https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> How can I stop the repeated execution...
# ask-marvin
s
@Marvin How can I stop the repeated execution of tasks or retries that continue despite having marked the task as successful in controlflow?
m
To stop the repeated execution of tasks or retries in ControlFlow, you can utilize termination conditions. ControlFlow supports flexible control over when an orchestration run should end through the use of 
run_until
conditions. These conditions allow you to specify complex termination logic based on various factors such as task completion, failure, or custom criteria. Here's a brief guide on how you might approach this: 1. Define Termination Conditions: Use the 
run_until
parameter when calling functions like 
run
, 
run_async
, 
run_tasks
, or 
run_tasks_async
to specify when the orchestration should terminate. For example, you can set a condition to stop execution after a certain number of attempts or when a task is marked as complete. 2. Combine Conditions: Termination conditions can be combined using boolean logic to create more complex criteria. For more details, you can refer to the ControlFlow documentation on running tasks. If you are using Prefect's core API, you might want to explore the top-level API for functions related to task execution control, such as 
allow_failure
or 
pause_flow_run
. However, the specifics will depend on your exact use case and the version of Prefect you are using.
s
@Marvin can you help with the following code? Can I not add some 20 sec timeout mechanism here?
Copy code
fields_detection = cf.Task(
            objective="Extract fields' values.",
            agents=[fields_detector],
            result_type=dict,
            context=context
        )
fields = fields_detection.run()
Given code goes into an infinite streaming loop.
m
In ControlFlow, you can implement a timeout mechanism using the 
run_until
conditions, which allow you to specify when a task should terminate. Although the documentation doesn't explicitly mention a timeout parameter, you can create a condition to stop the task if it runs longer than your desired duration. Here's a conceptual approach to implement a timeout: 1. Define a Timeout Condition: You can create a custom condition that checks the elapsed time since the task started and stops the task if it exceeds 20 seconds. 2. Use 
run_until
: Pass this condition to the 
run_until
parameter when executing the task. This will ensure the task terminates if it reaches the specified duration. For more details, refer to the ControlFlow documentation on running tasks. If ControlFlow doesn't support direct timeout settings, you may need to handle timeouts externally or modify the task's logic to respect a time constraint.
s
@Marvin How can I solve issue like the following? If you observe the image, it keeps repeating the same response so many times.
m
To address the issue of a task repeating the same response multiple times in ControlFlow, consider the following strategies: 1. Check Task Logic: Ensure that the task logic doesn't inherently cause repeated outputs. This might involve examining the underlying function or agent to confirm it processes inputs correctly and doesn't loop unintentionally. 2. Use Termination Conditions: As mentioned earlier, you can use the 
run_until
conditions to specify when a task should stop. This can prevent excessive repetitions by setting limits based on task completion, failure, or custom criteria. 3. Implement Idempotency: Design your task to be idempotent, meaning that running it multiple times with the same input should yield the same result without side effects. This can help prevent repeated outputs if the task is triggered multiple times. 4. Debugging and Logging: Add detailed logging within your task to understand its execution flow. This can help identify where and why the repetition occurs. 5. Concurrency Controls: If applicable, ensure your task execution doesn't involve unintended parallelism or concurrency that might lead to repeated outputs. If you have access to the specific image or log output, reviewing that might provide more insights into the root cause of the repetition. However, without seeing the image, these general strategies are a good starting point.
s
@Marvin Where am I supposed to place run_until in Agent(), Task() or run()?
m
The 
run_until
parameter should be used when you are executing tasks, specifically with functions like 
run
, 
run_async
, 
run_tasks
, or 
run_tasks_async
. It is not a parameter for 
Agent
or 
Task
initialization directly. Instead, you pass it when you call the run method on tasks or flows. Here's how you might use it conceptually:
Copy code
python
fields_detection.run(run_until=your_condition)
This allows you to specify termination conditions based on task completion, failure, or custom criteria, effectively controlling when the task execution should stop. For more detailed guidance, refer to the ControlFlow documentation on running tasks.
s
@Marvin check the following code and provide what is wrong here to remove the following error?
Copy code
# Define the run_until condition
def run_until_timeout(start_time, timeout=20):
    """Checks if the task has exceeded the timeout."""
    return time.time() - start_time > timeout

context = {"page_text": text}
start_time = time.time()
    try:
        fields_detection = cf.Task(
            objective="Extract fields' values.",
            agents=[fields_detector],
            result_type=dict,
            context=context
        )

        fields = cf.run_tasks(
            tasks=[fields_detection],
            run_until=run_until_timeout(start_time, timeout=20)
        )
Error:
Copy code
TypeError: 'bool' object is not callable
17:08:03.474 | ERROR   | Task run 'Run task: Task #78327020 ("Extract fields\' values.")' - Finished in state Failed("Task run encountered an exception TypeError: 'bool' object is not callable")
Error while finding fields: 'bool' object is not callable
m
The error you're encountering is due to the way the 
run_until
condition is being used. The 
run_until
parameter expects a callable function that returns a boolean, rather than the result of a boolean expression. Here's how you can fix it: 1. Define the Condition as a Callable: Instead of passing the result of 
run_until_timeout(start_time, timeout=20)
, you should pass the function itself, so it can be called during task execution. 2. Modify the Function to Accept No Arguments: You can use a lambda or a closure to pass the 
start_time
and 
timeout
without evaluating them immediately. Here's a revised version of your code:
Copy code
python
import time

# Define the run_until condition
def run_until_timeout(start_time, timeout=20):
    """Checks if the task has exceeded the timeout."""
    return time.time() - start_time > timeout

context = {"page_text": text}
start_time = time.time()

fields_detection = cf.Task(
    objective="Extract fields' values.",
    agents=[fields_detector],
    result_type=dict,
    context=context
)

# Pass a callable to run_until
fields = cf.run_tasks(
    tasks=[fields_detection],
    run_until=lambda: run_until_timeout(start_time, timeout=20)
)
This change ensures that 
run_until_timeout
is called during the task execution to check if the timeout condition is met, rather than being evaluated immediately when 
run_tasks
is called.
s
I made the changes, and getting new error @Marvin
Copy code
TypeError: fieldDetection.<locals>.<lambda>() takes 0 positional arguments but 1 was given
17:14:07.109 | ERROR   | Task run 'Run task: Task #2b7667d2 ("Extract fields\' values.")' - Finished in state Failed('Task run encountered an exception TypeError: fieldDetection.<locals>.<lambda>() takes 0 positional arguments but 1 was given')
Error while finding fields: fieldDetection.<locals>.<lambda>() takes 0 positional arguments but 1 was given
{}
m
The error you're encountering suggests that the 
run_until
lambda function is expected to accept an argument, but it currently does not. The 
run_until
condition you provide should be callable and should be able to handle the parameters passed by the ControlFlow system. To resolve this, you can modify the lambda function to accept the necessary argument. Here's a conceptual fix:
Copy code
python
import time

# Define the run_until condition
def run_until_timeout(start_time, timeout=20):
    """Checks if the task has exceeded the timeout."""
    return time.time() - start_time > timeout

context = {"page_text": text}
start_time = time.time()

fields_detection = cf.Task(
    objective="Extract fields' values.",
    agents=[fields_detector],
    result_type=dict,
    context=context
)

# Adjust the lambda to accept the required argument
fields = cf.run_tasks(
    tasks=[fields_detection],
    run_until=lambda *args: run_until_timeout(start_time, timeout=20)
)
The 
*args
in the lambda function allows it to accept any arguments that might be passed by the ControlFlow system, even if they are not used in your logic. This should prevent the 
TypeError
you're seeing.
s
@Marvin how can I solve the following prefect error :
Copy code
12:02:57.159 | ERROR   | GlobalEventLoopThread | prefect._internal.concurrency - Service 'EventsWorker' failed to process item Event(occurred=DateTime(2025, 2, 21, 6, 14, 36, 60827, tzinfo=Timezone('UTC')), event='prefect.task-run.Running', resource=Resource(root={'prefect.resource.id': 'prefect.task-run.ba142228-1c8a-4fb6-a365-fd4430c7c311', 'prefect.resource.name': 'Update Operation Task-4d8', 'prefect.state-message': '', 'prefect.state-name': 'Running', 'prefect.state-timestamp': '2025-02-21T06:14:36.060827+00:00', 'prefect.state-type': 'RUNNING', 'prefect.orchestration': 'client'}), related=[RelatedResource(root={'prefect.resource.id': 'prefect.flow-run.2129eac2-ed2a-45bf-b151-a8f5465976b0', 'prefect.resource.role': 'flow-run', 'prefect.resource.name': 'hypnotic-lemming'}), RelatedResource(root={'prefect.resource.id': 'prefect.flow.c669e2ec-5e97-463b-b8f6-58d93df7d403', 'prefect.resource.role': 'flow', 'prefect.resource.name': 'AI Flow'})], payload={'intended': {'from': 'PENDING', 'to': 'RUNNING'}, 'initial_state': {'type': 'PENDING', 'name': 'Pending', 'message': '', 'state_details': {}}, 'validated_state': {'type': 'RUNNING', 'name': 'Running', 'message': '', 'state_details': {}, 'data': None}, 'task_run': {'name': 'Update Operation Task-4d8', 'task_key': 'update_operation_task-afd2b636', 'dynamic_key': '4d8b424e-70a0-4af0-8d93-6684ec6856cd', 'empirical_policy': {'max_retries': 0, 'retry_delay_seconds': 0.0, 'retries': 5, 'retry_delay': 5}, 'tags': [], 'labels': {}, 'task_inputs': {'operation_task_id': [], 'result': [], 'status': []}, 'run_count': 1, 'flow_run_run_count': 1, 'expected_start_time': '2025-02-21T06:14:36.059811Z', 'start_time': '2025-02-21T06:14:36.060827Z', 'total_run_time': 0.0}}, id=UUID('70e53acf-a5dd-4e8c-a946-a2b707759dcd'), follows=UUID('f67b5b4b-8350-4835-97fc-cf4a4dd09029'))
12:03:07.161 | WARNING | prefect.events.clients - Unable to connect to '<wss://api.prefect.cloud/api/accounts/37a363d9-7501-4c4f-9ad0-95716d629955/workspaces/a4c5b649-144a-4a79-a8b3-72feedab756b/events/in>'. Please check your network settings to ensure websocket connections to the API are allowed. Otherwise event data (including task run data) may be lost. Reason: . Set PREFECT_DEBUG_MODE=1 to see the full error.
12:03:07.163 | ERROR   | GlobalEventLoopThread | prefect._internal.concurrency - Service 'EventsWorker' failed to process item Event(occurred=DateTime(2025, 2, 21, 6, 14, 39, 223367, tzinfo=Timezone('UTC')), event='prefect.task-run.Completed', resource=Resource(root={'prefect.resource.id': 'prefect.task-run.ba142228-1c8a-4fb6-a365-fd4430c7c311', 'prefect.resource.name': 'Update Operation Task-4d8', 'prefect.state-message': '', 'prefect.state-name': 'Completed', 'prefect.state-timestamp': '2025-02-21T06:14:39.223367+00:00', 'prefect.state-type': 'COMPLETED', 'prefect.orchestration': 'client'}), related=[RelatedResource(root={'prefect.resource.id': 'prefect.flow-run.2129eac2-ed2a-45bf-b151-a8f5465976b0', 'prefect.resource.role': 'flow-run', 'prefect.resource.name': 'hypnotic-lemming'}), RelatedResource(root={'prefect.resource.id': 'prefect.flow.c669e2ec-5e97-463b-b8f6-58d93df7d403', 'prefect.resource.role': 'flow', 'prefect.resource.name': 'AI Flow'})],
m
Your message was too long by 597 tokens...
s
Copy code
12:02:57.159 | ERROR   | GlobalEventLoopThread | prefect._internal.concurrency - Service 'EventsWorker' failed to process item Event(occurred=DateTime(2025, 2, 21, 6, 14, 36, 60827, tzinfo=Timezone('UTC')), event='prefect.task-run.Running', resource=Resource(root={'prefect.resource.id': 'prefect.task-run.ba142228-1c8a-4fb6-a365-fd4430c7c311', 'prefect.resource.name': 'Update Operation Task-4d8', 'prefect.state-message': '', 'prefect.state-name': 'Running', 'prefect.state-timestamp': '2025-02-21T06:14:36.060827+00:00', 'prefect.state-type': 'RUNNING', 'prefect.orchestration': 'client'}), related=[RelatedResource(root={'prefect.resource.id': 'prefect.flow-run.2129eac2-ed2a-45bf-b151-a8f5465976b0', 'prefect.resource.role': 'flow-run', 'prefect.resource.name': 'hypnotic-lemming'}), RelatedResource(root={'prefect.resource.id': 'prefect.flow.c669e2ec-5e97-463b-b8f6-58d93df7d403', 'prefect.resource.role': 'flow', 'prefect.resource.name': 'AI Flow'})], payload={'intended': {'from': 'PENDING', 'to': 'RUNNING'}, 'initial_state': {'type': 'PENDING', 'name': 'Pending', 'message': '', 'state_details': {}}, 'validated_state': {'type': 'RUNNING', 'name': 'Running', 'message': '', 'state_details': {}, 'data': None}, 'task_run': {'name': 'Update Operation Task-4d8', 'task_key': 'update_operation_task-afd2b636', 'dynamic_key': '4d8b424e-70a0-4af0-8d93-6684ec6856cd', 'empirical_policy': {'max_retries': 0, 'retry_delay_seconds': 0.0, 'retries': 5, 'retry_delay': 5}, 'tags':...
@Marvin can you solve the above error message?
m
I'm afraid I don't have the context of the error message you're referring to. Could you please provide the error message or describe the issue you're encountering in more detail?
s
@Marvin solve the error:
Copy code
12:02:57.159 | ERROR   | GlobalEventLoopThread | prefect._internal.concurrency - Service 'EventsWorker' failed to process item Event(occurred=DateTime(2025, 2, 21, 6, 14, 36, 60827, tzinfo=Timezone('UTC')), event='prefect.task-run.Running', resource=Resource(root={'prefect.resource.id': 'prefect.task-run.ba142228-1c8a-4fb6-a365-fd4430c7c311', 'prefect.resource.name': 'Update Operation Task-4d8', 'prefect.state-message': '', 'prefect.state-name': 'Running', 'prefect.state-timestamp': '2025-02-21T06:14:36.060827+00:00', 'prefect.state-type': 'RUNNING', 'prefect.orchestration': 'client'}), related=[RelatedResource(root={'prefect.resource.id': 'prefect.flow-run.2129eac2-ed2a-45bf-b151-a8f5465976b0', 'prefect.resource.role': 'flow-run', 'prefect.resource.name': 'hypnotic-lemming'}), RelatedResource(root={'prefect.resource.id': 'prefect.flow.c669e2ec-5e97-463b-b8f6-58d93df7d403', 'prefect.resource.role': 'flow', 'prefect.resource.name': 'AI Flow'})], payload={'intended': {'from': 'PENDING', 'to': 'RUNNING'}, 'initial_state': {'type': 'PENDING', 'name': 'Pending', 'message': '', 'state_details': {}}, 'validated_state': {'type': 'RUNNING', 'name': 'Running', 'message': '', 'state_details': {}, 'data': None}, 'task_run': {'name': 'Update Operation Task-4d8', 'task_key': 'update_operation_task-afd2b636', 'dynamic_key': '4d8b424e-70a0-4af0-8d93-6684ec6856cd', 'empirical_policy': {'max_retries': 0, 'retry_delay_seconds': 0.0, 'retries': 5, 'retry_delay': 5}, 'tags':...
m
Your message was too long by 2 tokens...
s
Fine. solve this @Marvin
Copy code
/events/in'. Please check your network settings to ensure websocket connections to the API are allowed. Otherwise event data (including task run data) may be lost. Reason: . Set PREFECT_DEBUG_MODE=1 to see the full error.
m
This error message suggests that there is an issue with establishing a WebSocket connection to the Prefect API. Here are a few steps you can take to troubleshoot and potentially resolve the issue: 1. Network Settings: Ensure that your network allows WebSocket connections. This might involve checking firewall settings or network policies that could be blocking such connections. 2. Environment Variables: Check if the environment variable 
PREFECT_API_URL
is correctly set to the API endpoint you are trying to connect to. 3. Debug Mode: Set the environment variable 
PREFECT_DEBUG_MODE=1
to enable debug mode and get more detailed error messages. This might give you more insight into what is causing the connection to fail. 4. Proxy Settings: If you are behind a corporate proxy, ensure that your proxy settings allow WebSocket connections. 5. Prefect Server: Ensure that the Prefect server is running and accessible from your client. You can test this by trying to access the API endpoint directly from your browser or using a tool like 
curl
. If these steps do not resolve the issue, I recommend checking the Prefect logs for more detailed error messages or reaching out to the Prefect community for further assistance. If you need more specific information from the Prefect documentation, I can search for relevant topics.
4 Views
Open in Slack
PreviousNext
Powered by