@Marvin, work_pool is online, but deployment says that it have not been pulled in the last 60 seconds

It looks like there are several possible reasons that might cause your deployment not to be pulled despite the work pool being online. 1. Configuration Issues: Ensure that your work pool and deployment are properly configured. You can follow this guide to push work pools to check the details. 2. Deployment Actions: Verify that the

pull

action is properly configured in your

prefect.yaml

file. This is essential for pulling your deployment in remote execution environments. Check more details on that here. 3. Viewing and Updating Work Pools: You can inspect and update your work pools using the Prefect CLI or UI. More details are available in the work pools concept documentation. Although no specific GitHub issues were found related to this, ensuring correct configurations should help resolve the issue. If the problem persists, you might want to check for any recent changes in the Prefect repository or open a new issue.

@Marvin Failed to submit flow run to infrastrucutre, Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/workers/base.py", line 908, in _submit_run_and_capture_errors result = await self.run( File "/usr/local/lib/python3.10/site-packages/prefect_kubernetes/worker.py", line 612, in run async with self._get_configured_kubernetes_client(configuration) as client: File "/usr/local/lib/python3.10/contextlib.py", line 199, in aenter return await anext(self.gen) File "/usr/local/lib/python3.10/site-packages/prefect_kubernetes/worker.py", line 743, in _get_configured_kubernetes_client await config.load_incluster_config() TypeError: object NoneType can't be used in 'await' expression

It appears that this issue has been reported before. Here are some potential steps to resolve it: 1. Check Kubernetes Configuration: Ensure that your Kubernetes configuration is correct and accessible. It looks like the

config.load_incluster_config()

method is encountering a

NoneType

object, which suggests that the Kubernetes client configuration might not be set up properly. 2. Library and Version: Confirm you are using the appropriate versions of

prefect-kubernetes

and other related packages. You might be experiencing compatibility issues with the Prefect version and your Python environment. 3. GitHub Issue Reference: A similar issue was reported in GitHub Issue #14858. Consider reviewing this issue for more context and potential updates or fixes provided by the community or maintainers. 4. Prefect Documentation: Refer to the work pools concept documentation for detailed steps on configuring and troubleshooting work pools and workers. If these steps don't resolve the issue, consider opening a new issue on the Prefect GitHub repository with details of your configuration and the error traceback for more targeted support.