@Marvin for loop causing memory leak in prefect, but not when ran outside of prefect. How do i prevent this?

It appears that memory leaks can occur in Prefect within loops due to tasks persisting results in memory, and even with aggressive garbage collection, this might not resolve the issue. Based on the GitHub issues, here are some steps you can consider to mitigate the memory leak when running your flow in a loop: 1. Set

persist_result=False

and

cache_result_in_memory=False

for your tasks. This prevents unnecessary persistence of data, which might lead to memory leaks. 2. Use Python's garbage collection (

gc.collect()

) after significant memory-consuming operations or at the end of the flow run to free up memory. 3. Be cautious with the use of

map

and

submit

in loops, especially with

ConcurrentTaskRunner

, as it can lead to hanging or memory issues. 4. If you're running into this issue with Prefect 2.x, consider structuring your flows to work with large dataframes efficiently without keeping them in memory longer than necessary - this might involve writing intermediate results to disk or using other forms of external storage. You may need to inspect your code and understand how data is managed within your tasks. Another aspect to consider is the use of external libraries that might have memory leaks. Here are some issues and documentation pages for your reference: - Memory leak in an infinite loop: GitHub Issue #10338 - Task input persisted leading to memory not being released: GitHub Issue #10952 - Hanging flow execution when submitting or mapping tasks: GitHub Issue #7934 If the standard approaches don't solve your issue, it might be necessary to follow the GitHub issues for any updates or workarounds provided by the Prefect community or to inquire further into Prefect's support channels.