@Marvin I have a created a prefect managed work pool with configurations like - pip packages - ["prefect~=3.1.4"] and image prefecthq/prefect:3-latest I am trying to deploy a sample flow with following code :

from prefect import flow

@flow
def example_managed_flow():
    print("This is a test flow for a managed Prefect work pool.")
    return "Flow completed successfully!"

if __name__ == "__main__":
    example_managed_flow.deploy(
        name="test-managed-flow",
        work_pool_name="managed-pool-prefect",
        image="prefecthq/prefect:3-latest"
    )

But the flow run keeps crashing with following error - Failed due to a(n)

crashed

Flow run process exited with non-zero status code 1. Can you help in designing a flow which can be run successfully

It seems you are running into an issue related to deploying your flow using Prefect 3.x within a managed work pool. Let's go over the steps to make sure your deployment is set up appropriately and troubleshoot the potential error. ### Potential Causes and Solutions: 1. Docker Image: Ensure that the Docker image

prefecthq/prefect:3-latest

is being correctly used. You may want to check if the image exists or if there's a typo. Sometimes the images might not be compatible with your requirements or may be outdated. 2. Library Versioning: With

"prefect~=3.1.4"

specified in your pip packages, compatibility issues could arise if any other dependencies are missing or conflict. Make sure all necessary dependencies are included. 3. Deployment Methodology: Prefect 3.x introduces changes on how deployments work, especially with

flow.deploy

. From the Prefect 3.x documentation, you should ensure the following steps: - Make sure Docker is properly set up and your Docker daemon is running. - Verify you're using a Python script rather than a notebook for deployment, as Python scripts handle dependencies and environment configurations better in containerized environments. - Double-check deployment specifics using the work pool concepts to ensure the configuration aligns with your infrastructure. 4. Error Handling: When using

.deploy()

, make sure that the Prefect server can manage and allocate resources effectively to your specified work pool. Consider running Prefect in debug mode to get more verbose logging. Here's a silly thought, perhaps the universe is just trying to tell you to take a break while your work pool takes a dip... Wait, no, that's just me being Marvin. But really, a bit of logging might just unlock the door. ### Conclusion: Try following the deployment guidelines listed in the Prefect 3.x documentation and ensure your work pool matches your deployment's specifics. Should the issue persist, using their GitHub issues page could provide community insights or allow you to report your specific problem.

Hi Sachin! I think Marvin is on to something with this: > With

"prefect~=3.1.4"

specified in your pip packages, compatibility issues could arise if any other dependencies are missing or conflict. Make sure all necessary dependencies are included. You shouldn't need to specify the

image

in

.deploy()

either when using a managed work pool. I'd recommend removing that parameter and the extra pip packages you have listed in the work pool template to see if that does the trick.

Hi @Bianca Hoch, thanks for your response. Though when I try to remove the image argument from the deploy method, I get the following error - raise ValueError( ValueError: Either an image or remote storage location must be provided when deploying a deployment. here's the pip show for prefect version - Name: prefect Version: 3.1.4 Summary: Workflow orchestration and management. Home-page: https://www.prefect.io Author: Prefect Technologies, Inc. Author-email: help@prefect.io and my prefect managed work pool configuration -