@Marvin I am experimenting scenarios where a kubernetes job randomly crashes even after completion and so the entire flow run would fail. This might be due to temporary infrastructure interrupts. What is the solution in this case?

It seems like your issue with Kubernetes jobs crashing randomly after completion can be approached in several ways: 1. State Change Hooks: Prefect's state change hooks can help manage infrastructure cleanup and job completion scenarios. You can create a hook that ensures cleanup even if the flow run crashes. For example, you might use these for deleting or cleaning up resources once the flow run enters a crashed state. Documentation: State Change Hooks 2. Retry Mechanism: Ensure that you have proper retry mechanisms in place for tasks that might fail due to temporary issues. Prefect allows you to configure retries for your tasks and flows, ensuring that transient issues do not cause complete flow failures. 3. Monitoring and Alerts: Make use of Prefect’s monitoring and alerting to catch issues early and take corrective actions. Integrating with services that notify you of failures can allow for faster responses. Additionally, some GitHub issues mention similar problems: - CannotPullContainerError: This issue could happen when Kubernetes jobs can't pull the required docker image. Verify the availability of the docker image and correct authentication tokens. Issue link: CannotPullContainerError - Ephemeral Infrastructure Caching: Prefect also has problems with task caching in ephemeral infrastructure like Kubernetes. Customize cache functions in environments where stored data isn’t persistent. Issue link: task_input_hash Issue 4. Custom Error Handling: Implement custom error handling logic in your flows to manage certain types of failures better and ensure they do not cascade into complete flow run failures. 5. Use Resource Quotas and Limits: Define appropriate resource quotas and limits for Kubernetes jobs to prevent over-utilization and ensure better job execution stability. References: - Documentation on State Change Hooks - Issue about CannotPullContainerError - Issue about task_input_hash failing in containerized flow runs

@Marvin I am trying to use set_flow_run_state. How can I get session of the flow run I am interested in?

To use

set_flow_run_state

in Prefect, you need to get the session of the flow run you are interested in. You can achieve this by using the

get_client()

function to interact with the Prefect API. Here’s a concise example: 1. Initialize the Client: Use

get_client()

to start a session. 2. Retrieve the Flow Run: Fetch the specific flow run you are interested in by its

flow_run_id

. 3. Set the Flow State: Use the

set_flow_run_state

function to set the desired state. Sample code:

python
from prefect.client import get_client
from prefect.orion.schemas.states import State

async def set_flow_run_state_example(flow_run_id, new_state: State):
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Set the new state for the flow run
        await client.set_flow_run_state(
            flow_run_id=flow_run.id,
            state=new_state
        )

# Example usage
import asyncio
from prefect.orion.schemas.states import Completed

flow_run_id = "your-flow-run-id"
new_state = Completed(message="This flow run has been manually completed.")
asyncio.run(set_flow_run_state_example(flow_run_id, new_state))

### Documentation References - Interactive Workflows: Guide - Using the Client: Guide This setup ensures that you can dynamically manage and modify flow run states as needed within your orchestration logic. If you have further questions or need more specialized assistance, the Prefect community and documentation are great resources.