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# ask-marvin
a
@Marvin Ready to scale? Runs 1 Minutes Bronze - Orderbook, Bid Ask, Trades judicious-cuscus Logs Completed
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Beginning subflow run 'judicious-cuscus' for flow '1 Minutes Bronze - Orderbook, Bid Ask, Trades'
09:13:26 AM
Info
prefect.flow_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Crash detected! Execution was cancelled by the runtime environment.
09:13:35 AM
Error
prefect.task_runs
Finished in state Crashed('Execution was cancelled by the runtime environment.')
09:13:35 AM
Error
prefect.task_runs
Finished in state Completed()

why this happened?
m
✅ thought for 44.7 seconds
Based on the error logs and research, I can explain what's happening and suggest some solutions: 1. What's Happening: Your flow appears to be experiencing a runtime cancellation, which typically occurs when there's an issue with task coordination or resource management. The pattern of multiple tasks showing the same cancellation message suggests this might be related to concurrent task execution. 2. Likely Causes: - Resource constraints or timeouts - Concurrent task execution issues - Possible connection issues with external services - Tasks might not be properly waiting for their dependencies 3. Recommendations: a. Add Task Dependencies: Make sure your tasks properly wait for their dependencies. Use 
.wait()
or ensure proper task dependencies are set:
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python
   @flow
   def your_flow():
       # Make tasks explicitly wait for their dependencies
       task1_result = task1.submit()
       task2_result = task2.submit(wait_for=[task1_result])
       # Or use .wait() when needed
       task1_result.wait()
b. Add Error Handling: Implement proper error handling and retries for your tasks:
Copy code
python
   @task(retries=3, retry_delay_seconds=30)
   def your_task():
       try:
           # Your task logic here
           pass
       except Exception as e:
           logger.error(f"Task failed: {e}")
           raise
c. Resource Management: - Check if you're running into memory or CPU constraints - Consider adjusting any timeouts in your configuration - Monitor resource usage during execution 4. Additional Debugging Steps: - Add more detailed logging to your tasks to track their execution - Monitor the resource usage of your worker - Check for any external service timeouts or connection issues Would you like me to help you implement any of these solutions or would you like more details about a specific aspect? Also, it would be helpful if you could share more details about: - The nature of your tasks (CPU-intensive, I/O-bound, etc.) - The typical runtime of your tasks - Any external services or resources your tasks depend on
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