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A task has failed to un-serialize. #38

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leummas opened this issue Mar 1, 2024 · 1 comment
Open

A task has failed to un-serialize. #38

leummas opened this issue Mar 1, 2024 · 1 comment

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@leummas
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leummas commented Mar 1, 2024

Running the example code Im receiving the following error:


_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py", line 426, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/feature_creation_env/lib/python3.12/multiprocessing/queues.py", line 122, in get
return _ForkingPickler.loads(res)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/mrmr/init.py", line 1, in
from . import bigquery
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/mrmr/bigquery.py", line 3, in
from .main import mrmr_base, groupstats2fstat
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/mrmr/main.py", line 1, in
import pandas as pd
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/pandas/init.py", line 46, in
from pandas.core.api import (
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/pandas/core/api.py", line 47, in
from pandas.core.groupby import (
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/pandas/core/groupby/init.py", line 1, in
from pandas.core.groupby.generic import (
File "/opt/conda/envs/feature_creation_env/lib/python3.12/site-packages/pandas/core/groupby/generic.py", line 67, in
from pandas.core.frame import DataFrame
...
--> 754 raise self._result
755 return self._result
756 finally:

BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.

@benhorvath
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benhorvath commented May 9, 2024

I am also getting a similar error, with both classification and regression functions. Looking at my activity monitor, I notice the user CPU shoots up to close to 100% right before the error triggers. Something isn't quite right with the parallel jobs code.

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