@Marvin after I deleted a deployment, I still see the agent logging

Deployment not found

. How can I make the agent stopping that and completely delete the deployment?

It seems that the issue you're experiencing with the agent logging "Deployment not found" after deleting a deployment is a known bug. This occurs because deleting a deployment does not always remove all the scheduled runs associated with it. As a result, the system continues to attempt to execute these runs, leading to the error message. To address this, you can manually cancel any remaining scheduled runs associated with the deleted deployment. This should stop the agent from attempting to execute them and eliminate the error message. For more details, you might want to refer to the related GitHub issues: - Deleting a deployment does not delete corresponding scheduled runs - Flow runs stuck in AwaitingRetry if corresponding deployment is deleted If the problem persists, consider reporting it to the Prefect team or checking for any updates that might address this bug.

@Marvin how do I get task state from PrefectConcurrentFuture object

To get the task state from a

PrefectConcurrentFuture

object, you can use the

result()

method. This method will return the result of the task, and if the task has completed, it will also provide you with its final state. Here's a basic example:

python
from prefect.futures import PrefectConcurrentFuture

# Assuming `future` is your PrefectConcurrentFuture object
try:
    result = future.result(timeout=10)  # Set a timeout as needed
    # Access the state if needed
    state = future._final_state
except Exception as e:
    print(f"An error occurred: {e}")

Make sure to handle exceptions that may arise, such as timeouts or failures during task execution. For more details, you can refer to the Prefect source code on GitHub.

@Marvin what re the causes for the error “Execution was cancelled by the runtime environment”?

The error "Execution was cancelled by the runtime environment" in Prefect can occur due to several reasons related to the infrastructure and cancellation process: 1. Unsupported Infrastructure: If the infrastructure for the flow run does not support cancellation, the execution cannot be stopped as expected. 2. Identifier Scope Mismatch: If the identifier scope does not match when attempting to cancel a flow run, the worker cannot cancel it. This might lead to another worker attempting the cancellation. 3. Infrastructure Not Found: If the infrastructure associated with the run cannot be found or has already been terminated, the worker marks the flow run as cancelled. 4. Missing Infrastructure PID: If the infrastructure process ID (PID) is missing, the flow run is marked as cancelled, but the cancellation cannot be enforced. 5. Unexpected Errors: If the worker encounters an unexpected error during the cancellation process, the flow run may or may not be cancelled depending on where the error occurred. The worker might retry the cancellation, or another worker might attempt it. For more details, you can refer to the Prefect documentation on cancellation.

@Marvin why a flow state is completed when a task failed?

In Prefect, a flow can be marked as "Completed" even if a task within it fails, depending on how the flow's success criteria are defined. Here's how it generally works: 1. Terminal States: Prefect defines terminal states for both tasks and flows, such as

COMPLETED

,

FAILED

, and

CRASHED

. A flow is considered

COMPLETED

if it reaches the end of its execution without unhandled errors or exceptions. 2. Task Return Values: A task will be placed in a

Completed

state if it returns any Python object, unless it explicitly returns a

Failed

state. If a task fails but is handled within the flow (e.g., by catching exceptions or using retries), the flow itself can still end in a

Completed

state. 3. Flow Logic: The flow's logic can be designed to handle task failures gracefully. For example, if a task failure is anticipated and managed (like through conditional logic or retries), the flow can complete successfully. 4. State Handling: You can directly access and manipulate state objects within a flow using the

return_state

flag to conditionally respond to task or flow states. For more details on managing states, you can refer to the Prefect documentation on managing states.

@Marvin how can I make a flow run failing when one or more task runs fail?

To ensure a flow run fails when one or more task runs fail in Prefect, you can explicitly check the state of each task and raise an exception if any task fails. Here’s a strategy to achieve that: 1. Check Task States: Use the

return_state

flag to get the state of each task. If any task returns a failed state, you can manually raise an exception in the flow. 2. Custom Logic in Flow: Implement custom logic within your flow to monitor task states and determine the overall flow state based on task outcomes. Here's a basic example:

python
from prefect import flow, task
from prefect.states import Failed

@task
def my_task(x):
    if x < 0:
        raise ValueError("Negative value!")
    return x

@flow
def my_flow():
    task1 = my_task(-1)
    task2 = my_task(2)

    # Collect task states
    task_states = [task1, task2]

    # Check for any failed tasks
    for state in task_states:
        if isinstance(state, Failed):
            raise RuntimeError("Flow failed because a task failed.")

if __name__ == "__main__":
    my_flow()

This approach ensures the flow will raise an error and be marked as failed if any task within it fails. Adjust the logic to fit the specific needs of your flow.

@Marvin in Prefect 3.2.2 where are the artifacts saved?

In Prefect 3.2.2, artifacts are not stored as files in a specific location on disk. Instead, they are managed and accessed through the Prefect UI and API. Here's how it works: 1. Viewing Artifacts: Artifacts can be viewed in the Prefect UI under the artifacts tab of the associated flow run or task run. This allows you to see the latest versions and the history of each artifact. 2. Programmatic Access: You can manage artifacts programmatically using the Prefect REST API. This includes creating, reading, and deleting artifacts as part of your workflow. 3. CLI Access: Artifacts can also be accessed and managed using the CLI, where you can view all artifacts or filter them by specific keys. For more detailed information, you can refer to the Prefect documentation on artifacts.