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Hi! Memory issues with Prefect: I have a parent fl...
# ask-community
a
Hi! Memory issues with Prefect: I have a parent flow that runs a couple of children flows in sequence, and the more flows that have run the more total memory is allocated. I would except the total memory allocated to be independent of the number of flows. See example code. Running without flow/task decorators does not have this issue. Is there some setting I am missing that would correctly free the memory for each subflow, once completed?
Untitled.py
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j
Hey @Andreas Nord, just chiming in, but I have no idea.. I wonder if we would need to run literally gc.collect in order for the df to be erased, or del df.. Seems excessive though.. Hoping someone can provide insight, as I am very curious 🙏
a
Yes for sure it should not be necessary to do that
a
Curious to see if it’s fixed by switching to a sequential task runner? https://docs.prefect.io/latest/concepts/task-runners/
a
Like this?
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@flow(task_runner=task_runners.SequentialTaskRunner)
def child_flow():
    df = big_df.submit()


@flow(task_runner=task_runners.SequentialTaskRunner)
def parent():
    for i in range(1):
        child_flow()
    print("lots of memory allocated here")

parent()
There is no difference
j
@Andreas Nord does this answer it -> https://docs.prefect.io/2.11.4/concepts/results/?h=memor#caching-of-results-in-memory
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@flow(cache_result_in_memory=False)
def foo():
    return "pretend this is large data"
and for tasks there's 2 (might be for flow too don't have a testset up though)
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@task(persist_result=False, cache_result_in_memory=False)
def bar():
    return "pretend this is biiiig data"
^Should link to section named
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Caching of results in memory¶
a
Yeah I tried this, if I do both persist_result=False, cache_result_in_memory=False, it works but I cannot use my task results within the same flow so it's not useable
j
even if you do `
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big_df.submit().result()
?
oh i see, one sec lemme play around with it.. stand-by
So with your original code, I had around 
(<tel:4001261235|4001261235>, 4001587381)
(I think it's in bytes and the right number is peak, but I honestly haven't used tracemalloc in a bit..) of memory usage. When I run this, I have about 
(826042347, 5485995247)
, which is lower left number, but higher right... which I don't know if this solves it. The left number here is the same number I get when I run range (1), and also by adding return_state=True, it seems to be a bit slower too.. I don't know if this is solution or even right direction, just sharing findings, lemme know what you think
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from prefect import task, flow
import pandas as pd
import numpy as np

import tracemalloc


@task(cache_result_in_memory=False)
def big_df():
    df = pd.DataFrame(np.ones((10**7, 10)))
    return df


@flow()
def child_flow():
    df = big_df.submit(return_state=True).result()

@flow()
def parent():
    for i in range(5):
        child_flow()
    print("lots of memory allocated here")

parent()
This has 
(26057819, 5486067324)
, which appears to be really high peak but lower memory.. progress haha *I don't think task runner is doing anything here, just second cache result is)
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from prefect import task, flow
from prefect.task_runners import SequentialTaskRunner
import pandas as pd
import numpy as np

import tracemalloc


@task(cache_result_in_memory=False)
def big_df():
    df = pd.DataFrame(np.ones((10**7, 10)))
    return df


@flow(
    cache_result_in_memory=False,
    task_runner=SequentialTaskRunner(),
)
def child_flow():
    df = big_df.submit(return_state=True).result()

@flow()
def parent():
    tracemalloc.start()

    print(tracemalloc.get_traced_memory())

    for i in range(5):
        child_flow()
    print("lots of memory allocated here")

    print("\n\n\n\n")

    print(tracemalloc.get_traced_memory())

    tracemalloc.stop()

parent()
a
With peak you mean peak memory usage? In my case I need to prioritize the peak because my prefect process is crashing because of the memory usage.
Thanks for having a look! These things should be more clearly documented...
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