[x] - I have read the CONTRIBUTING.md document and this tool is appropriate for the tools-iuc repo.
[x] - License permits unrestricted use (educational + commercial)
[x] - This PR adds a new tool or tool collection
[ ] - This PR updates an existing tool or tool collection
[ ] - This PR does something else (explain below)
The error I get when running planemo test is the following:
Job in error state.. tool_id: ip_superdsm, exit_code: 1, stderr: 2023-06-21 00:18:53,303 INFO worker.py:1544 -- Started a local Ray instance. View the dashboard at [1m[32m127.0.0.1:8265 [39m[22m
[2m[33m(raylet)[0m [2023-06-21 00:19:01,791 E 1327542 1327573] (raylet) agent_manager.cc:135: The raylet exited immediately because the Ray agent failed. The raylet fate shares with the agent. This can happen because the Ray agent was unexpectedly killed or failed. Agent can fail when
[2m[33m(raylet)[0m - The version of `grpcio` doesn't follow Ray's requirement. Agent can segfault with the incorrect `grpcio` version. Check the grpcio version `pip freeze | grep grpcio`.
[2m[33m(raylet)[0m - The agent failed to start because of unexpected error or port conflict. Read the log `cat /tmp/ray/session_latest/dashboard_agent.log`. You can find the log file structure here https://docs.ray.io/en/master/ray-observability/ray-logging.html#logging-directory-structure.
[2m[33m(raylet)[0m - The agent is killed by the OS (e.g., out of memory).
Traceback (most recent call last):
File "/home/void/Documents/galaxy-image-analysis/tools/superdsm/run-superdsm.py", line 41, in <module>
data, cfg, _ = superdsm.automation.process_image(pipeline, cfg, img)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/superdsm/automation.py", line 117, in process_image
return pipeline.process_image(g_raw, cfg=cfg, **kwargs)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/superdsm/pipeline.py", line 172, in process_image
dt = stage(data, cfg, out=out, log_root_dir=log_root_dir)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/superdsm/pipeline.py", line 61, in __call__
output_data = self.process(input_data, cfg=cfg, out=out, log_root_dir=log_root_dir)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/superdsm/c2freganal.py", line 148, in process
y_id = ray.put(y)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/ray/_private/worker.py", line 2452, in put
object_ref = worker.put_object(value, owner_address=serialize_owner_address)
File "/home/void/miniconda3/envs/mulled-v1-f473b96c9754fd351c3d4e956e80fa4d198ea0b04c54321dd7854a4fc50429b5/lib/python3.9/site-packages/ray/_private/worker.py", line 621, in put_object
self.core_worker.put_serialized_object_and_increment_local_ref(
File "python/ray/_raylet.pyx", line 1780, in ray._raylet.CoreWorker.put_serialized_object_and_increment_local_ref
File "python/ray/_raylet.pyx", line 1669, in ray._raylet.CoreWorker._create_put_buffer
File "python/ray/_raylet.pyx", line 209, in ray._raylet.check_status
ray.exceptions.RaySystemError: System error: Broken pipe
I suspect memory issues to be the cause due to the "The agent is killed by the OS (e.g., out of memory)" hint and since the error occurs when calling ray.put, which is supposed to allocate inter-process shared memory.
FOR CONTRIBUTOR:
The error I get when running
planemo testis the following:I suspect memory issues to be the cause due to the "The agent is killed by the OS (e.g., out of memory)" hint and since the error occurs when calling
ray.put, which is supposed to allocate inter-process shared memory.