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<@ULVA73B9P> If I retry a flow run in the UI, the ...
# ask-marvin
n
@Marvin If I retry a flow run in the UI, the parameters are not the same. The Pydantic model is evaluated again. Since I have a default_factory on a field, the value is not the same anymore
m
When retrying a flow run in Prefect, the parameters are re-evaluated, which can lead to different values if you're using a 
default_factory
in a Pydantic model. This behavior is due to the fact that the 
default_factory
is executed again, generating a new value each time. To maintain the same parameter values on retries, consider the following approaches: 1. Persist Parameters: Store the initial parameters in a persistent storage or state and retrieve them on retries to ensure consistency. 2. Manual Parameter Handling: Override the default behavior by capturing the parameters at the start of the flow run and reusing them if a retry occurs. 3. Avoid 
default_factory
for Critical Defaults: If the default value must remain constant across retries, consider setting a static default value or handling the default logic within the flow itself. For more details on result persistence and handling in Prefect, you can refer to the Prefect documentation on results. If you have further questions or need assistance with specific implementations, feel free to ask!
n
@Marvin How do I implement the Manual Paramter Handling
m
To implement manual parameter handling in Prefect, you can capture and store the parameters at the beginning of the flow run. Here's a simple approach using Prefect's API: 1. Capture Parameters: At the start of the flow, capture the parameters and store them in a location that can be accessed during retries. This could be a database, a file, or a Prefect block. 2. Retrieve Parameters on Retry: When a flow run is retried, retrieve the stored parameters and use them instead of the default ones. Here's a basic implementation example:
Copy code
python
from prefect import flow, task
import json

# Task to store parameters
@task
def store_parameters(params, file_path="params.json"):
    with open(file_path, "w") as f:
        json.dump(params, f)

# Task to retrieve parameters
@task
def retrieve_parameters(file_path="params.json"):
    with open(file_path, "r") as f:
        return json.load(f)

# Your flow
@flow
def my_flow(param1=None, param2=None):
    # Check if parameters are already stored
    try:
        params = retrieve_parameters()
        param1 = params.get("param1", param1)
        param2 = params.get("param2", param2)
    except FileNotFoundError:
        # Store parameters if not already stored
        store_parameters({"param1": param1, "param2": param2})

    # Your flow logic here
    print(f"Running with param1: {param1}, param2: {param2}")

# Run the flow
my_flow(param1="initial_value1", param2="initial_value2")
### Key Points: - Parameter Storage: Use a simple JSON file to store and retrieve parameters. This can be replaced with any persistent storage you prefer. - Flow Logic: The flow checks for existing parameters on each run and uses them if available. For more detailed guidance on Prefect's flow and task management, you can refer to the Prefect documentation on input handling. If you have further questions or need more complex implementations, feel free to ask!
n
@Marvin Also, can I call a task from another task?
m
In Prefect, tasks are designed to be independent units of work, and the typical pattern is to orchestrate task execution within a flow. However, you can call a task from another task if necessary, though this is not the standard practice and might lead to unexpected behavior in some scenarios. ### Standard Practice - Orchestrate in a Flow: Generally, you should orchestrate task execution within a flow, where tasks can be defined and their dependencies managed. ### Calling a Task from Another Task If you must call a task from another task, you can do so by invoking the task function directly. Here's a simple example:
Copy code
python
from prefect import flow, task

@task
def task_a():
    return "Task A result"

@task
def task_b():
    # Call task_a from task_b
    result = task_a.fn()  # Use .fn() to call the task function directly
    print(f"Task B received: {result}")

@flow
def my_flow():
    task_b()

my_flow()
### Important Considerations: - Direct Invocation: Using 
.fn()
bypasses Prefect's task orchestration features, so you won't get automatic retries, logging, or result handling for the inner task call. - Dependency Management: Ensure that your flow manages dependencies correctly to avoid issues with task execution order. For more details on task orchestration and dependencies, you can refer to the Prefect documentation on tasks. If you have further questions or need guidance on a specific use case, feel free to ask!
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