@Marvin is there a way to explicitly clear memory in prefect for large-running flows?

It seems that the search didn't return directly relevant information regarding explicitly clearing memory in Prefect for large-running flows. However, there are different strategies in Prefect for managing flow and task run data, which may indirectly help to manage memory by controlling what is persisted or retained. Prefect doesn’t provide a built-in feature explicitly labeled for clearing memory during a flow run. Memory management is usually handled by the Python interpreter and the underlying infrastructure where your flow is running. However, you can use several strategies to manage memory usage within your flows: 1. Result Handlers: By using custom result handlers, you can control how and where task results are stored, which can prevent large data structures from staying in memory if they’re written to disk or an external service instead. 2. Flow Run Retention Policy: In Prefect Cloud, flow and task run data is retained according to a retention policy tied to your account. This policy determines how long run data remains available in the Prefect UI and via the Prefect CLI and API after the flow run ends. 3. Efficient Coding Practices: Write your tasks in a way that they don’t retain unnecessary data in memory. For instance, using generators instead of lists for large data sets can reduce memory usage. 4. Control Flow Techniques: Use control flow techniques such as mapping and checkpointing to break down large tasks into smaller, more manageable pieces and reduce the memory footprint of each task execution. For additional specific information, you may consider looking into the Prefect Results and Result Handlers documentation for advice on how to optimize memory usage by controlling how task results are handled. If you require further assistance or more specialized strategies, you could consider reaching out to the community via Prefect Slack channels or GitHub issues for advice from other users who might have tackled similar challenges.

@Marvin specifically, i have a document conversion task that takes a PDF and converts it to images. this works on smaller PDFs but on large ones it fills up memory quite rapidly. i am wondering if it is possible to force prefect to purge memory after completion of certain tasks

The search results do not seem to directly address the issue of forcing memory cleanup after task completion. However, we can infer some best practices based on Python's garbage collection and Prefect's task execution model that may help manage memory in tasks that process large files: 1. Explicit Memory Management in Python: Make sure to explicitly delete large objects and call the garbage collector. After processing the PDF and it's no longer needed, you can delete the objects and prompt Python's garbage collector manually:

python
   import gc

   @task
   def process_pdf(pdf_file):
       images = convert_pdf_to_images(pdf_file)
       # Do something with the images
       del images  # Explicitly delete the object
       gc.collect()  # Force garbage collection

2. Managing Task Results: You can manage task results by ensuring they are not unnecessarily stored or cached. By default, task results are cached in memory during a flow run and persisted according to the

PREFECT_LOCAL_STORAGE_PATH

setting. Ensure your task does not cache large data if not needed. 3. Use of Temporary Files: Use temporary files or file-like objects (such as

io.BytesIO

for binary data) for intermediates, which can be cleaned up automatically. 4. Modular Flow Design: If possible, break the flow into separate parts, where each part processes some chunks of the PDF and cleans up before proceeding to the next part. This could involve separate flows for each stage of the PDF processing or multiple tasks that each handle a part of the process. 5. Task Concurrency and Isolation: Use task concurrency features to isolate memory-intensive tasks, running them in separate processes or even separate machines, to prevent one memory-intensive task from affecting others. As of now, there doesn’t seem to be a built-in flag or function in Prefect to trigger an explicit memory purge after a task completes that would be more aggressive than what Python’s garbage collector does on its own. If memory issues persist, consider discussing this with the Prefect community or opening an issue in the Prefect GitHub repository for more specialized support.

@Marvin what does persist_results do

In Prefect, the

persist_result

parameter controls whether or not the result of a task or flow is stored in a designated storage location after execution. This can be useful for various reasons, including enabling retries, caching, and ensuring data availability after the flow run completes. Here's a concise explanation of the key points about `persist_result`: - Memory Management: By default, Prefect keeps all task results in memory during the flow run. This can be costly for large results, and using

persist_result

can help mitigate memory usage by offloading results to storage. - Default Behavior: Prefect does not persist results unless necessary for specific features (like retries or caching) or if explicitly asked to. You can override this behavior using the

persist_result

parameter. - Configuration Examples: - Flow Level:

python
    from prefect import flow, task

    @flow(persist_result=True)
    def my_flow():
        # Results of this flow will be persisted
        ...

- Task Level:

python
    from prefect import flow, task

    @task(persist_result=True)
    def my_task():
        # Results of this task will be persisted
        return some_large_result

    @task(persist_result=False)
    def another_task():
        # Results of this task will not be persisted
        return another_result

- Configure Default Behavior: You can also change Prefect's default persistence behavior globally by setting the configuration parameter `PREFECT_RESULTS_PERSIST_BY_DEFAULT`:

bash
  prefect config set PREFECT_RESULTS_PERSIST_BY_DEFAULT=true

- Storage Configuration: If you enable

persist_result

, Prefect will use a default storage location and serializer unless you specify otherwise. This helps in managing large data that should not reside entirely in memory. By strategically using

persist_result

, you can manage memory usage in your flow runs more effectively, allowing for smoother execution of memory-intensive tasks. For more detailed information, consult the Prefect Results Documentation.

@Marvin so is persist_results on by default?

No,

persist_result

is not on by default in Prefect. By default, Prefect does not persist task and flow results unless it is necessary for specific features such as retries or caching, or if explicitly configured to do so. Here is how you can explicitly configure it: - Enable Persisting for a Specific Task or Flow:

python
  from prefect import flow, task

  @flow(persist_result=True)
  def my_flow():
      # The result of this flow will be persisted
      return data

  @task(persist_result=True)
  def my_task():
      # The result of this task will be persisted
      return some_large_result

- Disable Persisting for a Specific Task or Flow:

python
  @task(persist_result=False)
  def another_task():
      # The result of this task will not be persisted
      return another_result

- Set Global Default for Persisting Results: If you want to change the global default behavior to always persist results unless otherwise specified, you can do so with the following configuration:

bash
  prefect config set PREFECT_RESULTS_PERSIST_BY_DEFAULT=true

This configuration ensures that results will be persisted unless explicitly set otherwise at the task or flow level. For more comprehensive information, refer to the Prefect Results Documentation.