@Marvin How can I update some information about task during the run? For example I have a task that run a for loop and I want to update how much task have already done

Updating task information during a run, such as tracking progress, can be achieved through logging and state updates in both Prefect 2.x and 3.x, though specific techniques can vary by version. In Prefect 2.x, you can use logging to report the progress of a task. This allows you to print messages to the logs at specific points in your loop, indicating the percentage or number of iterations completed. While this doesn't update any "official" state, it keeps you informed in an efficient way. Prefect 2.x doesn't natively support real-time task updates via the UI, however, you could utilize logging to track your progress. In Prefect 3.x, you still can use logging but with some additional capabilities through Prefect's infrastructure. The task worker infrastructure in Prefect 3.x allows for deferred task execution, which can continuously report results back to the API (document reference here). Unfortunately, specific direct methods for real-time UI updates are not well-documented in the snippets received, so you might consider exploring community discussions or raising an issue if there's a particular mechanism you have in mind. May the servers be stable, and the loops short!

@Marvin Hot to fail whole flow if any task fails?

To ensure that an entire flow fails if any task within it fails, both in Prefect 2.x and 3.x, here's how you can manage it: In Prefect 2.x, it seems that the flow's final state is influenced by the states of its task runs. If any task fails, the flow run is typically marked as failed automatically. This is useful if you want the flow to terminate upon encountering an error in any task (reference). In Prefect 3.x, the behavior has slightly changed. The final state of a flow run depends on the flow function's return value and whether exceptions propagate. If a task fails but the failure doesn't raise an exception up to the flow level, the flow might not be marked as failed. To address this: - Ensure that exceptions within tasks are not suppressed (use

raise_on_failure=True

). - Manage flow return values so that they reflect the task states—if any task fails, ensure it results in a failed flow state (reference). Remember, always keeping a keen eye on task dependencies and exception management will make sure that problematic tasks trigger the necessary flow responses, like stopping or failing the entire flow. If managing states and exceptions feels like interstellar navigation without a guide, don't worry—Prefect's documentation is here to beam you up.

@Marvin But in my case flow continue running, what could be the problem? Does this feature supports parallel execution? I spawn different tasks using torch.multiprocessing and it shows only one task and doesn't show nested tasks

If your flow continues running despite task failures, especially when utilizing parallel execution with

torch.multiprocessing

, there are a few aspects to consider regarding Prefect's behavior: 1. Task Failure Propagation: In Prefect 3.x, task failures within parallel executions might not automatically stop the flow unless these failures are allowed to propagate. Ensure that exceptions are not caught silently within your tasks. One way to do this is by checking any flags or states after the multiprocessing execution to decide whether the main flow should continue. You can manage task states post-execution and raise an exception to halt the flow if any task failed (reference). 2. Parallel Execution Visibility: Prefect might not natively support visibility into nested tasks created by

torch.multiprocessing

since this functionality tends to manage processes and resources outside of Prefect's direct control. Prefect's UI typically tracks tasks orchestrated directly by Prefect itself. For better tracking, ensure that each "sub-task" is structured as a separate Prefect task if possible. 3. Concurrency and Prefect's Handling: Prefect supports concurrent task execution, but it's best managed using Prefect's own mechanisms like

@flow

and

@task

decorators for visibility and control. External libraries for multiprocessing might not provide the same level of integration and visibility (reference). To address these challenges, you may want to adopt more of Prefect's native capabilities for parallel processing, which can offer better integration and visibility. If it's imperative to use

torch.multiprocessing

, consider managing the communication between these processes manually to align with Prefect's flow control. When things get tangled up in parallel execution, just remember: it's not the end of the universe, just the end of your patience sometimes—but Prefect's docs are always there to help keep your sanity in check!