Let me break down what's likely happening in your case and provide some solutions: 1. Root Cause Analysis: - You're running 200 tasks in parallel, each using ~300MB - Total theoretical memory usage: 200 * 300MB = ~60GB - Even if not all tasks run exactly simultaneously, the memory footprint is substantial - SIGKILL typically occurs when the system's OOM (Out Of Memory) killer activates 2. Solutions: a) Control Concurrency:

python
   from prefect import flow
   
   @flow(task_runner=ConcurrentTaskRunner(max_concurrent_tasks=20))
   def your_flow():
       # Your tasks here

Start with a lower number of concurrent tasks (like 20) and adjust based on your system's memory. b) Use Dask for Better Memory Management:

python
   from prefect_dask import DaskTaskRunner
   
   @flow(task_runner=DaskTaskRunner(cluster_kwargs={"n_workers": 4}))
   def your_flow():
       # Your tasks here

Dask provides better memory management and true parallelism across multiple processes. c) Batch Processing:

python
   from prefect import flow, task
   
   @task
   def process_batch(items):
       # Process a subset of items
       pass
   
   @flow
   def main_flow():
       all_items = # your 200 items
       batch_size = 20
       batches = [all_items[i:i + batch_size] for i in range(0, len(all_items), batch_size)]
       for batch in batches:
           process_batch.submit(batch)

3. Additional Recommendations: - Monitor memory usage using tools like

memory_profiler

to identify potential leaks - Ensure proper cleanup of resources in your tasks - Consider using a worker with more memory if running in production - If possible, optimize individual task memory usage 4. System-level Solutions: - Increase swap space if running on Linux - Set memory limits for your Prefect worker/deployment if running in a container - Consider using container orchestration (like Kubernetes) with proper resource limits Would you like me to: 1. Research more specific details about your particular use case? 2. Provide more detailed examples of any of these solutions? 3. Look into specific memory optimization techniques for your tasks? Also, it would be helpful to know: - What type of operations are these tasks performing? - Are you running this locally or in a production environment? - What task runner are you currently using?