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<@ULVA73B9P> Prefect isn't detecting task dependen...
# ask-marvin
t
@Marvin Prefect isn't detecting task dependencies when I invoke a function manually. However, the documentation claims that this would happen automatically. (I can't imagine how that would work, but I was hoping to be pleasantly surprised). > When a result from a task is used as input for another task, Prefect automatically recognizes the task that result originated from as an upstream dependency. > This applies to every way you can run tasks with Prefect, whether you're calling the task function directly, calling 
.submit()
, or calling 
.map()
. Subflows similarly recognize tasks results as upstream dependencies. Another page says it's a good idea to use either all task invocations or all python invocations
Copy code
@task(log_prints=True)
def get_random_number() -> int:
    return random.randint(0, 100)


@task(log_prints=True)
def print_random_number(random_number: int) -> None:
    print(f"The random number is: {random_number}")


@flow(log_prints=True)
def random_number_flow():
(dependency not detected)")
    print_random_number(get_random_number())

    random_number_result = get_random_number.submit()
    print_random_number.submit(random_number_result).wait()
m
Your message was too long by 50 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Prefect isn't detecting task dependencies when I invoke a function manually. However, the documentation claims that this would happen automatically. (I can't imagine how that would work, but I was hoping to be pleasantly surprised). The documentation: https://orion-docs.prefect.io/latest/guides/specifying-upstream-dependencies/
Automatic
When a result from a task is used as input for another task, Prefect automatically recognizes the task that result originated from as an upstream dependency.
This applies to every way you can run tasks with Prefect, whether you're calling the task function directly, calling 
.submit()
, or calling 
.map()
. Subflows similarly recognize tasks results as upstream dependencies.
This page says it's a good idea to use either all task invocations or all python invocations (calling the method directly) https://orion-docs.prefect.io/latest/concepts/task-runners/?h=submit#using-results-from-submitted-tasks ```@task(log_prints=True) def get_random_number() -> int: return random.randint(0, 100) @task(log_prints=True) def print_random_number(random_number: int) -> None: print(f"The random number is: {random_number}") @flow(log_prints=True) def random_number_flow():
From the information gathered, it looks like there might be some confusion with how dependencies are detected and managed in Prefect, especially when invoking tasks in different ways. Here's the breakdown based on Prefect's documentation and issue tracking: 1. Automatic Dependency Resolution: Prefect does attempt to automatically detect dependencies when the output of one task is used as an input to another. This should work when you are directly using task functions in a sequence. However, sometimes manually using 
.submit()
and 
.map()
might require more explicit dependency management. 2. Explicit Dependency Management: Prefect allows you to be explicit about dependencies, which might help in scenarios where automatic detection isn't working as expected. This can be done using the 
wait_for
parameter, where you specify tasks that must be completed before proceeding. More about this can be found in Prefect 2.x documentation and Prefect 3.x documentation. 3. Multiple Invocations: It's mentioned that using a mix of task invocation methods (direct call vs. 
.submit()
) can lead to confusion. The 
.submit()
method should wrap the task execution into a future, ensuring that dependencies are managed more dynamically at runtime. The 3.x docs explain how 
.submit()
works, where it waits for dependencies to complete before proceeding. 4. Known Issues: There have been issues reported, like tasks being executed multiple times on some platforms, which might lead to oddities in execution flow if not configured properly. An example is with deployments on GKE Autopilot. GitHub Issue #5485 discusses these challenges and might be worth a brief read if you are deploying in similar environments. To solve the problem you're facing: - Ensure you use consistent invocation methods within a single flow to leverage automatic dependencies. - Use the 
wait_for
parameter explicitly if you are mixing different methods or setups that might confuse Prefect's automatic detector. - Double-check any environment-specific quirks documented in Prefect's GitHub issues if you're deploying on platforms like GKE. Hope this helps. Don't forget, not all robots are perfect at perfecting dependencies!
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