@Marvin is it possible to force garbage collection in prefect to free up memory?

It doesn't appear that Prefect provides an explicit way to force garbage collection to free up memory via its APIs. However, there are ways to manage memory usage in Prefect using result caching and persistence features. Here are a few strategies based on the Prefect documentation: 1. Disable in-memory caching of results. By setting

cache_result_in_memory=False

on a task, you can ensure that the result of your task will be persisted and won't be kept in memory by default. Refer to the documentation on results. 2. Utilize result storage blocks to store task results in external locations, such as AWS S3 or local filesystem block, which can free up memory on the machine running the flow. More information. 3. Write and read data to disk directly in your flow, avoiding in-memory passing of big data between tasks. Prefect Guides for Big Data. 4. Cache task results to avoid rerunning tasks unnecessarily. Keep in mind that this technique requires that task results are persisted. Caching is discussed in the tasks concept page. Remember, managing external result storage and implementing caching strategies require additional setup and awareness of how Prefect handles task results. If you are specifically looking to perform garbage collection in Python, you can invoke

gc.collect()

from Python's built-in

gc

module to force garbage collection, but the effects and appropriateness of this approach will depend on the specifics of your Prefect workloads and how they are structured.