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@Marvin i also have python 3.11 here. i dont have the problem if i am on python 3.9

It sounds like you're experiencing an issue with Prefect on Python 3.11 that does not occur with Python 3.9. I'll need more information to provide specific guidance, but let's get started by looking into known issues and documentation related to Prefect and Python 3.11 compatibility. I'll search the Prefect documentation and GitHub issues to see if there are any known compatibility issues or solutions for using Prefect with Python 3.11. It appears there are some issues and considerations for using Prefect with Python 3.11: 1. Prefect Version and Docker Tags: - Prefect Docker images are tagged with specific Python versions, for example,

prefect:2-python3.11

. It's advisable to use specific versions carefully, especially since images can update automatically to the latest Prefect version. - Reference: Docker and Prefect Versioning 2.

prefect-azure

Compatibility: - The

azureml-core

package used in

prefect-azure

isn't fully compatible with Python 3.11 and Python 3.12. There's ongoing work to add warnings and handle tests that use

azureml-core

under these Python versions. - GitHub Issue on Prefect-Azure 3. General Workflow Issues: - Some issues have been reported when using certain functionalities (e.g.,

ProcessPoolExecutor

) with Python 3.11 in Prefect workflows, particularly noted to hang on Linux but work on MacOS when default settings are used. - A workaround involves setting the start method to

spawn

. This can resolve some issues. - GitHub Issue on ProcessPoolExecutor Recommendations: 1. Review the specific Prefect version and Python version compatibility, especially when using Docker images. 2. Be aware of limitations with

prefect-azure

and avoid using incompatible components. 3. Adapt workflows by adjusting multiprocessing settings, such as changing the start method to

spawn

if you encounter issues. If you have any specific errors or more details about the issue, please share them for a targeted solution.

@Marvin any idea what package could have caused the issue?

To identify the specific package causing the issue, we should consider the following steps: 1. Review Error Messages: - If you have any error messages or tracebacks from Prefect or Python 3.11, they can provide clues about the specific package or functionality causing the issue. 2. Dependencies and Compatibility: - Check the dependencies of your Prefect setup and see if any are known to have compatibility issues with Python 3.11. Some common culprits may include

azureml-core

or other libraries with native extensions. 3. Isolation and Testing: - Create a minimal environment with just Prefect and its core dependencies. Incrementally add other packages until the issue surfaces. Here's how you can proceed: 1. Identify Known Issues: From the information gathered earlier: -

azureml-core

needs to be checked as it has known issues with Python 3.11. - Multiprocessing-related libraries or settings could also be problematic. 2. Minimal Environment Setup: Create a new virtual environment and install only Prefect:

python3.11 -m venv env
   source env/bin/activate
   pip install prefect

3. Incremental Addition of Packages: Add other packages one by one, and monitor for issues:

pip install your-package

4. Run Your Workflow: Test your workflow after each installation to see if the issue reappears. If you provide more specific error messages or details about the problematic workflow, we can further narrow down the potential cause. Without detailed error logs, it is generally packages with native extensions or those interacting with multiprocessing that are likely suspects.