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<@ULVA73B9P> my flow exited with status code: -9; ...
# marvin-in-the-wild
k
@Marvin my flow exited with status code: -9; with the message: "This indicates that the process exited due to a SIGKILL signal. Typically, this is either caused by manual cancellation or high memory usage causing the operating system to terminate the process.". can i log this error message at mlflow?
m
To log the error message in MLflow from a Prefect flow, you can configure MLflow to capture logs and then log the specific error. Here’s a step-by-step guide to achieve this: 1. Set up MLflow logging: First, you need to install the required libraries:
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sh
   pip install mlflow prefect
2. Integrate MLflow logging in your Prefect flow: Modify your Prefect flow to log messages to MLflow. Here’s an example code snippet:
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python
   import mlflow
   import logging
   from prefect import flow, get_run_logger

   # Initialize MLflow logging
   mlflow.set_experiment("my_experiment")

   def log_to_mlflow(message):
       # Log an error message to MLflow
       mlflow.log_param("flow_status", "error")
       mlflow.log_text(message, "error.log")

   @flow(name="log-error-flow")
   def log_error_flow():
       logger = get_run_logger()
       try:
           # Your flow logic here
           raise Exception("Flow execution failed due to some error.")
       except Exception as e:
           error_message = f"Flow exited with error: {e}"
           logger.error(error_message)
           log_to_mlflow(error_message)
           raise

   if __name__ == "__main__":
       log_error_flow()
In this example, the 
log_to_mlflow
function captures the error message and logs it to MLflow. 3. Run your flow to see the results: When you run the flow, if any errors occur, the error message will be logged to MLflow. You can find more information about Prefect logging from the Prefect logging guide. For more details on potential issues or suggestions, you can refer to the related GitHub issue. If you need further help, let me know. It’s not like I have anything better to do.
k
@Marvin does SIGKILL raise any exception i can catch? the default try-except doesn't work
m
A 
SIGKILL
signal (signal number 9) is an unrecoverable signal that the operating system sends to terminate a process. This means the process is immediately terminated without any opportunity to catch the signal or to run any cleanup code. In Python, you can handle other signals like 
SIGTERM
or 
SIGINT
by using signal handlers, but 
SIGKILL
cannot be caught or ignored by the application. This is why your 
try-except
block doesn't work for handling 
SIGKILL
. ### Handling Other Signals If you want to handle other signals like 
SIGTERM
(which can be caught), you can set up a signal handler like this:
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python
import signal
import sys
from prefect import flow, get_run_logger

def handle_sigterm(signum, frame):
    logger = get_run_logger()
    error_message = "Received SIGTERM, performing cleanup."
    logger.error(error_message)
    # Logging to MLflow
    log_to_mlflow(error_message)
    sys.exit(0)

# Register the SIGTERM handler
signal.signal(signal.SIGTERM, handle_sigterm)

def log_to_mlflow(message):
    import mlflow
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("flow_status", "terminated")
    mlflow.log_text(message, "error.log")

@flow(name="handle-signals-flow")
def handle_signals_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Flow started.")
    # Simulate a long-running task
    try:
        while True:
            pass
    except Exception as e:
        error_message = f"Unexpected error: {e}"
        logger.error(error_message)
        log_to_mlflow(error_message)
        raise

if __name__ == "__main__":
    handle_signals_flow()
In this code: - The 
handle_sigterm
function handles the 
SIGTERM
signal. - The signal handler is registered using 
signal.signal(signal.SIGTERM, handle_sigterm)
. - If the flow receives a 
SIGTERM
, it logs the message, calls 
log_to_mlflow
, and exits gracefully. ### Summary - 
SIGKILL
cannot be caught or handled by your application. The operating system immediately terminates your process. - You can handle other signals like 
SIGTERM
and 
SIGINT
using signal handlers and perform necessary cleanup. If you face 
SIGKILL
due to high memory usage or other resource constraints, monitoring your system resources and optimizing your flow may help avoid these situations.
k
@Marvin in the code you sent, what happens if my flow ends with sigkill? will it log the status of sigterm in my mlflow?
m
No, if your flow ends with a SIGKILL signal, it won't log the SIGTERM status in your MLflow because SIGKILL cannot be caught or handled. When a process receives SIGKILL, it terminates immediately without executing any cleanup code, signal handlers, or logging mechanisms. However, you can monitor the status of your flow externally. Here's a way to handle it: 1. External Monitoring: Use an external process or daemon to monitor the health and status of your flow. This external monitor can periodically check if your flow is running and log to MLflow or another logging service if it detects an unexpected termination. 2. Flow Logging: Ensure that your flow logs its start and successful completion so that you can infer if it was terminated unexpectedly. ### Example with External Monitoring Here's a basic example of how you might set up an external monitor script:
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python
import time
import subprocess
import mlflow

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("flow_status", status)
    mlflow.log_text(message, "status.log")

def monitor_flow():
    process = subprocess.Popen(["python", "your_flow_script.py"])
    try:
        while True:
            time.sleep(1)
            if process.poll() is not None:  # Process has terminated
                ret_code = process.returncode
                if ret_code == -9:  # SIGKILL
                    log_to_mlflow("killed", "Process was killed with SIGKILL.")
                else:
                    log_to_mlflow("stopped", f"Process ended with code {ret_code}.")
                break
    except KeyboardInterrupt:
        process.terminate()
        log_to_mlflow("terminated", "Monitor was manually terminated.")

if __name__ == "__main__":
    monitor_flow()
This script runs your flow and monitors its status: - If the process ends with a return code of 
-9
, it logs to MLflow that it was killed with SIGKILL. - You can extend this to handle other signals and statuses. ### Summary - SIGKILL is not catchable and will terminate your flow abruptly. - Use an external monitoring solution to handle and log such situations. - Ensure MLflow logging for flow start, completion, or any detection of unexpected termination by the external monitor. This way, you can have robust logging that covers unexpected terminations.
k
@Marvin when my flow is beign cancelled, my task has already received the sigterm? can i catch my task state and log it at mlflow?
m
Yes, when a flow is being cancelled, tasks within the flow generally receive a 
SIGTERM
signal to terminate them gracefully. You can catch this signal in your task and log its state to MLflow before the task exits. ### Handling 
SIGTERM
in a Task Here's an example of how you can handle 
SIGTERM
in a Prefect task and log its state to MLflow:
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python
import signal
import sys
import mlflow
from prefect import flow, task, get_run_logger

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

def handle_sigterm(signum, frame):
    logger = get_run_logger()
    message = "Task received SIGTERM, logging state to MLflow."
    logger.error(message)
    log_to_mlflow("terminated", message)
    sys.exit(0)

# Register the SIGTERM handler
signal.signal(signal.SIGTERM, handle_sigterm)

@task
def my_task():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Task started.")
    try:
        # Simulate work
        for i in range(10):
            <http://logger.info|logger.info>(f"Working... {i+1}/10")
            time.sleep(1)
    except Exception as e:
        error_message = f"Unexpected error: {e}"
        logger.error(error_message)
        log_to_mlflow("error", error_message)
        raise

@flow(name="handle-sigterm-task-flow")
def handle_sigterm_task_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Flow started.")
    my_task()

if __name__ == "__main__":
    handle_sigterm_task_flow()
### Important Points: 1. Signal Handler: The 
handle_sigterm
function handles the 
SIGTERM
signal. It logs the signal and the task state to MLflow before exiting. 2. Registration: The signal handler is registered using 
signal.signal(signal.SIGTERM, handle_sigterm)
. 3. Task Execution: The main task (
my_task
) simulates some work and can be interrupted by a 
SIGTERM
. ### Running the Task When you run the flow, if it is cancelled (thus sending 
SIGTERM
to the task), the 
handle_sigterm
function will log the termination state to MLflow. ### Note: - The flow itself will still be managed by Prefect, including retries or state management. The task state is logged just before exiting due to 
SIGTERM
. - This example uses basic logging and can be extended based on your specific requirements. By handling the 
SIGTERM
signal, you can ensure that your task logs its final state to MLflow before termination, providing better observability into your task's lifecycle.
k
@Marvin hey, I'm running my prefect flows using ECS. but when ECS returns this error: RuntimeError: {'tasks': [], 'failures': [{'reason': 'Capacity is unavailable at this time. Please try again later or in a different availability zone'}], 'ResponseMetadata': {'RequestId': 'da71d935-9b1f-4277-b6e9-cad475d0682d', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'da71d935-9b1f-4277-b6e9-cad475d0682d', 'content-type': 'application/x-amz-json-1.1', 'content-length': '135', 'date': 'Wed, 26 Jun 2024 165844 GMT'}, 'RetryAttempts': 0}} my prefect flow crashes. can I modify the number of retry attempts to increase my resilience?
m
To increase the resilience of your Prefect flow running on ECS by modifying the number of retry attempts, you can configure the retry behavior directly in your Prefect tasks. Prefect offers flexible retry options, including exponential backoff and jitter, which will help manage retries more effectively when encountering capacity issues. ### Implementing Retry Logic in Prefect Tasks Here's how you can set up retries for a task in Prefect using exponential backoff and jitter to handle ECS capacity issues:
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python
from prefect import flow, task
from prefect.tasks import exponential_backoff
import mlflow

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5))
def ecs_task():
    # Simulating task logic that interacts with ECS
    # Replace this with your actual ECS task logic
    import boto3
    ecs_client = boto3.client('ecs')
    
    try:
        response = ecs_client.run_task(
            cluster='your-cluster',
            taskDefinition='your-task-def',
            count=1,
            launchType='FARGATE'
            # other task parameters
        )
        failures = response.get('failures', [])
        if failures:
            raise RuntimeError(f"Task failed: {failures}")
    except Exception as e:
        error_message = f"Task execution failed: {e}"
        log_to_mlflow("error", error_message)
        raise

@flow(name="ecs-with-retries-flow")
def ecs_with_retries_flow():
    ecs_task()

if __name__ == "__main__":
    ecs_with_retries_flow()
### Explanation: 1. Task Configuration: - The 
ecs_task
is configured with 
retries=5
, which means it will retry up to five times. - 
retry_delay_seconds
is set to use 
exponential_backoff
with a base backoff of 10 seconds and a jitter factor of 0.5 to avoid thundering herds. 2. Handling Failures: - If the ECS client returns failures, the task will raise a 
RuntimeError
, triggering the retry mechanism. - The task logs the error state to MLflow using 
log_to_mlflow
before raising the exception. 3. Flow Execution: - The flow 
ecs_with_retries_flow
runs the 
ecs_task
, inheriting its retry configuration. ### Additional Considerations: - ECS Capacity Providers: You might also want to explore setting up ECS capacity providers to handle scaling more effectively, as described in the Prefect GitHub issue. - System-Level Monitoring: Ensure you have system-level alerts and monitoring in place to catch persistent capacity issues that exceed the retries configured within your tasks. By implementing these retry strategies, you increase the resilience of your Prefect flows against transient ECS capacity issues. You can adjust the retry parameters according to your specific needs and operational constraints.
k
@Marvin is there any other way? can i set this at variables?
m
Yes, you can configure task retries and retry delays using environment variables in Prefect. This allows you to set default values for retries globally, rather than specifying them within each task. Here’s how you can do it: ### Setting Global Retry Configuration You can set the retry configuration globally using Prefect’s configuration system. This is particularly useful in environments like ECS where you might want to avoid hardcoding these values.
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sh
# Set default number of retries for flows
prefect config set PREFECT_FLOW_DEFAULT_RETRIES=2

# Set default number of retries for tasks
prefect config set PREFECT_TASK_DEFAULT_RETRIES=3

# Set default retry delays for flows (in seconds)
prefect config set PREFECT_FLOW_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]

# Set default retry delays for tasks (in seconds)
prefect config set PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]
The above settings can be added to your environment setup script or directly in the command line before running your Prefect agent or worker. ### Using Configuration in Your Prefect Tasks and Flows When these settings are applied, they will provide default values for retries and retry delays for all tasks and flows, unless explicitly overridden:
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python
from prefect import flow, task, get_run_logger
import mlflow

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

@task
def ecs_task():
    import boto3
    ecs_client = boto3.client('ecs')
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting ECS task...")
    
    try:
        response = ecs_client.run_task(
            cluster='your-cluster',
            taskDefinition='your-task-definition',
            count=1,
            launchType='FARGATE'
        )
        failures = response.get('failures', [])
        if failures:
            raise RuntimeError(f"Task failed: {failures}")
    except Exception as e:
        error_message = f"Task execution failed: {e}"
        logger.error(error_message)
        log_to_mlflow("error", error_message)
        raise

@flow(name="ecs-with-global-retries")
def ecs_with_global_retries_flow():
    ecs_task()

if __name__ == "__main__":
    ecs_with_global_retries_flow()
### Summary 1. Global Configuration: Set global retry settings via Prefect’s configuration system using environment variables. 2. Environment Setup: You can configure these settings as part of your environment setup script. This is useful for making changes without altering your codebase. 3. Task and Flow Behavior: With these settings, Prefect tasks and flows will inherit the default retry behaviors unless they are specifically overridden. Using these configurations, you can increase the resilience of your Prefect flows running on ECS by leveraging retries more effectively. If the ECS capacity is temporarily unavailable, the tasks will retry according to the configuration you set, reducing the likelihood of failures due to transient capacity issues.
k
@Marvin if i only put retries at my task, would it be able to recover from the ecs error? RuntimeError: {'tasks': [], 'failures': [{'reason': 'Capacity is unavailable at this time. Please try again later or in a different availability zone'}], 'ResponseMetadata': {'RequestId': 'da71d935-9b1f-4277-b6e9-cad475d0682d', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'da71d935-9b1f-4277-b6e9-cad475d0682d', 'content-type': 'application/x-amz-json-1.1', 'content-length': '135', 'date': 'Wed, 26 Jun 2024 165844 GMT'}, 'RetryAttempts': 0}}
m
Yes, if you configure retries within your Prefect task, it should be able to recover from the ECS error 
RuntimeError: {'reason': 'Capacity is unavailable at this time. Please try again later or in a different availability zone'}
by retrying the task after an appropriate delay. Here's how you can set up the task with retry logic: ### Setting Up Retries in a Task You can use the retry configuration directly in your Prefect task to handle transient ECS capacity issues:
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python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
import mlflow

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5))
def ecs_task():
    logger = get_run_logger()
    ecs_client = boto3.client('ecs')
    <http://logger.info|logger.info>("Starting ECS task...")
    
    try:
        response = ecs_client.run_task(
            cluster='your-cluster',
            taskDefinition='your-task-definition',
            count=1,
            launchType='FARGATE'
        )
        
        failures = response.get('failures', [])
        if failures:
            raise RuntimeError(f"Task failed: {failures}")
        
        <http://logger.info|logger.info>("ECS task started successfully.")
        
    except Exception as e:
        error_message = f"Task execution failed: {e}"
        logger.error(error_message)
        log_to_mlflow("error", error_message)
        raise

@flow(name="ecs-task-flow-with-retries")
def ecs_task_flow():
    ecs_task()

if __name__ == "__main__":
    ecs_task_flow()
### Explanation: 1. Retry Configuration: - The task 
ecs_task
is configured to retry up to 5 times. - 
retry_delay_seconds
uses 
exponential_backoff
with a base backoff factor of 10 seconds and a jitter factor of 0.5 to stagger retries, preventing potential overloading of the ECS service. 2. Handling Failures: - If the ECS client returns failures (e.g., capacity being unavailable), the task raises a 
RuntimeError
. - The task logs the error state to MLflow using 
log_to_mlflow
before raising the exception, allowing you to track task failures. 3. Flow Execution: - The flow 
ecs_task_flow
runs the 
ecs_task
, inheriting its retry configuration. ### Global Configuration (Optional) While the above setup should handle your requirement, you can also set up global retry settings as previously discussed for more comprehensive resilience across all tasks and flows in your environment:
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sh
# You can set these configurations as needed
prefect config set PREFECT_TASK_DEFAULT_RETRIES=5
prefect config set PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]
### Summary - Task-Level Retry: By configuring retries directly within your task, you ensure that the task will retry on encountering ECS capacity issues. - Logging: Failures and retries are logged, allowing you to monitor and diagnose issues. - Resilience: This approach provides resilience against transient errors by automatically retrying the task. By following this approach, your task should be able to recover from ECS errors related to capacity being unavailable, thus increasing the robustness of your workflow.
k
@Marvin can my on_failure hook verify that i'm still retrying before take the action? my on_failure saves my code error in my database, but i dont want this to happen until i have made all retries possible and my flow actually failed.
m
Yes, you can implement an 
on_failure
hook that only executes its logic after all retries have been exhausted and the task has genuinely failed. To achieve this, you can check the current retry count and compare it to the maximum number of retries within the 
on_failure
hook. ### Example Implementation Below is a code example demonstrating how to set up an 
on_failure
hook that saves the error to a database only after all retries have been exhausted: 1. Define the Task with the 
on_failure
Hook:
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python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
from prefect.engine.signals import FAIL
import mlflow
import os

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

def on_failure(task, state, exception):
    logger = get_run_logger()
    current_retry_count = state.context.get("task_run_count", 0)
    retry_limit = task.retries
    
    if current_retry_count >= retry_limit:
        error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
        logger.error(error_message)
        log_to_mlflow("final_failure", error_message)
        # Save error to database or take other actions
        save_error_to_database(task.name, exception)
    else:
        <http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")

def save_error_to_database(task_name, exception):
    # Placeholder for your database logic
    # Replace with actual database interaction code
    print(f"Saving error to database: {task_name} - {exception}")

@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5), on_failure=on_failure)
def ecs_task():
    logger = get_run_logger()
    ecs_client = boto3.client('ecs')
    <http://logger.info|logger.info>("Starting ECS task...")
    
    try:
        response = ecs_client.run_task(
            cluster='your-cluster',
            taskDefinition='your-task-def',
            count=1,
            launchType='FARGATE'
        )
        
        failures = response.get('failures', [])
        if failures:
            raise RuntimeError(f"Task failed: {failures}")
        
        <http://logger.info|logger.info>("ECS task started successfully.")
        
    except Exception as e:
        error_message = f"Task execution failed: {e}"
        logger.error(error_message)
        log_to_mlflow("error", error_message)
        raise

@flow(name="ecs-task-flow-with-retries")
def ecs_task_flow():
    ecs_task()

if __name__ == "__main__":
    ecs_task_flow()
### Explanation: 1. Retry Logic: - The task 
ecs_task
includes a retry configuration with exponential backoff and jitter. 2. Custom 
on_failure
Hook: - The 
on_failure
function checks the current retry count (
current_retry_count
) against the retry limit (
retry_limit
). - If all retries have been exhausted (
current_retry_count >= retry_limit
), it logs an error message, and logs the final status to MLflow, and executes the error-saving logic in 
save_error_to_database
. - If retries are still ongoing, it logs the intermediate failure states without saving to the database. 3. Flow Execution: - The 
ecs_task_flow
runs the 
ecs_task
, inheriting its retry configuration and 
on_failure
hook. ### Summary: - Selective Error Logging: The 
on_failure
hook conditionally performs actions based on whether the task has genuinely failed after all retries. - Controlled Error Reporting: This prevents premature logging of errors to your database if the task is still retrying. - Resilient Error Handling: Ensures that errors are captured and handled appropriately, only after you've exhausted all retry attempts.
By following this approach, you ensure that the error is only logged to the database after the task has completely failed, avoiding premature error reports during retries.
k
@Marvin will this work even using dask, right?
m
Yes, this approach will work with Dask as well. Prefect handles task retries, state transitions, and hooks in a backend-independent way. The logic for retries and 
on_failure
hooks remains the same regardless of the execution backend, whether it's local, Dask, Kubernetes, or other infrastructure. Here's how to integrate the task and flow defined earlier with a Dask executor: 1. Set Up Dask Executor: Install Dask if you haven't already:
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sh
   pip install dask[distributed]
2. Use Dask Executor in the Flow: Modify your flow to use the Dask task runner:
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python
   import boto3
   from prefect import flow, task, get_run_logger
   from prefect.tasks import exponential_backoff
   from prefect.engine.signals import FAIL
   import mlflow
   from prefect.task_runners import DaskTaskRunner

   def log_to_mlflow(status, message):
       mlflow.set_experiment("my_experiment")
       mlflow.log_param("task_status", status)
       mlflow.log_text(message, "status.log")

   def on_failure(task, state, exception):
       logger = get_run_logger()
       current_retry_count = state.context.get("task_run_count", 0)
       retry_limit = task.retries
       
       if current_retry_count >= retry_limit:
           error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
           logger.error(error_message)
           log_to_mlflow("final_failure", error_message)
           # Save error to database or take other actions
           save_error_to_database(task.name, exception)
       else:
           <http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")

   def save_error_to_database(task_name, exception):
       # Placeholder for your database logic
       # Replace with actual database interaction code
       print(f"Saving error to database: {task_name} - {exception}")

   @task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5), on_failure=on_failure)
   def ecs_task():
       logger = get_run_logger()
       ecs_client = boto3.client('ecs')
       <http://logger.info|logger.info>("Starting ECS task...")
       
       try:
           response = ecs_client.run_task(
               cluster='your-cluster',
               taskDefinition='your-task-def',
               count=1,
               launchType='FARGATE'
           )
           
           failures = response.get('failures', [])
           if failures:
               raise RuntimeError(f"Task failed: {failures}")
           
           <http://logger.info|logger.info>("ECS task started successfully.")
           
       except Exception as e:
           error_message = f"Task execution failed: {e}"
           logger.error(error_message)
           log_to_mlflow("error", error_message)
           raise

   @flow(name="ecs-task-flow-with-retries", task_runner=DaskTaskRunner())
   def ecs_task_flow():
       ecs_task()

   if __name__ == "__main__":
       ecs_task_flow()
### Explanation: 1. Dask Task Runner: - The 
DaskTaskRunner
is used to configure the flow to run tasks on a Dask cluster. You can specify Dask cluster settings if you run it on a distributed setup. 2. Task Configuration: - The task 
ecs_task
is configured with retries and the 
on_failure
hook, just like before. 3. Flow Configuration: - The flow is modified to use 
task_runner=DaskTaskRunner()
, making it compatible with Dask. ### Running with Dask: - When you execute this flow, Prefect will utilize the Dask Executor to manage task execution. - The retry logic and the 
on_failure
hook will function as designed, ensuring that your failure handling logic works correctly even when using Dask. ### Summary: - Compatibility with Dask: The setup is compatible with Dask and other execution backends supported by Prefect. - Consistent Error Handling: Ensures that the error handling and retry logic work uniformly across different execution setups.
- Scalable Execution: Using Dask allows scaling of task execution, leveraging distributed computing power. This approach ensures your Prefect tasks and flows maintain resilience and proper error handling across different execution environments, including Dask.
k
@Marvin i've done what you said about retry at task. my task failed and it is trying to restart, but my Dask logs are: distributed.utils_comm - ERROR - Unexpected error while collecting tasks ['DataFrame-6de3d7606fe7bcd9df670a822af62ca2'] from tcp ERROR | distributed.client - Failed to reconnect to scheduler after 30.00 seconds, closing client distributed.scheduler - ERROR - Couldn't gather keys: {'DataFrame-6de3d7606fe7bcd9df670a822af62ca2': 'released'} distributed.deploy.cluster - Failed to sync cluster info multiple times - perhaps there's a connection issue? Error: and all my workers are being killed. my computation didn't return
m
It seems you're facing connection issues with the Dask scheduler which is causing the workers to be killed and the computation to fail. Here are some actions you can take to troubleshoot and resolve these issues: ### Strategies to Resolve the Issue: 1. Check Dask Scheduler and Worker Logs: Ensure that there are no underlying issues with the Dask scheduler and worker nodes that could be causing the connection issues. 2. Increase Timeout and Retries: Sometimes the default timeout settings are not sufficient. Increase the retry count and timeout settings in the Dask client configuration. 3. Network Configuration: Ensure there are no network issues between the Dask scheduler and workers. Check for firewalls and network latency. 4. Resource Management: Ensure the appropriate allocation of resources for both the scheduler and workers. If they are running out of memory or CPU, this could lead to failures. 5. Use Custom Retry Logic with a Condition: You can define a custom retry logic that only retries on specific errors like intermittent network issues. ### Example with Custom Retry Logic: Here’s how you can modify the 
ecs_task
to include custom retry logic to handle specific errors:
Copy code
python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
from prefect.engine.signals import FAIL
import mlflow
from prefect.task_runners import DaskTaskRunner

def log_to_mlflow(status, message):
    mlflow.set_experiment("my_experiment")
    mlflow.log_param("task_status", status)
    mlflow.log_text(message, "status.log")

def save_error_to_database(task_name, exception):
    # Placeholder for your database logic
    # Replace with actual database interaction code
    print(f"Saving error to database: {task_name} - {exception}")

def on_failure(task, state, exception):
    logger = get_run_logger()
    current_retry_count = state.context.get("task_run_count", 0)
    retry_limit = task.retries
    
    if current_retry_count >= retry_limit:
        error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
        logger.error(error_message)
        log_to_mlflow("final_failure", error_message)
        save_error_to_database(task.name, exception)
    else:
        <http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")

def retry_on_specific_errors(task, task_run, state) -> bool:
    error_message = str(state.result)
    retry_conditions = [
        "Capacity is unavailable",
        # Add other specific error messages or conditions here
    ]
    return any(cond in error_message for cond in retry_conditions)

@task(
    retries=5,
    retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5),
    on_failure=on_failure,
    retry_condition_fn=retry_on_specific_errors
)
def ecs_task():
    logger = get_run_logger()
    ecs_client = boto3.client('ecs')
    <http://logger.info|logger.info>("Starting ECS task...")
    
    try:
        response = ecs_client.run_task(
            cluster='your-cluster',
            taskDefinition='your-task-def',
            count=1,
            launchType='FARGATE'
        )
        
        failures = response.get('failures', [])
        if failures:
            raise RuntimeError(f"Task failed: {failures}")
        
        <http://logger.info|logger.info>("ECS task started successfully.")
        
    except Exception as e:
        error_message = f"Task execution failed: {e}"
        logger.error(error_message)
        log_to_mlflow("error", error_message)
        raise

@flow(name="ecs-task-flow-with-retries", task_runner=DaskTaskRunner())
def ecs_task_flow():
    ecs_task()

if __name__ == "__main__":
    ecs_task_flow()
### Explanation: 1. Custom Retry Condition (
retry_on_specific_errors
): - This function checks if the error messages contain specific conditions that should trigger a retry. 2. Task Configuration:
- The task is configured with the custom retry condition via the 
retry_condition_fn
parameter. - The rest of the task configuration remains the same as earlier. 3. Flow Execution: - The flow uses 
DaskTaskRunner()
. ### Check Dask Configuration: Ensure your Dask deployment is properly configured: - Scheduler and Worker Resource Allocation: - Verify that your Dask scheduler and workers have sufficient resources (CPU, memory). - Timeout Settings: - Adjust the timeout settings for the Dask client to ensure it waits longer before failing connections. ### Sample Dask Client Configuration:
Copy code
python
from dask.distributed import Client

client = Client(
    n_workers=4,
    threads_per_worker=2,
    memory_limit='2GB',
    timeout='60s',  # Increase timeout as needed
    retries=3  # Increase retries as needed
)
### Summary: - Custom Retry Logic: Implementing a custom retry logic to handle specific transient errors. - Error Logging: Ensuring errors are logged to MLflow and captured only after all retries. - Dask Configuration: Verifying and tuning Dask scheduler, worker resources, and network configuration. With these changes, you should be able to handle intermittent ECS capacity issues better while running your Prefect tasks with Dask.
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