjust your code:

python
from prefect import flow
from prefect.blocks.core import Block

@flow
async def my_flow():
    block = await Block.aload("my_block")

# Or, if you prefer a synchronous call
block = Block.load("my_block", _sync=True)

For more details, you can refer to the Prefect 3.x upgrade guide which explains these changes in the context of Prefect 3.x.

but this error occurs when deploying through the yaml file using the command: prefect deploy

The error you're facing when deploying a flow using the

prefect.yaml

file and the

prefect deploy

command is likely due to asynchronous block references not being awaited. This issue arises because asynchronous methods need to be awaited to execute properly, and it seems that the deployment process is not handling this correctly in your setup. Here's what you can do to address this: 1. Review your

prefect.yaml

file: Ensure that any block references or methods that are asynchronous are correctly handled. If they are being called directly, consider using the

_sync=True

parameter to handle them synchronously. 2. Deployment Mechanics: When you run

prefect deploy

, the

prefect.yaml

file is loaded, and all variable and block references are resolved. If there's an asynchronous block that isn't awaited, it might cause the warning you're seeing. Ensure that all blocks are properly configured to be synchronous if needed. 3. Check Prefect Version: Make sure you're using a version of Prefect that properly supports the configurations you're using. Sometimes, these issues can be version-specific. For more details on how the deployment process works and how blocks are handled, you can refer to the Prefect YAML documentation. If the issue persists, you might need to check the specific blocks or methods you're using in the YAML file and ensure they're compatible with the deployment process.

@Marvin give me an example of tha deploy that uses the _sync=True parameter on a prefect.yaml file

It seems that there isn't a direct example in the documentation of using the

_sync=True

parameter within a

prefect.yaml

file. However, you can apply the concept by ensuring that any block or method calls that are asynchronous are handled synchronously in your flow code that is referenced by the

prefect.yaml

. Here's a conceptual example of how you might structure your flow and deployment: 1. Flow Code (Python File): Ensure that any asynchronous operations are handled synchronously if necessary.

python
   from prefect import flow
   from prefect.blocks.core import Block

   @flow
   def my_flow():
       block = Block.load("my_block", _sync=True)
       # Rest of your flow logic

2. prefect.yaml: Reference the above flow in your deployment configuration.

yaml
   name: my-deployment
   description: A deployment for my_flow
   version: 1.0
   flow: path/to/your_flow_file.py:my_flow
   build: 
     - prefect build
   push:
     - prefect push
   infra_overrides:
     # Any infrastructure-specific overrides

In this setup, ensure that your flow file (e.g.,

your_flow_file.py

) is correctly referenced in the

prefect.yaml

. The

_sync=True

parameter is used directly in the flow code to ensure synchronous execution. If you need further customization or encounter issues, reviewing the Prefect YAML documentation might provide additional insights.